Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab

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1. 1

   Roch, L. M.; Häse, F.; Kreisbeck, C.; Tamayo-Mendoza, T.; Yunker, L. P. E.; Hein, J. E.; Aspuru-Guzik, A. ChemOS: An Orchestration Software to Democratize Autonomous Discovery. PLoS One 2020, 15, e0229862,  DOI: 10.1371/journal.pone.0229862

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   ChemOS: An orchestration software to democratize autonomous discovery

   Roch, Loic M.; Hase, Florian; Kreisbeck, Christoph; Tamayo-Mendoza, Teresa; Yunker, Lars P. E.; Hein, Jason E.; Aspuru-Guzik, Alan

   PLoS One (2020), 15 (4), e0229862CODEN: POLNCL; ISSN:1932-6203. (Public Library of Science)

   The current Edisonian approach to discovery requires up to two decades of fundamental and applied research for materials technologies to reach the market. Such a slow and capital-intensive turnaround calls for disruptive strategies to expedite innovation. Self-driving labs. have the potential to provide the means to revolutionize experimentation by empowering automation with artificial intelligence to enable autonomous discovery. However, the lack of adequate software solns. significantly impedes the development of self-driving labs. In this paper, we make progress towards addressing this challenge, and we propose and develop an implementation of ChemOS; a portable, modular and versatile software package which supplies the structured layers necessary for the deployment and operation of self-driving labs. ChemOS facilitates the integration of automated equipment, and it enables remote control of automated labs. ChemOS can operate at various degrees of autonomy; from fully unsupervised experimentation to actively including inputs and feedbacks from researchers into the experimentation loop. The flexibility of ChemOS provides a broad range of functionality as demonstrated on five applications, which were executed on different automated equipment, highlighting various aspects of the software package.
2. 2

   Langner, S.; Häse, F.; Perea, J. D.; Stubhan, T.; Hauch, J.; Roch, L. M.; Heumueller, T.; Aspuru-Guzik, A.; Brabec, C. J. Beyond Ternary OPV: High-Throughput Experimentation and Self-Driving Laboratories Optimize Multicomponent Systems. Adv. Mater. 2020, 32, 1907801,  DOI: 10.1002/adma.201907801

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   Beyond Ternary OPV: High-Throughput Experimentation and Self-Driving Laboratories Optimize Multicomponent Systems

   Langner, Stefan; Haese, Florian; Perea, Jose Dario; Stubhan, Tobias; Hauch, Jens; Roch, Loic M.; Heumueller, Thomas; Aspuru-Guzik, Alan; Brabec, Christoph J.

   Advanced Materials (Weinheim, Germany) (2020), 32 (14), 1907801CODEN: ADVMEW; ISSN:0935-9648. (Wiley-VCH Verlag GmbH & Co. KGaA)

   Fundamental advances to increase the efficiency as well as stability of org. photovoltaics (OPVs) are achieved by designing ternary blends, which represents a clear trend toward multicomponent active layer blends. The development of high-throughput and autonomous experimentation methods is reported for the effective optimization of multicomponent polymer blends for OPVs. A method for automated film formation enabling the fabrication of ≤ 6048 films per day is introduced. Equipping this automated experimentation platform with a Bayesian optimization, a self-driving lab. is constructed that autonomously evaluates measurements to design and execute the next expts. To demonstrate the potential of these methods, a 4D parameter space of quaternary OPV blends is mapped and optimized for photostability. While with conventional approaches, roughly 100 mg of material would be necessary, the robot-based platform can screen 2000 combinations with < 10 mg, and machine-learning-enabled autonomous experimentation identifies stable compns. with < 1 mg.
3. 3

   Christensen, M.; Yunker, L. P. E.; Adedeji, F.; Häse, F.; Roch, L. M.; Gensch, T.; dos Passos Gomes, G.; Zepel, T.; Sigman, M. S.; Aspuru-Guzik, A.; Hein, J. E. Data-Science Driven Autonomous Process Optimization. Commun. Chem. 2021, 4, 112,  DOI: 10.1038/s42004-021-00550-x

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   Data-science driven autonomous process optimization

   Christensen Melodie; Yunker Lars P E; Zepel Tara; Hein Jason E; Christensen Melodie; Adedeji Folarin; Hase Florian; Roch Loic M; Aspuru-Guzik Alan; Hase Florian; Roch Loic M; Dos Passos Gomes Gabriel; Aspuru-Guzik Alan; Hase Florian; Roch Loic M; Dos Passos Gomes Gabriel; Aspuru-Guzik Alan; Hase Florian; Dos Passos Gomes Gabriel; Aspuru-Guzik Alan; Hase Florian; Roch Loic M; Gensch Tobias; Sigman Matthew S; Aspuru-Guzik Alan

   Communications chemistry (2021), 4 (1), 112 ISSN:.

   Autonomous process optimization involves the human intervention-free exploration of a range process parameters to improve responses such as product yield and selectivity. Utilizing off-the-shelf components, we develop a closed-loop system for carrying out parallel autonomous process optimization experiments in batch. Upon implementation of our system in the optimization of a stereoselective Suzuki-Miyaura coupling, we find that the definition of a set of meaningful, broad, and unbiased process parameters is the most critical aspect of successful optimization. Importantly, we discern that phosphine ligand, a categorical parameter, is vital to determination of the reaction outcome. To date, categorical parameter selection has relied on chemical intuition, potentially introducing bias into the experimental design. In seeking a systematic method for selecting a diverse set of phosphine ligands, we develop a strategy that leverages computed molecular feature clustering. The resulting optimization uncovers conditions to selectively access the desired product isomer in high yield.
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   Seifrid, M.; Hickman, R. J.; Aguilar-Granda, A.; Lavigne, C.; Vestfrid, J.; Wu, T. C.; Gaudin, T.; Hopkins, E. J.; Aspuru-Guzik, A. Routescore: Punching the Ticket to More Efficient Materials Development. ACS Cent. Sci. 2022, 8, 122– 131,  DOI: 10.1021/acscentsci.1c01002

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   Routescore: Punching the Ticket to More Efficient Materials Development

   Seifrid, Martin; Hickman, Riley J.; Aguilar-Granda, Andres; Lavigne, Cyrille; Vestfrid, Jenya; Wu, Tony C.; Gaudin, Theophile; Hopkins, Emily J.; Aspuru-Guzik, Alan

   ACS Central Science (2022), 8 (1), 122-131CODEN: ACSCII; ISSN:2374-7951. (American Chemical Society)

   Self-driving labs., in the form of automated experimentation platforms guided by machine learning algorithms have emerged as a potential soln. to the need for accelerated science. While new tools for automated anal. and characterization are being developed at a steady rate, automated synthesis remains the bottleneck in the chem. space accessible to self-driving labs. Combining automated and manual synthesis efforts immediately significantly expands the explorable chem. space. To effectively direct the different capabilities of automated (higher throughput and less labor) and manual synthesis (greater chem. versatility), we describe a protocol, the RouteScore, that quantifies the cost of combined synthetic routes. In this work, the RouteScore is used to det. the most efficient synthetic route to a well-known pharmaceutical (structure-oriented optimization), and to simulate a self-driving lab that finds the most easily synthesizable org. laser mol. with specific photophys. properties from a space of ∼3500 possible mols. (property-oriented optimization). These two examples demonstrate the power and flexibility of our approach in mixed synthetic planning and optimization, and esp. in down-selecting promising candidates from a large chem. space via a priori estn. of the synthetic costs.
5. 5

   Häse, F.; Roch, L. M.; Aspuru-Guzik, A. Next-Generation Experimentation with Self-Driving Laboratories. Trends Chem. 2019, 1, 282– 291,  DOI: 10.1016/j.trechm.2019.02.007

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   Next-Generation Experimentation with Self-Driving Laboratories

   Hase, Florian; Roch, Loic M.; Aspuru-Guzik, Alan

   Trends in Chemistry (2019), 1 (3), 282-291CODEN: TCRHBQ; ISSN:2589-5974. (Cell Press)

   A review. The ever-growing demand for advanced functional materials requires disruption of conventional approaches to experimentation and acceleration of the discovery process. State-of-the-art approaches to scientific discovery are inherently slow, capital intensive, and have arguably reached a plateau. Significant advances are possible when rethinking and redesigning the traditional experimentation process. Self-driving labs. promise to substantially accelerate the discovery process by augmenting automated experimentation platforms with artificial intelligence (AI). AI methods actively search for promising exptl. procedures by hypothesizing about their outcomes based on previous expts. This feedback loop is crucial to reduce the no. of expts. needed for discovery. Supplying automated platforms with AI enables self-driving labs. to fully embrace the vision of autonomous experimentation.
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   MacLeod, B. P.; Parlane, F. G. L.; Rupnow, C. C.; Dettelbach, K. E.; Elliott, M. S.; Morrissey, T. D.; Haley, T. H.; Proskurin, O.; Rooney, M. B.; Taherimakhsousi, N.; Dvorak, D. J.; Chiu, H. N.; Waizenegger, C. E. B.; Ocean, K.; Mokhtari, M.; Berlinguette, C. P. A Self-Driving Laboratory Advances the Pareto Front for Material Properties. Nat. Commun. 2022, 13, 995,  DOI: 10.1038/s41467-022-28580-6

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   A self-driving laboratory advances the Pareto front for material properties

   MacLeod, Benjamin P.; Parlane, Fraser G. L.; Rupnow, Connor C.; Dettelbach, Kevan E.; Elliott, Michael S.; Morrissey, Thomas D.; Haley, Ted H.; Proskurin, Oleksii; Rooney, Michael B.; Taherimakhsousi, Nina; Dvorak, David J.; Chiu, Hsi N.; Waizenegger, Christopher E. B.; Ocean, Karry; Mokhtari, Mehrdad; Berlinguette, Curtis P.

   Nature Communications (2022), 13 (1), 995CODEN: NCAOBW; ISSN:2041-1723. (Nature Portfolio)

   Useful materials must satisfy multiple objectives, where the optimization of one objective is often at the expense of another. The Pareto front reports the optimal trade-offs between these conflicting objectives. Here we use a self-driving lab., Ada, to define the Pareto front of conductivities and processing temps. for palladium films formed by combustion synthesis. Ada discovers new synthesis conditions that yield metallic films at lower processing temps. (below 200°C) relative to the prior art for this technique (250°C). This temp. difference makes possible the coating of different commodity plastic materials (e.g., Nafion, polyethersulfone). These combustion synthesis conditions enable us to to spray coat uniform palladium films with moderate cond. (1.1 x 105 S m-1) at 191°C. Spray coating at 226°C yields films with conductivities (2.0 x 106 S m-1) comparable to those of sputtered films (2.0 to 5.8 x 106 S m-1). This work shows how a self-driving laboratoy can discover materials that provide optimal trade-offs between conflicting objectives.
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   Rooney, M. B.; MacLeod, B. P.; Oldford, R.; Thompson, Z. J.; White, K. L.; Tungjunyatham, J.; Stankiewicz, B. J.; Berlinguette, C. P. A Self-Driving Laboratory Designed to Accelerate the Discovery of Adhesive Materials. Digit. Discovery 2022,  DOI: 10.1039/D2DD00029F

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   Tao, H.; Wu, T.; Kheiri, S.; Aldeghi, M.; Aspuru-Guzik, A.; Kumacheva, E. Self-Driving Platform for Metal Nanoparticle Synthesis: Combining Microfluidics and Machine Learning. Adv. Funct. Mater. 2021, 31, 2106725,  DOI: 10.1002/adfm.202106725

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   Self-Driving Platform for Metal Nanoparticle Synthesis: Combining Microfluidics and Machine Learning

   Tao, Huachen; Wu, Tianyi; Kheiri, Sina; Aldeghi, Matteo; Aspuru-Guzik, Alan; Kumacheva, Eugenia

   Advanced Functional Materials (2021), 31 (51), 2106725CODEN: AFMDC6; ISSN:1616-301X. (Wiley-VCH Verlag GmbH & Co. KGaA)

   Many applications of inorg. nanoparticles (NPs), including photocatalysis, photovoltaics, chem. and biochem. sensing, and theranostics, are governed by NP optical properties. Exploration and identification of reaction conditions for the synthesis of NPs with targeted spectroscopic characteristics is a time-, labor-, and resource-intensive task, as it involves the optimization of multiple interdependent reaction conditions. Integration of machine learning (ML) and microfluidics (MF) offers accelerated identification and optimization of reaction conditions for NP synthesis. Here, an autonomous ML-driven, oscillatory MF platform for the synthesis of NPs is reported. The platform utilized multiple recipes and reaction times for the synthesis of NPs with different dimensions, conducted spectroscopic NP characterization, and employed ML approaches to analyze multiple yet prioritized spectroscopic NP characteristics, and identified reaction conditions for the synthesis of NPs with targeted optical properties. The platform is also used to develop an understanding of the relationship between reaction conditions and NP properties. This study shows the strong potential of ML-driven oscillatory MF platforms in materials science and paves the way for automated NP development.
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   Gao, W.; Raghavan, P.; Coley, C. W. Autonomous Platforms for Data-Driven Organic Synthesis. Nat. Commun. 2022, 13, 1075,  DOI: 10.1038/s41467-022-28736-4

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   Autonomous platforms for data-driven organic synthesis

   Gao, Wenhao; Raghavan, Priyanka; Coley, Connor W.

   Nature Communications (2022), 13 (1), 1075CODEN: NCAOBW; ISSN:2041-1723. (Nature Portfolio)

   Achieving autonomous multi-step synthesis of novel mol. structures in chem. discovery processes is a goal shared by many researchers. In this Comment, we discuss key considerations of what an ideal platform may look like and the apparent state of the art. While most hardware challenges can be overcome with clever engineering, other challenges will require advances in both algorithms and data curation.
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    Gupta, A.; Ong, Y.; Feng, L. Insights on Transfer Optimization: Because Experience Is the Best Teacher. IEEE Trans. Emerg. Top. Comput. Intell. 2018, 2, 51– 64,  DOI: 10.1109/TETCI.2017.2769104

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    Shi, Y.; Prieto, P. L.; Zepel, T.; Grunert, S.; Hein, J. E. Automated Experimentation Powers Data Science in Chemistry. Acc. Chem. Res. 2021, 54, 546– 555,  DOI: 10.1021/acs.accounts.0c00736

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    Automated Experimentation Powers Data Science in Chemistry

    Shi, Yao; Prieto, Paloma L.; Zepel, Tara; Grunert, Shad; Hein, Jason E.

    Accounts of Chemical Research (2021), 54 (3), 546-555CODEN: ACHRE4; ISSN:0001-4842. (American Chemical Society)

    A review. Data science has revolutionized chem. research and continues to break down barriers with new interdisciplinary studies. The introduction of computational models and machine learning (ML) algorithms in combination with automation and traditional exptl. techniques has enabled scientific advancement across nearly every discipline of chem., from materials discovery, to process optimization, to synthesis planning. However, predictive tools powered by data science are only as good as their data sets and, currently, many of the data sets used to train models suffer from several limitations, including being sparse, limited in scope and requiring human curation. Likewise, computational data faces limitations in terms of accurate modeling of nonideal systems and can suffer from low translation fidelity from simulation to real conditions. The lack of diverse data and the need to be able to test it exptl. reduces both the accuracy and scope of the predictive models derived from data science. This Account contextualizes the need for more complex and diverse exptl. data and highlights how the seamless integration of robotics, machine learning, and data-rich monitoring techniques can be used to access it with minimal human labor. We propose three broad categories of data in chem.: data on fundamental properties, data on reaction outcomes, and data on reaction mechanics. We highlight flexible, automated platforms that can be deployed to acquire and leverage these data. The first platform combines solid- and liq.-dosing modules with computer vision to automate soly. screening, thereby gathering fundamental data that are necessary for almost every exptl. design. Using computer vision offers the addnl. benefit of creating a visual record, which can be referenced and used to further interrogate and gain insight on the data collected. The second platform iteratively tests reaction variables proposed by a ML algorithm in a closed-loop fashion. Exptl. data related to reaction outcomes are fed back into the algorithm to drive the discovery and optimization of new materials and chem. processes. The third platform uses automated process anal. technol. to gather real-time data related to reaction kinetics. This system allows the researcher to directly interrogate the reaction mechanisms in granular detail to det. exactly how and why a reaction proceeds, thereby enabling reaction optimization and deployment.
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    Strieth-Kalthoff, F.; Sandfort, F.; Kühnemund, M.; Schäfer, F. R.; Kuchen, H.; Glorius, F. Machine Learning for Chemical Reactivity: The Importance of Failed Experiments. Angew. Chem., Int. Ed. 2022, 61, e202204647,  DOI: 10.1002/anie.202204647

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    Machine Learning for Chemical Reactivity: The Importance of Failed Experiments

    Strieth-Kalthoff, Felix; Sandfort, Frederik; Kuehnemund, Marius; Schafer, Felix R.; Kuchen, Herbert; Glorius, Frank

    Angewandte Chemie, International Edition (2022), 61 (29), e202204647CODEN: ACIEF5; ISSN:1433-7851. (Wiley-VCH Verlag GmbH & Co. KGaA)

    A review. Assessing the outcomes of chem. reactions in a quant. fashion has been a cornerstone across all synthetic disciplines. Classically approached through empirical optimization, data-driven modeling bears an enormous potential to streamline this process. However, such predictive models require significant quantities of high-quality data, the availability of which is limited: Main reasons for this include exptl. errors and, importantly, human biases regarding expt. selection and result reporting. In a series of case studies, we investigate the impact of these biases for drawing general conclusions from chem. reaction data, revealing the utmost importance of "neg." examples. Eventually, case studies into data expansion approaches showcase directions to circumvent these limitations-and demonstrate perspectives towards a long-term data quality enhancement in chem.
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    Beker, W.; Roszak, R.; Wołos, A.; Angello, N. H.; Rathore, V.; Burke, M. D.; Grzybowski, B. A. Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki–Miyaura Coupling. J. Am. Chem. Soc. 2022, 144, 4819– 4827,  DOI: 10.1021/jacs.1c12005

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    Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki-Miyaura Coupling

    Beker, Wiktor; Roszak, Rafal; Wolos, Agnieszka; Angello, Nicholas H.; Rathore, Vandana; Burke, Martin D.; Grzybowski, Bartosz A.

    Journal of the American Chemical Society (2022), 144 (11), 4819-4827CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)

    A review. Applications of machine learning (ML) to synthetic chem. rely on the assumption that large nos. of literature-reported examples should enable construction of accurate and predictive models of chem. reactivity. This paper demonstrates that abundance of carefully curated literature data may be insufficient for this purpose. Using an example of Suzuki-Miyaura coupling with heterocyclic building blocks-and a carefully selected database of >10,000 literature examples-this article shows that ML models cannot offer any meaningful predictions of optimum reaction conditions, even if the search space is restricted to only solvents and bases. This result holds irresp. of the ML model applied (from simple feed-forward to state-of-the-art graph convolution neural networks) or the representation to describe the reaction partners (various fingerprints, chem. descriptors, latent representations, etc.). In all cases, the ML methods fail to perform significantly better than naive assignments based on the sheer frequency of certain reaction conditions reported in the literature. These unsatisfactory results likely reflect subjective preferences of various chemists to use certain protocols and, other biasing factors as mundane as availability of certain solvents/reagents, and/or a lack of neg. data. These findings highlight the likely importance of systematically generating reliable and standardized data sets for algorithm training.
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    Wilbraham, L.; Mehr, S. H. M.; Cronin, L. Digitizing Chemistry Using the Chemical Processing Unit: From Synthesis to Discovery. Acc. Chem. Res. 2021, 54, 253– 262,  DOI: 10.1021/acs.accounts.0c00674

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    Digitizing Chemistry Using the Chemical Processing Unit: From Synthesis to Discovery

    Wilbraham, Liam; Mehr, S. Hessam M.; Cronin, Leroy

    Accounts of Chemical Research (2021), 54 (2), 253-262CODEN: ACHRE4; ISSN:0001-4842. (American Chemical Society)

    The digitization of chem. is not simply about using machine learning or artificial intelligence systems to process chem. data, or about the development of ever more capable automation hardware; instead, it is the creation of a hard link between an abstracted process ontol. of chem. and bespoke hardware for performing reactions or exploring reactivity. Chem. digitization is therefore about the unambiguous development of an architecture, a chem. state machine, that uses this ontol. to connect precise instruction sets to hardware that performs chem. transformations. This approach enables a universal std. for describing chem. procedures via a chem. programming language and facilitates unambiguous dissemination of these procedures. We predict that this std. will revolutionize the ability of chemists to collaborate, increase reproducibility and safety, as we all as optimize for cost and efficiency. Most importantly, the digitization of chem. will dramatically reduce the labor needed to make new compds. and broaden accessible chem. space. In recent years, the developments of automation in chem. have gone beyond flow chem. alone, with many bespoke workflows being developed not only for automating chem. synthesis but also for materials, nanomaterials, and formulation prodn. Indeed, the leap from fixed-configuration synthesis machines like peptide, nucleic acid, or dedicated cross-coupling engines is important for developing a truly universal approach to "dial-a-mol.". In this case, a key conceptual leap is the use of a batch system that can encode the chem. reagents, solvent, and products as packets which can be moved around the system, and a graph-based approach for the description of hardware modules that allows the compilation of chem. code that runs on, in principle, any hardware. Further, the integration of sensor systems for monitoring and controlling the state of the chem. synthesis machine, as well as high resoln. spectroscopic tools, is vital if these systems are to facilitate closed-loop autonomous expts. Systems that not only make mols. and materials, but also optimize their function, and use algorithms to assist with the development of new synthetic pathways and process optimization are also possible. Here, we discuss how the digitization of chem. is happening, building on the plethora of technol. developments in hardware and software. Importantly, digital-chem. robot systems need to integrate feedback from simple sensors, e.g., cond. or temp., as well as online analytics in order to navigate process space autonomously. This will open the door to accessing known mols. (synthesis), exploring whether known compds./reactions are possible under new conditions (optimization), and searching chem. space for unknown and unexpected new mols., reactions, and modes of reactivity (discovery). We will also discuss the role of chem. knowledge and how this can be used to challenge bias, as well as define and expand synthetically accessible chem. space using programmable robotic chem. state machines.
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    Rohrbach, S.; Šiaučiulis, M.; Chisholm, G.; Pirvan, P.-A.; Saleeb, M.; Mehr, S. H. M.; Trushina, E.; Leonov, A. I.; Keenan, G.; Khan, A.; Hammer, A.; Cronin, L. Digitization and Validation of a Chemical Synthesis Literature Database in the ChemPU. Science 2022, 377, 172– 180,  DOI: 10.1126/science.abo0058

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    Digitization and validation of a chemical synthesis literature database in the ChemPU

    Rohrbach, Simon; Siauciulis, Mindaugas; Chisholm, Greig; Pirvan, Petrisor-Alin; Saleeb, Michael; Mehr, S. Hessam M.; Trushina, Ekaterina; Leonov, Artem I.; Keenan, Graham; Khan, Aamir; Hammer, Alexander; Cronin, Leroy

    Science (Washington, DC, United States) (2022), 377 (6602), 172-180CODEN: SCIEAS; ISSN:1095-9203. (American Association for the Advancement of Science)

    Despite huge potential, automation of synthetic chem. has only made incremental progress over the past few decades. We present an automatically executable chem. reaction database of 100 mols. representative of the range of reactions found in contemporary org. synthesis. These reactions include transition metal-catalyzed coupling reactions, heterocycle formations, functional group interconversions, and multicomponent reactions. The chem. reaction codes or χDLs for the reactions have been stored in a database for version control, validation, collaboration, and data mining. Of these syntheses, more than 50 entries from the database have been downloaded and robotically run in seven modular ChemPU's with yields and purities comparable to those achieved by an expert chemist. We also demonstrate the automatic purifn. of a range of compds. using a chromatog. module seamlessly coupled to the platform and programmed with the same language.
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    Bubliauskas, A.; Blair, D. J.; Powell-Davies, H.; Kitson, P. J.; Burke, M. D.; Cronin, L. Digitizing Chemical Synthesis in 3D Printed Reactionware. Angew. Chem., Int. Ed. 2022, 61, e202116108,  DOI: 10.1002/anie.202116108

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    Digitizing Chemical Synthesis in 3D Printed Reactionware

    Bubliauskas, Andrius; Blair, Daniel J.; Powell-Davies, Henry; Kitson, Philip J.; Burke, Martin D.; Cronin, Leroy; Acknow

    Angewandte Chemie, International Edition (2022), 61 (24), e202116108CODEN: ACIEF5; ISSN:1433-7851. (Wiley-VCH Verlag GmbH & Co. KGaA)

    Chem. digitization requires an unambiguous link between expts. and the code used to generate the exptl. conditions and outcomes, yet this process is not standardized, limiting the portability of any chem. code. What is needed is a universal approach to aid this process using a well-defined std. that is composed of syntheses that are employed in modular hardware. Herein a new approach is presented to the digitization of org. synthesis that combines process chem. principles with 3D printed reactionware. This approach outlines the process for transforming unit operations into digitized hardware and well-defined instructions that ensure effective synthesis. To demonstrate this, the process is outlined for digitizing 3 MIDA boronate building blocks, an ester hydrolysis, a Wittig olefination, a Suzuki-Miyaura coupling reaction, and synthesis of the drug sulfanilamide.
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    Roch, L. M.; Häse, F.; Kreisbeck, C.; Tamayo-Mendoza, T.; Yunker, L. P. E.; Hein, J. E.; Aspuru-Guzik, A. ChemOS: Orchestrating Autonomous Experimentation. Sci. Robot. 2018, 3 (19), 1,  DOI: 10.1126/scirobotics.aat5559

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    Häse, F.; Roch, L. M.; Kreisbeck, C.; Aspuru-Guzik, A. Phoenics: A Bayesian Optimizer for Chemistry. ACS Cent. Sci. 2018, 4, 1134– 1145,  DOI: 10.1021/acscentsci.8b00307

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    Phoenics: A Bayesian Optimizer for Chemistry

    Hase, Florian; Roch, Loic M.; Kreisbeck, Christoph; Aspuru-Guzik, Alan

    ACS Central Science (2018), 4 (9), 1134-1145CODEN: ACSCII; ISSN:2374-7951. (American Chemical Society)

    We report Phoenics, a probabilistic global optimization algorithm identifying the set of conditions of an exptl. or computational procedure which satisfies desired targets. Phoenics combines ideas from Bayesian optimization with concepts from Bayesian kernel d. estn. As such, Phoenics allows to tackle typical optimization problems in chem. for which objective evaluations are limited, due to either budgeted resources or time-consuming evaluations of the conditions, including experimentation or enduring computations. Phoenics proposes new conditions based on all previous observations, avoiding, thus, redundant evaluations to locate the optimal conditions. It enables an efficient parallel search based on intuitive sampling strategies implicitly biasing toward exploration or exploitation of the search space. Our benchmarks indicate that Phoenics is less sensitive to the response surface than already established optimization algorithms. We showcase the applicability of Phoenics on the Oregonator, a complex case-study describing a nonlinear chem. reaction network. Despite the large search space, Phoenics quickly identifies the conditions which yield the desired target dynamic behavior. Overall, we recommend Phoenics for rapid optimization of unknown expensive-to-evaluate objective functions, such as experimentation or long-lasting computations.
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    Häse, F.; Roch, L. M.; Aspuru-Guzik, A. Chimera: Enabling Hierarchy Based Multi-Objective Optimization for Self-Driving Laboratories. Chem. Sci. 2018, 9, 7642– 7655,  DOI: 10.1039/C8SC02239A

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    Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories

    Hase, Florian; Roch, Loic M.; Aspuru-Guzik, Alan

    Chemical Science (2018), 9 (39), 7642-7655CODEN: CSHCCN; ISSN:2041-6520. (Royal Society of Chemistry)

    Finding the ideal conditions satisfying multiple pre-defined targets simultaneously is a challenging decision-making process, which impacts science, engineering, and economics. Addnl. complexity arises for tasks involving experimentation or expensive computations, as the no. of evaluated conditions must be kept low. We propose Chimera as a general purpose achievement scalarizing function for multi-target optimization where evaluations are the limiting factor. Chimera combines concepts of a priori scalarizing with lexicog. approaches and is applicable to any set of n unknown objectives. Importantly, it does not require detailed prior knowledge about individual objectives. The performance of Chimera is demonstrated on several well-established analytic multi-objective benchmark sets using different single-objective optimization algorithms. We further illustrate the applicability and performance of Chimera with two practical examples: (i) the auto-calibration of a virtual robotic sampling sequence for direct-injection, and (ii) the inverse-design of a four-pigment excitonic system for an efficient energy transport. The results indicate that Chimera enables a wide class of optimization algorithms to rapidly find ideal conditions. Addnl., the presented applications highlight the interpretability of Chimera to corroborate design choices for tailoring system parameters.
25. 25

    Häse, F.; Aldeghi, M.; Hickman, R. J.; Roch, L. M.; Aspuru-Guzik, A. Gryffin: An Algorithm for Bayesian Optimization of Categorical Variables Informed by Expert Knowledge. Appl. Phys. Rev. 2021, 8, 031406,  DOI: 10.1063/5.0048164

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    GRYFFIN: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge

    Hase, Florian; Aldeghi, Matteo; Hickman, Riley J.; Roch, Loic M.; Aspuru-Guzik, Alan

    Applied Physics Reviews (2021), 8 (3), 031406CODEN: APRPG5; ISSN:1931-9401. (American Institute of Physics)

    A review. Designing functional mols. and advanced materials requires complex design choices: tuning continuous process parameters such as temps. or flow rates, while simultaneously selecting catalysts or solvents. To date, the development of data-driven expt. planning strategies for autonomous experimentation has largely focused on continuous process parameters, despite the urge to devise efficient strategies for the selection of categorical variables. Here, we introduce GRYFFIN, a general-purpose optimization framework for the autonomous selection of categorical variables driven by expert knowledge. GRYFFIN augments Bayesian optimization based on kernel d. estn. with smooth approxns. to categorical distributions. Leveraging domain knowledge in the form of physicochem. descriptors, GRYFFIN can significantly accelerate the search for promising mols. and materials. GRYFFIN can further highlight relevant correlations between the provided descriptors to inspire phys. insights and foster scientific intuition. In addn. to comprehensive benchmarks, we demonstrate the capabilities and performance of GRYFFIN on three examples in materials science and chem.: (i) the discovery of non-fullerene acceptors for org. solar cells, (ii) the design of hybrid org.-inorg. perovskites for light-harvesting, and (iii) the identification of ligands and process parameters for Suzuki-Miyaura reactions. Our results suggest that GRYFFIN, in its simplest form, is competitive with state-of-the-art categorical optimization algorithms. However, when leveraging domain knowledge provided via descriptors, GRYFFIN outperforms other approaches while simultaneously refining this domain knowledge to promote scientific understanding. (c) 2021 American Institute of Physics.
26. 26

    Hickman, R. J.; Häse, F.; Roch, L. M.; Aspuru-Guzik, A. Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation. arXiv , 2021, 2103.03391.

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    There is no corresponding record for this reference.
27. 27

    Aldeghi, M.; Häse, F.; Hickman, R. J.; Tamblyn, I.; Aspuru-Guzik, A. Golem: An Algorithm for Robust Experiment and Process Optimization. arXiv , 2021, 2103.03716.

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    There is no corresponding record for this reference.
28. 28

    MacLeod, B. P.; Parlane, F. G. L.; Morrissey, T. D.; Häse, F.; Roch, L. M.; Dettelbach, K. E.; Moreira, R.; Yunker, L. P. E.; Rooney, M. B.; Deeth, J. R.; Lai, V.; Ng, G. J.; Situ, H.; Zhang, R. H.; Elliott, M. S.; Haley, T. H.; Dvorak, D. J.; Aspuru-Guzik, A.; Hein, J. E.; Berlinguette, C. P. Self-Driving Laboratory for Accelerated Discovery of Thin-Film Materials. Sci. Adv. 2020, 6, eaaz8867,  DOI: 10.1126/sciadv.aaz8867

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    Glasnov, T. N.; Kappe, C. O. The Microwave-to-Flow Paradigm: Translating High-Temperature Batch Microwave Chemistry to Scalable Continuous-Flow Processes. Chem. – Eur. J. 2011, 17, 11956– 11968,  DOI: 10.1002/chem.201102065

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    The Microwave-to-Flow Paradigm: Translating High-Temperature Batch Microwave Chemistry to Scalable Continuous-Flow Processes

    Glasnov, Toma N.; Kappe, C. Oliver

    Chemistry - A European Journal (2011), 17 (43), 11956-11968CODEN: CEUJED; ISSN:0947-6539. (Wiley-VCH Verlag GmbH & Co. KGaA)

    The popularity of dedicated microwave reactors in many academic and industrial labs. has produced a plethora of synthetic protocols that are based on this enabling technol. In the majority of examples, transformations that require several hours when performed using conventional heating under reflux conditions reach completion in a few minutes or even seconds in sealed-vessel, autoclave-type, microwave reactors. However, one severe drawback of microwave chem. is the difficulty in scaling this technol. to a prodn.-scale level. This concept article demonstrates that this limitation can be overcome by translating batch microwave chem. to scalable continuous-flow processes. For this purpose, conventionally heated micro- or mesofluidic flow devices fitted with a back-pressure regulator are employed, in which the high temps. and pressures attainable in a sealed-vessel microwave chem. batch expt. can be mimicked.
30. 30

    Plutschack, M. B.; Pieber, B.; Gilmore, K.; Seeberger, P. H. The Hitchhiker’s Guide to Flow Chemistry. Chem. Rev. 2017, 117, 11796– 11893,  DOI: 10.1021/acs.chemrev.7b00183

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    The Hitchhiker's Guide to Flow Chemistry

    Plutschack, Matthew B.; Pieber, Bartholomaeus; Gilmore, Kerry; Seeberger, Peter H.

    Chemical Reviews (Washington, DC, United States) (2017), 117 (18), 11796-11893CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)

    Flow chem. involves the use of channels or tubing to conduct a reaction in a continuous stream rather than in a flask. Flow equipment provides chemists with unique control over reaction parameters, enhancing reactivity or in some cases enabling new reactions. This relatively young technol. has received a remarkable amt. of attention in the past decade with many reports on what can be done in flow. Until recently, however, the question, "Should we do this in flow" has merely been an afterthought. This review introduces readers to the basic principles and fundamentals of flow chem. and critically discusses recent flow chem. accounts.
31. 31

    Bianchi, P.; Williams, J. D.; Kappe, C. O. Oscillatory Flow Reactors for Synthetic Chemistry Applications. J. Flow Chem. 2020, 10, 475– 490,  DOI: 10.1007/s41981-020-00105-6

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    Oscillatory flow reactors for synthetic chemistry applications

    Bianchi, Pauline; Williams, Jason D.; Kappe, C. Oliver

    Journal of Flow Chemistry (2020), 10 (3), 475-490CODEN: JFCOBJ; ISSN:2063-0212. (Akademiai Kiado)

    Abstr.: Oscillatory flow reactors (OFRs) superimpose an oscillatory flow to the net movement through a flow reactor. OFRs have been engineered to enable improved mixing, excellent heat- and mass transfer and good plug flow character under a broad range of operating conditions. Such features render these reactors appealing, since they are suitable for reactions that require long residence times, improved mass transfer (such as in biphasic liq.-liq. systems) or to homogeneously suspend solid particles. Various OFR configurations, offering specific features, have been developed over the past two decades, with significant progress still being made. This review outlines the principles and recent advances in OFR technol. and overviews the synthetic applications of OFRs for liq.-liq. and solid-liq. biphasic systems.
32. 32

    Christensen, M.; Yunker, L. P. E.; Shiri, P.; Zepel, T.; Prieto, P. L.; Grunert, S.; Bork, F.; Hein, J. E. Automation Isn’t Automatic. Chem. Sci. 2021, 12, 15473,  DOI: 10.1039/D1SC04588A

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    Automation isn't automatic

    Christensen, Melodie; Yunker, Lars P. E.; Shiri, Parisa; Zepel, Tara; Prieto, Paloma L.; Grunert, Shad; Bork, Finn; Hein, Jason E.

    Chemical Science (2021), 12 (47), 15473-15490CODEN: CSHCCN; ISSN:2041-6520. (Royal Society of Chemistry)

    Automation has become an increasingly popular tool for synthetic chemists over the past decade. Recent advances in robotics and computer science have led to the emergence of automated systems that execute common lab. procedures including parallel synthesis, reaction discovery, reaction optimization, time course studies, and crystn. development. While such systems offer many potential benefits, their implementation is rarely automatic due to the highly specialized nature of synthetic procedures. Each reaction category requires careful execution of a particular sequence of steps, the specifics of which change with different conditions and chem. systems. Careful assessment of these crit. procedural requirements and identification of the tools suitable for effective exptl. execution are key to developing effective automation workflows. Even then, it is often difficult to get all the components of an automated system integrated and operational. Data flows and specialized equipment present yet another level of challenge. Unfortunately, the pain points and process of implementing automated systems are often not shared or remain buried deep in the SI. This perspective provides an overview of the current state of automation of synthetic chem. at the benchtop scale with a particular emphasis on core considerations and the ensuing challenges of deploying a system. Importantly, we aim to reframe automation as decidedly not automatic but rather an iterative process that involves a series of careful decisions (both human and computational) and const. adjustment.
33. 33

    Gillis, E. P.; Burke, M. D. Multistep Synthesis of Complex Boronic Acids from Simple MIDA Boronates. J. Am. Chem. Soc. 2008, 130, 14084– 14085,  DOI: 10.1021/ja8063759

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    Multistep Synthesis of Complex Boronic Acids from Simple MIDA Boronates

    Gillis, Eric P.; Burke, Martin D.

    Journal of the American Chemical Society (2008), 130 (43), 14084-14085CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)

    Due to its sensitivity to most synthetic reagents, it is typically necessary to introduce the boronic acid functional group just prior to its use. Overcoming this important limitation, the authors herein report that air- and chromatog. stable N-methyliminodiacetic acid (MIDA) boronates are compatible with a wide range of common reagents which enables the multistep synthesis of complex boronic acid building blocks from simple B-contg. starting materials. X-ray and variable temp. NMR studies link the unique stability of MIDA boronates to a kinetic inaccessibility of the potentially reactive B p-orbital and/or N lone pair. These findings were collectively harnessed to achieve a short and modular total synthesis of (+)-crocacin C via the iterative cross-coupling of a structurally complex, MIDA-protected haloboronic acid building block.
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    Li, J.; Ballmer, S. G.; Gillis, E. P.; Fujii, S.; Schmidt, M. J.; Palazzolo, A. M. E.; Lehmann, J. W.; Morehouse, G. F.; Burke, M. D. Synthesis of Many Different Types of Organic Small Molecules Using One Automated Process. Science 2015, 347, 1221– 1226,  DOI: 10.1126/science.aaa5414

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    34

    Synthesis of many different types of organic small molecules using one automated process

    Li, Junqi; Ballmer, Steven G.; Gillis, Eric P.; Fujii, Seiko; Schmidt, Michael J.; Palazzolo, Andrea M. E.; Lehmann, Jonathan W.; Morehouse, Greg F.; Burke, Martin D.

    Science (Washington, DC, United States) (2015), 347 (6227), 1221-1226CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)

    A wide variety of human-prepd. and natural product small mols. such as all-trans-retinal I, the phosphodiesterase inhibitor II, oblongolide III, and the secodaphnane core compd. IV were prepd. using an automated process. Alkyl, alkenyl, and aryl N-methyliminodiacetate-protected boronic acid esters (MIDA-boronates) were sepd. on silica gel from other mols. lacking MIDA-boronate moieties; the MIDA-boronates were retained on silica gel when methanol/diethyl ether was used as the eluent and were eluted when THF was used as the eluent. MIDA-boronates thus acted as phase tags which can be activated selectively to undergo Suzuki coupling reactions. Iterative coupling reactions of MIDA-boronates using solid-phase catch-and-release chromatog. purifn. in combination with stereoselective cyclization reactions allowed a variety of small mols. contg. polyene, biaryl, macrocyclic, and fused polycyclic structures to be prepd. using a multipurpose flow reactor (synthesizer).
35. 35

    Anthony, J. E.; Heeney, M.; Ong, B. S. Synthetic Aspects of Organic Semiconductors. MRS Bull. 2008, 33, 698– 705,  DOI: 10.1557/mrs2008.142

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    Synthetic aspects of organic semiconductors

    Anthony, John E.; Heeney, Martin; Ong, Beng S.

    MRS Bulletin (2008), 33 (7), 698-705CODEN: MRSBEA; ISSN:0883-7694. (Materials Research Society)

    A review. This article discusses the importance of the choice of synthetic methodol. in the purity, and therefore performance, of both small-mol. and polymeric org. semiconductors. We discuss common methodologies used in the prepn. of org. semiconductors, paying particular attention to the impurities and byproducts that can arise during these synthetic approaches and how they can have an impact on semiconductor performance.
36. 36

    Kuehne, A. J. C.; Gather, M. C. Organic Lasers: Recent Developments on Materials, Device Geometries, and Fabrication Techniques. Chem. Rev. 2016, 116, 12823– 12864,  DOI: 10.1021/acs.chemrev.6b00172

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    Organic Lasers: Recent Developments on Materials, Device Geometries, and Fabrication Techniques

    Kuehne, Alexander J. C.; Gather, Malte C.

    Chemical Reviews (Washington, DC, United States) (2016), 116 (21), 12823-12864CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)

    Org. dyes have been used as gain medium for lasers since the 1960s, long before the advent of today's org. electronic devices. Org. gain materials are highly attractive for lasing due to their chem. tunability and large stimulated emission cross section. While the traditional dye laser has been largely replaced by solid-state lasers, a no. of new and miniaturized org. lasers have emerged that hold great potential for lab-on-chip applications, biointegration, low-cost sensing and related areas, which benefit from the unique properties of org. gain materials. On the fundamental level, these include high exciton binding energy, low refractive index (compared to inorg. semiconductors), and ease of spectral and chem. tuning. On a technol. level, mech. flexibility and compatibility with simple processing techniques such as printing, roll-to-roll, self-assembly, and soft-lithog. are most relevant. Here, the authors provide a comprehensive review of the developments in the field over the past decade, discussing recent advances in org. gain materials, which are today often based on solid-state org. semiconductors, as well as optical feedback structures, and device fabrication. Recent efforts toward continuous wave operation and elec. pumping of solid-state org. lasers are reviewed, and new device concepts and emerging applications are summarized.
37. 37

    Wu, T. C.; Granda, A. A.; Hotta, K.; Yazdani, S. A.; Pollice, R.; Vestfrid, J.; Hao, H.; Lavigne, C.; Seifrid, M.; Angello, N.; Bencheikh, F.; Hein, J. E.; Burke, M.; Adachi, C.; Aspuru-Guzik, A. A Materials Acceleration Platform for Organic Laser Discovery. ChemRxiv , 2022; DOI: 10.26434/chemrxiv-2022-9zm65 .

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    Yunker, L. P. E.; Donnecke, S.; Ting, M.; Yeung, D.; McIndoe, J. S. PythoMS: A Python Framework To Simplify and Assist in the Processing and Interpretation of Mass Spectrometric Data. J. Chem. Inf. Model. 2019, 59, 1295– 1300,  DOI: 10.1021/acs.jcim.9b00055

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    PythoMS: A Python Framework To Simplify and Assist in the Processing and Interpretation of Mass Spectrometric Data

    Yunker, Lars P. E.; Donnecke, Sofia; Ting, Michelle; Yeung, Darien; McIndoe, J. Scott

    Journal of Chemical Information and Modeling (2019), 59 (4), 1295-1300CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)

    Mass spectrometric data are copious and generate a processing burden that is best dealt with programmatically. PythoMS is a collection of tools based on the Python programming language that assist researchers in creating figures and video output that is informative, clear, and visually compelling. The PythoMS framework introduces a library of classes and a variety of scripts that quickly perform time-consuming tasks: making proprietary output readable; binning intensity vs. time data to simulate longer scan times (and hence reduce noise); calcg. theor. isotope patterns and overlaying them in histogram form on exptl. data (an approach that works even for overlapping signals); rendering videos that enable zooming into the baseline of intensity vs time plots (useful to make sense of data collected over a large dynamic range) or that depict the evolution of different species in a time-lapse format; calcg. aggregates; and providing a quick first-pass at identifying fragments in MS/MS spectra. PythoMS is a living project that will continue to evolve as addnl. scripts are developed and deployed.
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    Burger, B.; Maffettone, P. M.; Gusev, V. V.; Aitchison, C. M.; Bai, Y.; Wang, X.; Li, X.; Alston, B. M.; Li, B.; Clowes, R.; Rankin, N.; Harris, B.; Sprick, R. S.; Cooper, A. I. A Mobile Robotic Chemist. Nature 2020, 583, 237– 241,  DOI: 10.1038/s41586-020-2442-2

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    A mobile robotic chemist

    Burger, Benjamin; Maffettone, Phillip M.; Gusev, Vladimir V.; Aitchison, Catherine M.; Bai, Yang; Wang, Xiaoyan; Li, Xiaobo; Alston, Ben M.; Li, Buyi; Clowes, Rob; Rankin, Nicola; Harris, Brandon; Sprick, Reiner Sebastian; Cooper, Andrew I.

    Nature (London, United Kingdom) (2020), 583 (7815), 237-241CODEN: NATUAS; ISSN:0028-0836. (Nature Research)

    Abstr.: Technologies such as batteries, biomaterials and heterogeneous catalysts have functions that are defined by mixts. of mol. and mesoscale components. As yet, this multi-length-scale complexity cannot be fully captured by atomistic simulations, and the design of such materials from first principles is still rare1-5. Likewise, exptl. complexity scales exponentially with the no. of variables, restricting most searches to narrow areas of materials space. Robots can assist in exptl. searches6-14 but their widespread adoption in materials research is challenging because of the diversity of sample types, operations, instruments and measurements required. Here we use a mobile robot to search for improved photocatalysts for hydrogen prodn. from water15. The robot operated autonomously over eight days, performing 688 expts. within a ten-variable exptl. space, driven by a batched Bayesian search algorithm16-18. This autonomous search identified photocatalyst mixts. that were six times more active than the initial formulations, selecting beneficial components and deselecting neg. ones. Our strategy uses a dexterous19,20 free-roaming robot21-24, automating the researcher rather than the instruments. This modular approach could be deployed in conventional labs. for a range of research problems beyond photocatalysis.
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    Crosby, G. A.; Demas, J. N. Measurement of Photoluminescence Quantum Yields. Review. J. Phys. Chem. 1971, 75, 991– 1024,  DOI: 10.1021/j100678a001

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    Measurement of photoluminescence quantum yields. Review

    Crosby, Glenn A.; Demas, James N.

    Journal of Physical Chemistry (1971), 75 (8), 991-1024CODEN: JPCHAX; ISSN:0022-3654.

    A review is given with 147 refs. Methods and apparatus for measuring photoluminescence quantum yields, standards, apparatus calibration, and data corrections are described.
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    O’Connor, D. V. O.; Phillips, D. Time-Correlated Single Photon Counting; Academic Press: London, 1984.

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    Cava, R. J.; DiSalvo, F. J.; Brus, L. E.; Dunbar, K. R.; Gorman, C. B.; Haile, S. M.; Interrante, L. V.; Musfeldt, J. L.; Navrotsky, A.; Nuzzo, R. G.; Pickett, W. E.; Wilkinson, A. P.; Ahn, C.; Allen, J. W.; Burns, P. C.; Ceder, G.; Chidsey, C. E. D.; Clegg, W.; Coronado, E.; Dai, H.; Deem, M. W.; Dunn, B. S.; Galli, G.; Jacobson, A. J.; Kanatzidis, M.; Lin, W.; Manthiram, A.; Mrksich, M.; Norris, D.; Nozik, A. J.; Peng, X.; Rawn, C.; Rolison, D.; Singh, D. J.; Toby, B. H.; Tolbert, S.; Wiesner, U. B.; Woodward, P. M.; Yang, P. Future Directions in Solid State Chemistry: Report of the NSF-Sponsored Workshop. Prog. Solid State Chem. 2002, 30, 1– 101,  DOI: 10.1016/S0079-6786(02)00010-9

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    Future directions in solid state chemistry: report of the NSF-sponsored workshop

    Cava, Robert J.; DiSalvo, Francis J.; Brus, Louis E.; Dunbar, Kim R.; Gorman, Christopher B.; Haile, Sossina M.; Interrante, Leonard V.; Musfeldt, Janice L.; Navrotsky, Alexandra; Nuzzo, Ralph G.; Pickett, Warren E.; Wilkinson, Angus P.; Ahn, Channing; Allen, James W.; Burns, Peter C.; Ceder, Gerdrand; Chidsey, Christopher E. D.; Clegg, William; Coronado, Eugenio; Dai, Hongjie; Deem, Michael W.; Dunn, Bruce S.; Galli, Giulia; Jacobson, Allan J.; Kanatzidis, Mercouri; Lin, Wenbin; Manthiram, Arumugam; Mrksich, Milan; Norris, David; Nozik, Arthur J.; Peng, Xiaogang; Rawn, Claudia; Rolison, Debra; Singh, David J.; Toby, Brian H.; Tolbert, Sarah; Wiesner, Ulrich B.; Woodward, Patrick M.; Yang, Peidong

    Progress in Solid State Chemistry (2002), 30 (1-2), 1-101CODEN: PSSTAW; ISSN:0079-6786. (Elsevier Science Ltd.)

    A review. A long-established area of scientific excellence in Europe, solid state chem. has emerged in the US in the past two decades as a field experiencing rapid growth and development. At its core, it is an interdisciplinary melding of chem., physics, engineering, and materials science, as it focuses on the design, synthesis and structural characterization of new chem. compds. and characterization of their phys. properties. As a consequence of this inherently interdisciplinary character, the solid state chem. community is highly open to the influx of new ideas and directions. The inclusionary character of the field's culture has been a significant factor in its continuing growth and vitality. This report presents an elaboration of discussions held during an NSF-sponsored workshop on Future Directions in Solid State Chem., held on the UC Davis Campus in Oct. 2001. That workshop was the second of a series of workshops planned in this topical area. The first, held at NSF headquarters in Arlington, Virginia, in Jan. of 1998, was designed to address the core of the field, describing how it has developed in the US and worldwide in the past decade, and how the members of the community saw the central thrusts of research and education in solid state chem. proceeding in the next several years. A report was published on that workshop (J.M. Honig, chair, "Proceedings of the Workshop on the Present Status and Future Developments of Solid State Chem. and Materials", Arlington, VA, Jan. 15-16, 1998) describing the state of the field and recommendations for future development of the core discipline. In the spirit of continuing to expand the scope of the solid state chem. community into new areas of scientific inquiry, the workshop elaborated in this document was designed to address the interfaces between our field and fields where we thought there would be significant opportunity for the development of new scientific advancements through increased interaction. The 7 topic areas, described in detail in this report, ranged from those with established ties to solid state chem. such as Earth and planetary sciences, and energy storage and conversion, to those such as condensed matter physics, where the connections are in their infancy, to biol., where the opportunities for connections are largely unexplored. Exciting ties to materials chem. were explored in discussions on mol. materials and nanoscale science, and a session on the importance of improving the ties between solid state chemists and experts in characterization at national exptl. facilities was included. The full report elaborates these ideas extensively.
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    Aspuru-Guzik, A.; Persson, K. Materials Acceleration Platform: Accelerating Advanced Energy Materials Discovery by Integrating High-Throughput Methods and Artificial Intelligence; Mission Innovation: Innovation Challenge 6; Canadian Institute for Advanced Research, 2018.

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    Fabbri, E.; Schmidt, T. J. Oxygen Evolution Reaction─The Enigma in Water Electrolysis. ACS Catal. 2018, 8, 9765– 9774,  DOI: 10.1021/acscatal.8b02712

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    Oxygen Evolution Reaction-The Enigma in Water Electrolysis

    Fabbri, Emiliana; Schmidt, Thomas J.

    ACS Catalysis (2018), 8 (10), 9765-9774CODEN: ACCACS; ISSN:2155-5435. (American Chemical Society)

    A review is given. We aim at increasing the awareness among the scientific community devoted to progresses in water electrolyzers of the very recent development made in the fundamental understanding of the OER, particularly focusing on the increasing consciousness that several processes actually underpin the evolution of O from a metal oxide catalyst. Traditionally, the OER mechanism on metal oxides has been derived from that on metal catalysts, where the main parameter governing the reaction overpotential is the binding strength of O (or oxygenated species/intermediates) on the catalyst surface following the Sabatier principle: the best catalyst in terms of displaying the min. overpotential binds O on its surface neither too strongly nor too weakly.
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    Coley, C. W.; Eyke, N. S.; Jensen, K. F. Autonomous Discovery in the Chemical Sciences Part II: Outlook. Angew. Chem., Int. Ed. 2020, 59 (52), 23414– 23436,  DOI: 10.1002/anie.201909989

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    Autonomous Discovery in the Chemical Sciences Part II: Outlook

    Coley, Connor W.; Eyke, Natalie S.; Jensen, Klavs F.

    Angewandte Chemie, International Edition (2020), 59 (52), 23414-23436CODEN: ACIEF5; ISSN:1433-7851. (Wiley-VCH Verlag GmbH & Co. KGaA)

    This two-part Review examines how automation has contributed to different aspects of discovery in the chem. sciences. In this second part, we reflect on a selection of exemplary studies. It is increasingly important to articulate what the role of automation and computation has been in the scientific process and how that has or has not accelerated discovery. One can argue that even the best automated systems have yet to "discover" despite being incredibly useful as lab. assistants. We must carefully consider how they have been and can be applied to future problems of chem. discovery in order to effectively design and interact with future autonomous platforms. The majority of this Review defines a large set of open research directions, including improving our ability to work with complex data, build empirical models, automate both phys. and computational expts. for validation, select expts., and evaluate whether we are making progress towards the ultimate goal of autonomous discovery. Addressing these practical and methodol. challenges will greatly advance the extent to which autonomous systems can make meaningful discoveries.
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    Molga, K.; Szymkuć, S.; Grzybowski, B. A. Chemist Ex Machina: Advanced Synthesis Planning by Computers. Acc. Chem. Res. 2021, 54, 1094– 1106,  DOI: 10.1021/acs.accounts.0c00714

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    Chemist Ex Machina: Advanced Synthesis Planning by Computers

    Molga, Karol; Szymkuc, Sara; Grzybowski, Bartosz A.

    Accounts of Chemical Research (2021), 54 (5), 1094-1106CODEN: ACHRE4; ISSN:0001-4842. (American Chemical Society)

    Teaching computers to plan multistep syntheses of arbitrary target mols.-including natural products-has been one of the oldest challenges in chem., dating back to the 1960s. This Account recapitulates two decades of our group's work on the software platform called Chematica, which very recently achieved this long-sought objective and has been shown capable of planning synthetic routes to complex natural products, several of which were validated in the lab. For the machine to plan syntheses at an expert level, it must know the rules describing chem. reactions and use these rules to expand and search the networks of synthetic options. The rules must be of high quality: They must delineate accurately the scope of admissible substituents, capture all relevant stereochem. information, detect potential reactivity conflicts, and protection requirements. They should yield only those synthons that are chem. stable and energetically allowed (e.g., not too strained) and should be able to extrapolate beyond examples already published in the literature. In parallel, the network-search algorithms must be able to assign meaningful scores to the sets of synthons they encounter, make judicious choices which of the network's branches to expand, and when to withdraw from unpromising ones. They must be able to strategize over multiple steps to resolve intermittent reactivity conflicts, exchange functional groups, or overcome local maxima of mol. complexity. Meeting all these requirements makes the problem of computer-driven retrosynthesis very multifaceted, combining expert and AI approaches further supplemented by quantum-mech. and mol.-mechanics calcns. Development of Chematica has been a very long and gradual process because all these components are needed. Any shortcuts-for example, reliance on only expert or only data-based approaches-yield chem. na.ovrddot.ive and often erroneous syntheses, esp. for complex targets. On the bright side, once all the requisite algorithms are implemented-as they now are-they not only streamline conventional synthetic planning but also enable completely new modalities that would challenge any human chemist, for example, synthesis with multiple constraints imposed simultaneously or library-wide syntheses in which the machine constructs "global plans" leading to multiple targets and benefiting from the use of common intermediates. These types of analyses will have profound impact on the practice of chem. industry, designing more economical, more green, and less hazardous pathways.
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    Shim, E.; Kammeraad, J. A.; Xu, Z.; Tewari, A.; Cernak, T.; Zimmerman, P. M. Predicting Reaction Conditions from Limited Data through Active Transfer Learning. Chem. Sci. 2022, 13, 6655– 6668,  DOI: 10.1039/D1SC06932B

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    Predicting reaction conditions from limited data through active transfer learning

    Shim, Eunjae; Kammeraad, Joshua A.; Xu, Ziping; Tewari, Ambuj; Cernak, Tim; Zimmerman, Paul M.

    Chemical Science (2022), 13 (22), 6655-6668CODEN: CSHCCN; ISSN:2041-6520. (Royal Society of Chemistry)

    Transfer and active learning have the potential to accelerate the development of new chem. reactions, using prior data and new expts. to inform models that adapt to the target area of interest. This article shows how specifically tuned machine learning models, based on random forest classifiers, can expand the applicability of Pd-catalyzed cross-coupling reactions to types of nucleophiles unknown to the model. First, model transfer is shown to be effective when reaction mechanisms and substrates are closely related, even when models are trained on relatively small nos. of data points. Then, a model simplification scheme is tested and found to provide comparative predictivity on reactions of new nucleophiles that include unseen reagent combinations. Lastly, for a challenging target where model transfer only provides a modest benefit over random selection, an active transfer learning strategy is introduced to improve model predictions. Simple models, composed of a small no. of decision trees with limited depths, are crucial for securing generalizability, interpretability, and performance of active transfer learning.
48. 48

    Grzybowski, B. A.; Badowski, T.; Molga, K.; Szymkuć, S. Network Search Algorithms and Scoring Functions for Advanced-Level Computerized Synthesis Planning. WIREs Comput. Mol. Sci., e1630; DOI: 10.1002/wcms.1630 .

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    Shahriari, B.; Swersky, K.; Wang, Z.; Adams, R. P.; de Freitas, N. Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proc. IEEE 2016, 104, 148– 175,  DOI: 10.1109/JPROC.2015.2494218

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    Hickman, R. J.; Aldeghi, M.; Häse, F.; Aspuru-Guzik, A. Bayesian Optimization with Known Experimental and Design Constraints for Chemistry Applications. arXiv , 2022, 2203.17241.

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51. 51

    Blaženović, I.; Kind, T.; Torbašinović, H.; Obrenović, S.; Mehta, S. S.; Tsugawa, H.; Wermuth, T.; Schauer, N.; Jahn, M.; Biedendieck, R.; Jahn, D.; Fiehn, O. Comprehensive Comparison of in Silico MS/MS Fragmentation Tools of the CASMI Contest: Database Boosting Is Needed to Achieve 93% Accuracy. J. Cheminformatics 2017, 9, 32,  DOI: 10.1186/s13321-017-0219-x

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    Comprehensive comparison of in silico MS/MS fragmentation tools of the CASMI contest: database boosting is needed to achieve 93% accuracy

    Blazenovic Ivana; Jahn Martina; Biedendieck Rebekka; Jahn Dieter; Blazenovic Ivana; Schauer Nicolas; Blazenovic Ivana; Kind Tobias; Mehta Sajjan S; Wermuth Tobias; Fiehn Oliver; Torbasinovic Hrvoje; Obrenovic Slobodan; Tsugawa Hiroshi; Fiehn Oliver

    Journal of cheminformatics (2017), 9 (1), 32 ISSN:1758-2946.

    In mass spectrometry-based untargeted metabolomics, rarely more than 30% of the compounds are identified. Without the true identity of these molecules it is impossible to draw conclusions about the biological mechanisms, pathway relationships and provenance of compounds. The only way at present to address this discrepancy is to use in silico fragmentation software to identify unknown compounds by comparing and ranking theoretical MS/MS fragmentations from target structures to experimental tandem mass spectra (MS/MS). We compared the performance of four publicly available in silico fragmentation algorithms (MetFragCL, CFM-ID, MAGMa+ and MS-FINDER) that participated in the 2016 CASMI challenge. We found that optimizing the use of metadata, weighting factors and the manner of combining different tools eventually defined the ultimate outcomes of each method. We comprehensively analysed how outcomes of different tools could be combined and reached a final success rate of 93% for the training data, and 87% for the challenge data, using a combination of MAGMa+, CFM-ID and compound importance information along with MS/MS matching. Matching MS/MS spectra against the MS/MS libraries without using any in silico tool yielded 60% correct hits, showing that the use of in silico methods is still important.
52. 52

    De Vijlder, T.; Valkenborg, D.; Lemière, F.; Romijn, E. P.; Laukens, K.; Cuyckens, F. A Tutorial in Small Molecule Identification via Electrospray Ionization-mass Spectrometry: The Practical Art of Structural Elucidation. Mass Spectrom. Rev. 2018, 37, 607– 629,  DOI: 10.1002/mas.21551

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    A tutorial in small molecule identification via electrospray ionization-mass spectrometry: The practical art of structural elucidation

    De Vijlder, Thomas; Valkenborg, Dirk; Lemiere, Filip; Romijn, Edwin P.; Laukens, Kris; Cuyckens, Filip

    Mass Spectrometry Reviews (2018), 37 (5), 607-629CODEN: MSRVD3; ISSN:0277-7037. (John Wiley & Sons, Inc.)

    A review. The identification of unknown mols. has been one of the cornerstone applications of mass spectrometry for decades. Most of what is discussed in this tutorial also applies to other atm. pressure ionization methods like atm. pressure chem./photoionization. We focus primarily on the fundamental steps of MS-based structural elucidation of individual unknown compds., rather than describing strategies for large-scale identification in complex samples. We critically discuss topics like the detection of protonated and deprotonated ions ([M + H]+ and [M-H]-) as well as other adduct ions, the detn. of the mol. formula, and provide some basic rules on the interpretation of product ion spectra. Our tutorial focuses primarily on the fundamental steps of MS-based structural elucidation of individual unknown compds. (eg, contaminants in chem. prodn., pharmacol. alteration of drugs), rather than describing strategies for large-scale identification in complex samples. This tutorial also discusses strategies to obtain useful orthogonal information (UV/Vis, H/D exchange, chem. derivatization, etc) and offers an overview of the different informatics tools and approaches that can be used for structural elucidation of small mols. It is primarily intended for beginning mass spectrometrists and researchers from other mass spectrometry sub-disciplines that want to get acquainted with structural elucidation are interested in some practical tips and tricks.
53. 53

    Cook, A.; Johnson, A. P.; Law, J.; Mirzazadeh, M.; Ravitz, O.; Simon, A. Computer-Aided Synthesis Design: 40 Years On. WIREs Comput. Mol. Sci. 2012, 2, 79– 107,  DOI: 10.1002/wcms.61

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    53

    Computer-aided synthesis design. 40 years on

    Cook, Anthony; Johnson, A. Peter; Law, James; Mirzazadeh, Mahdi; Ravitz, Orr; Simon, Aniko

    Wiley Interdisciplinary Reviews: Computational Molecular Science (2012), 2 (1), 79-107CODEN: WIRCAH; ISSN:1759-0884. (Wiley-Blackwell)

    A review. The discipline of retrosynthetic anal. is now just over 40 years old. From the earliest day, attempts were made to incorporate this approach into computer programs to test the extent in which chem. perception and synthetic thinking could be formalized. Despite pioneering research efforts, computer-aided synthetic anal. failed to achieve widespread routine use by chemists, which can be attributed in part to the difficulty of building the required high-quality retrosynthetic transform databases required for credible analyses. However, with the advent over the past 25 years of large comprehensive reaction databases, work on successfully automating the construction of reliable and comprehensive reaction rule databases is promising to revitalize research in this field. This review compares and contrasts the diverse approaches taken by selected programs in both the design and implementation of mol. feature perception and reaction rule representation, and the concepts of synthetic strategy selection, representation, and execution were reviewed. In particular, the current work on automating the construction of reliable and comprehensive synthetic rule sets from available reaction databases in newer programs such as ARChem were discussed. The authors argued that the progress achieved in this aspect paves the way to a deeper exploration of computer approaches to applying strategy and control in the synthesis problem.
54. 54

    Rappoport, D.; Aspuru-Guzik, A. Predicting Feasible Organic Reaction Pathways Using Heuristically Aided Quantum Chemistry. J. Chem. Theory Comput. 2019, 15, 4099– 4112,  DOI: 10.1021/acs.jctc.9b00126

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    Predicting Feasible Organic Reaction Pathways Using Heuristically Aided Quantum Chemistry

    Rappoport, Dmitrij; Aspuru-Guzik, Alan

    Journal of Chemical Theory and Computation (2019), 15 (7), 4099-4112CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)

    Studying org. reaction mechanisms using quantum chem. methods requires from the researcher an extensive knowledge of both org. chem. and first-principles computation. The need for empirical knowledge arises because any reasonably complete exploration of the potential energy surfaces (PES) of org. reactions is computationally prohibitive. The authors have previously introduced the heuristically-aided quantum chem. (HAQC) approach to modeling complex chem. reactions, which abstrs. the empirical knowledge in terms of chem. heuristics-simple rules guiding the PES exploration-and combines them with structure optimizations using quantum chem. methods. The HAQC approach makes use of heuristic kinetic criteria for selecting reaction paths that are not only plausible, i.e., consistent with the empirical rules of org. reactivity, but also feasible under the reaction conditions. The authors develop heuristic kinetic feasibility criteria, which correctly predict feasible reaction pathways for a wide range of simple polar (substitutions, addns., and eliminations) and pericyclic org. reactions (cyclizations, sigmatropic shifts, and cycloaddns.). In contrast to knowledge-based reaction mechanism prediction methods, the same kinetic heuristics are successful in classifying reaction pathways as feasible or infeasible across this diverse set of reaction mechanisms. The energy profiles of HAQC and their potential applications in machine learning of chem. reactivity are discussed.
55. 55

    Rappoport, D. Reaction Networks and the Metric Structure of Chemical Space(s). J. Phys. Chem. A 2019, 123, 2610– 2620,  DOI: 10.1021/acs.jpca.9b00519

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    Reaction Networks and the Metric Structure of Chemical Space(s)

    Rappoport, Dmitrij

    Journal of Physical Chemistry A (2019), 123 (13), 2610-2620CODEN: JPCAFH; ISSN:1089-5639. (American Chemical Society)

    In this paper, we develop a formal definition of chem. space as a discrete metric space of mols. and analyze its properties. To this end, we utilize the shortest path metric on reaction networks to define a distance function between mols. of the same stoichiometry (no. of atoms). The distance between mols. with different stoichiometries is formalized by making use of the partial ordering of stoichiometries with respect to inclusion. Calcns. of fractal dimension on metric spaces for individual stoichiometries show that they have low intrinsic dimensionality, about an order of magnitude less than the dimension of the underlying reactive potential energy surface. Our findings suggest that efficient search strategies on chem. space can be designed that take advantage of its metric structure.
56. 56

    Wołos, A.; Roszak, R.; Żądło-Dobrowolska, A.; Beker, W.; Mikulak-Klucznik, B.; Spólnik, G.; Dygas, M.; Szymkuć, S.; Grzybowski, B. A. Synthetic Connectivity, Emergence, and Self-Regeneration in the Network of Prebiotic Chemistry. Science 2020, 369, eaaw1955,  DOI: 10.1126/science.aaw1955

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57. 57

    Arya, A.; Ray, J.; Sharma, S.; Simbron, R. C.; Lozano, A.; Smith, H. B.; Andersen, J. L.; Chen, H.; Meringer, M.; Cleaves, H. J. An Open Source Computational Workflow for the Discovery of Autocatalytic Networks in Abiotic Reactions. Chem. Sci. 2022, 13, 4838– 4853,  DOI: 10.1039/D2SC00256F

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    An open source computational workflow for the discovery of autocatalytic networks in abiotic reactions

    Arya, Aayush; Ray, Jessica; Sharma, Siddhant; Cruz Simbron, Romulo; Lozano, Alejandro; Smith, Harrison B.; Andersen, Jakob Lykke; Chen, Huan; Meringer, Markus; Cleaves II, Henderson James

    Chemical Science (2022), 13 (17), 4838-4853CODEN: CSHCCN; ISSN:2041-6520. (Royal Society of Chemistry)

    A central question in origins of life research is how non-entailed chem. processes, which simply dissipate chem. energy because they can do so due to immediate reaction kinetics and thermodn, enabled the origin of highly-entailed ones, in which concatenated kinetically and thermodynamically favorable processes enhanced some processes over others. Some degree of mol. complexity likely had to be supplied by environmental processes to produce entailed self-replicating processes. The origin of entailment, therefore, must connect to fundamental chem. that builds mol. complexity. We present here an open-source chemoinformatic workflow to model abiol. chem. to discover such entailment. This pipeline automates generation of chem. reaction networks and their anal. to discover novel compds. and autocatalytic processes. We demonstrate this pipelines capabilities against a well-studied model system by vetting it against expremental data. This workflow can enable rapid identification of products of complex chemistries and their underlying synthetic relationships to help identify autocatalysis, and potentially self-organization, in such systems. The algorithms used in this study are open-source and reconfigurable by other user-developed workflows.
58. 58

    Allen, F.; Pon, A.; Wilson, M.; Greiner, R.; Wishart, D. CFM-ID: A Web Server for Annotation, Spectrum Prediction and Metabolite Identification from Tandem Mass Spectra. Nucleic Acids Res. 2014, 42, W94– W99,  DOI: 10.1093/nar/gku436

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    CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra

    Allen, Felicity; Pon, Allison; Wilson, Michael; Greiner, Russ; Wishart, David

    Nucleic Acids Research (2014), 42 (W1), W94-W99CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)

    CFM-ID is a web server supporting three tasks assocd. with the interpretation of tandem mass spectra (MS/MS) for the purpose of automated metabolite identification: annotation of the peaks in a spectrum for a known chem. structure; prediction of spectra for a given chem. structure and putative metabolite identification-a predicted ranking of possible candidate structures for a target spectrum. The algorithms used for these tasks are based on Competitive Fragmentation Modeling (CFM), a recently introduced probabilistic generative model for the MS/MS fragmentation process that uses machine learning techniques to learn its parameters from data. These algorithms have been extensively tested on multiple datasets and have been shown to out-perform existing methods such as MetFrag and FingerId. This web server provides a simple interface for using these algorithms and a graphical display of the resulting annotations, spectra and structures. CFM-ID is made freely available at http://cfmid.wishartlab.com.
59. 59

    Djoumbou-Feunang, Y.; Pon, A.; Karu, N.; Zheng, J.; Li, C.; Arndt, D.; Gautam, M.; Allen, F.; Wishart, D. S. CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification. Metabolites 2019, 9, 72,  DOI: 10.3390/metabo9040072

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    CFM-ID 3.0: significantly improved ESI-MS/MS prediction and compound identification

    Djoumbou-Feunang, Yannick; Pon, Allison; Karu, Naama; Zheng, Jiamin; Li, Carin; Arndt, Dav; Gautam, Maheswor; Allen, Felicity; Wishart, Dav S.

    Metabolites (2019), 9 (4), 72CODEN: METALU; ISSN:2218-1989. (MDPI AG)

    Metabolite identification for untargeted metabolomics is often hampered by the lack of exptl. collected ref. spectra from tandem mass spectrometry (MS/MS). To circumvent this problem, Competitive Fragmentation Modeling-ID (CFM-ID) was developed to accurately predict electrospray ionization-MS/MS (ESI-MS/MS) spectra from chem. structures and to aid in compd. identification via MS/MS spectral matching. While earlier versions of CFM-ID performed very well, CFM-ID's performance for predicting the MS/MS spectra of certain classes of compds., including many lipids, was quite poor. Furthermore, CFM-ID's compd. identification capabilities were limited because it did not use exptl. available MS/MS spectra nor did it exploit metadata in its spectral matching algorithm. Here, we describe significant improvements to CFM-ID's performance and speed. These include (1) the implementation of a rule-based fragmentation approach for lipid MS/MS spectral prediction, which greatly improves the speed and accuracy of CFM-ID; (2) the inclusion of exptl. MS/MS spectra and other metadata to enhance CFM-ID's compd. identification abilities; (3) the development of new scoring functions that improves CFM-ID's accuracy by 21.1%; and (4) the implementation of a chem. classification algorithm that correctly classifies unknown chems. (based on their MS/MS spectra) in >80% of the cases. This improved version called CFM-ID 3.0 is freely available as a web server.
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    Ji, H.; Deng, H.; Lu, H.; Zhang, Z. Predicting a Molecular Fingerprint from an Electron Ionization Mass Spectrum with Deep Neural Networks. Anal. Chem. 2020, 92, 8649– 8653,  DOI: 10.1021/acs.analchem.0c01450

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    Predicting a Molecular Fingerprint from an Electron Ionization Mass Spectrum with Deep Neural Networks

    Ji, Hongchao; Deng, Hanzi; Lu, Hongmei; Zhang, Zhimin

    Analytical Chemistry (Washington, DC, United States) (2020), 92 (13), 8649-8653CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)

    Electron ionization-mass spectrometry (EI-MS) hyphenated to gas chromatog. (GC) is the workhorse for analyzing volatile compds. in complex samples. The spectral matching method can only identify compds. within the spectral database. In response, we present a deep-learning-based approach (DeepEI) for structure elucidation of an unknown compd. with its EI-MS spectrum. DeepEI employs deep neural networks to predict mol. fingerprints from an EI-MS spectrum and searches the mol. structure database with the predicted fingerprints. We evaluated DeepEI with MassBank spectra, and the results indicate DeepEI is an effective identification method. In addn., DeepEI can work cooperatively with database spectral matching and NEIMS (fingerprint to spectrum method) to improve identification accuracy.
61. 61

    Xue, J.; Guijas, C.; Benton, H. P.; Warth, B.; Siuzdak, G. METLIN MS 2 Molecular Standards Database: A Broad Chemical and Biological Resource. Nat. Methods 2020, 17, 953– 954,  DOI: 10.1038/s41592-020-0942-5

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    METLIN MS2 molecular standards database: a broad chemical and biological resource

    Xue, Jingchuan; Guijas, Carlos; Benton, H. Paul; Warth, Benedikt; Siuzdak, Gary

    Nature Methods (2020), 17 (10), 953-954CODEN: NMAEA3; ISSN:1548-7091. (Nature Research)

    There is no expanded citation for this reference.
62. 62

    Horai, H.; Arita, M.; Kanaya, S.; Nihei, Y.; Ikeda, T.; Suwa, K.; Ojima, Y.; Tanaka, K.; Tanaka, S.; Aoshima, K.; Oda, Y.; Kakazu, Y.; Kusano, M.; Tohge, T.; Matsuda, F.; Sawada, Y.; Hirai, M. Y.; Nakanishi, H.; Ikeda, K.; Akimoto, N.; Maoka, T.; Takahashi, H.; Ara, T.; Sakurai, N.; Suzuki, H.; Shibata, D.; Neumann, S.; Iida, T.; Tanaka, K.; Funatsu, K.; Matsuura, F.; Soga, T.; Taguchi, R.; Saito, K.; Nishioka, T. MassBank: A Public Repository for Sharing Mass Spectral Data for Life Sciences. J. Mass Spectrom. 2010, 45, 703– 714,  DOI: 10.1002/jms.1777

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    MassBank: a public repository for sharing mass spectral data for life sciences

    Horai, Hisayuki; Arita, Masanori; Kanaya, Shigehiko; Nihei, Yoshito; Ikeda, Tasuku; Suwa, Kazuhiro; Ojima, Yuya; Tanaka, Kenichi; Tanaka, Satoshi; Aoshima, Ken; Oda, Yoshiya; Kakazu, Yuji; Kusano, Miyako; Tohge, Takayuki; Matsuda, Fumio; Sawada, Yuji; Hirai, Masami Yokota; Nakanishi, Hiroki; Ikeda, Kazutaka; Akimoto, Naoshige; Maoka, Takashi; Takahashi, Hiroki; Ara, Takeshi; Sakurai, Nozomu; Suzuki, Hideyuki; Shibata, Daisuke; Neumann, Steffen; Iida, Takashi; Tanaka, Ken; Funatsu, Kimito; Matsuura, Fumito; Soga, Tomoyoshi; Taguchi, Ryo; Saito, Kazuki; Nishioka, Takaaki

    Journal of Mass Spectrometry (2010), 45 (7), 703-714CODEN: JMSPFJ; ISSN:1076-5174. (John Wiley & Sons Ltd.)

    MassBank is the first public repository of mass spectra of small chem. compds. for life sciences (<3000 Da). The database contains 605 electron-ionization mass spectrometry(EI-MS), 137 fast atom bombardment MS and 9276 electrospray ionization (ESI)-MSn data of 2337 authentic compds. of metabolites, 11 545 EI-MS and 834 other-MS data of 10 286 volatile natural and synthetic compds., and 3045 ESI-MS2 data of 679 synthetic drugs contributed by 16 research groups (Jan. 2010). ESI-MS2 data were analyzed under nonstandardized, independent exptl. conditions. MassBank is a distributed database. Each research group provides data from its own MassBank data servers distributed on the Internet. MassBank users can access either all of the MassBank data or a subset of the data by specifying one or more exptl. conditions. In a spectral search to retrieve mass spectra similar to a query mass spectrum, the similarity score is calcd. by a weighted cosine correlation in which weighting exponents on peak intensity and the mass-to-charge ratio are optimized to the ESI-MS2 data. MassBank also provides a merged spectrum for each compd. prepd. by merging the analyzed ESI-MS2 data on an identical compd. under different collision-induced dissocn. conditions. Data merging has significantly improved the precision of the identification of a chem. compd. by 21-23% at a similarity score of 0.6. Thus, MassBank is useful for the identification of chem. compds. and the publication of exptl. data.
63. 63

    NIST. NIST 20 Tandem Mass Spectral Libraries; https://chemdata.nist.gov/dokuwiki/doku.php?id=chemdata:msms (accessed 2022-07-19).

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    Sans, V.; Porwol, L.; Dragone, V.; Cronin, L. A Self Optimizing Synthetic Organic Reactor System Using Real-Time in-Line NMR Spectroscopy. Chem. Sci. 2015, 6, 1258– 1264,  DOI: 10.1039/C4SC03075C

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    A self optimizing synthetic organic reactor system using real-time in-line NMR spectroscopy

    Sans, Victor; Porwol, Luzian; Dragone, Vincenza; Cronin, Leroy

    Chemical Science (2015), 6 (2), 1258-1264CODEN: CSHCCN; ISSN:2041-6520. (Royal Society of Chemistry)

    A configurable platform for synthetic chem. incorporating an in-line bench-top NMR capable of monitoring and controlling org. reactions in real-time is discussed. The platform is controlled by a modular LabView software control system for hardware, NMR, data anal., and feedback optimization. Using this platform, real-time advanced structural characterization of reaction mixts., including 19F, 13C, DEPT, 2-dimensional NMR spectroscopy (COSY, HSQC, 19F-COSY), are reported for the first time. The potential of this technique was demonstrated by optimizing a catalytic org. reaction in real-time, showing its applicability to self-optimizing systems using criteria such as stereo-selectivity, multi-nuclear measurements, or 2-dimensional correlations.
65. 65

    Granda, J. M.; Donina, L.; Dragone, V.; Long, D.-L.; Cronin, L. Controlling an Organic Synthesis Robot with Machine Learning to Search for New Reactivity. Nature 2018, 559, 377– 381,  DOI: 10.1038/s41586-018-0307-8

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    Controlling an organic synthesis robot with machine learning to search for new reactivity

    Granda, Jaroslaw M.; Donina, Liva; Dragone, Vincenza; Long, De-Liang; Cronin, Leroy

    Nature (London, United Kingdom) (2018), 559 (7714), 377-381CODEN: NATUAS; ISSN:0028-0836. (Nature Research)

    The discovery of chem. reactions is an inherently unpredictable and time-consuming process. An attractive alternative is to predict reactivity, although relevant approaches, such as computer-aided reaction design, are still in their infancy. Reaction prediction based on high-level quantum chem. methods is complex, even for simple mols. Although machine learning is powerful for data anal., its applications in chem. are still being developed. Inspired by strategies based on chemists' intuition, we propose that a reaction system controlled by a machine learning algorithm may be able to explore the space of chem. reactions quickly, esp. if trained by an expert. Here we present an org. synthesis robot that can perform chem. reactions and anal. faster than they can be performed manually, as well as predict the reactivity of possible reagent combinations after conducting a small no. of expts., thus effectively navigating chem. reaction space. By using machine learning for decision making, enabled by binary encoding of the chem. inputs, the reactions can be assessed in real time using NMR and IR spectroscopy. The machine learning system was able to predict the reactivity of about 1,000 reaction combinations with accuracy greater than 80 per cent after considering the outcomes of slightly over 10 per cent of the dataset. This approach was also used to calc. the reactivity of published datasets. Further, by using real-time data from our robot, these predictions were followed up manually by a chemist, leading to the discovery of four reactions.
66. 66

    Maschmeyer, T.; Prieto, P. L.; Grunert, S.; Hein, J. E. Exploration of Continuous-Flow Benchtop NMR Acquisition Parameters and Considerations for Reaction Monitoring. Magn. Reson. Chem. 2020, 58, 1234– 1248,  DOI: 10.1002/mrc.5094

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    Exploration of continuous-flow benchtop NMR acquisition parameters and considerations for reaction monitoring

    Maschmeyer, Tristan; Prieto, Paloma L.; Grunert, Shad; Hein, Jason E.

    Magnetic Resonance in Chemistry (2020), 58 (12), 1234-1248CODEN: MRCHEG; ISSN:0749-1581. (John Wiley & Sons Ltd.)

    This study focused on fundamental data acquisition parameter selection for a benchtop NMR (NMR) system with continuous flow, applicable for reaction monitoring. The effect of flow rate on the mixing behaviors within a flow cell was obsd., along with an exponential decay relationship between flow rate and the apparent spin-lattice relaxation time (T1*) of benzaldehyde. We also monitored sensitivity (as detd. by signal-to-noise ratios; SNRs) under various flow rates, analyte concns., and temps. of the analyte flask. Results suggest that a max. SNR can be achieved with low to medium flow rates and higher analyte concns. This was consistent with data collected with parameters that promote either slow or fast data acquisition. We further consider the effect of these conditions on the analyte's residence time, T1*, and magnetic field inhomogeneity that is a product of continuous flow. Altogether, our results demonstrate how fundamental acquisition parameters can be manipulated to achieve optimal data acquisition in continuous-flow NMR systems.
67. 67

    Chatterjee, S.; Guidi, M.; Seeberger, P. H.; Gilmore, K. Automated Radial Synthesis of Organic Molecules. Nature 2020, 579, 379– 384,  DOI: 10.1038/s41586-020-2083-5

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    Automated radial synthesis of organic molecules

    Chatterjee, Sourav; Guidi, Mara; Seeberger, Peter H.; Gilmore, Kerry

    Nature (London, United Kingdom) (2020), 579 (7799), 379-384CODEN: NATUAS; ISSN:0028-0836. (Nature Research)

    Abstr.: Automated synthesis platforms accelerate and simplify the prepn. of mols. by removing the phys. barriers to org. synthesis. This provides unrestricted access to biopolymers and small mols. via reproducible and directly comparable chem. processes. Current automated multistep syntheses rely on either iterative1-4 or linear processes5-9, and require compromises in terms of versatility and the use of equipment. Here we report an approach towards the automated synthesis of small mols., based on a series of continuous flow modules that are radially arranged around a central switching station. Using this approach, concise vols. can be exposed to any reaction conditions required for a desired transformation. Sequential, non-simultaneous reactions can be combined to perform multistep processes, enabling the use of variable flow rates, reuse of reactors under different conditions, and the storage of intermediates. This fully automated instrument is capable of both linear and convergent syntheses and does not require manual reconfiguration between different processes. The capabilities of this approach are demonstrated by performing optimizations and multistep syntheses of targets, varying concns. via inline dilns., exploring several strategies for the multistep synthesis of the anticonvulsant drug rufinamide10, synthesizing eighteen compds. of two deriv. libraries that are prepd. using different reaction pathways and chemistries, and using the same reagents to perform metallaphotoredox carbon-nitrogen cross-couplings11 in a photochem. module-all without instrument reconfiguration.
68. 68

    Bahr, M. N.; Damon, D. B.; Yates, S. D.; Chin, A. S.; Christopher, J. D.; Cromer, S.; Perrotto, N.; Quiroz, J.; Rosso, V. Collaborative Evaluation of Commercially Available Automated Powder Dispensing Platforms for High-Throughput Experimentation in Pharmaceutical Applications. Org. Process Res. Dev. 2018, 22, 1500– 1508,  DOI: 10.1021/acs.oprd.8b00259

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    Collaborative Evaluation of Commercially Available Automated Powder Dispensing Platforms for High-Throughput Experimentation in Pharmaceutical Applications

    Bahr, Matthew N.; Damon, David B.; Yates, Simon D.; Chin, Alexander S.; Christopher, J. David; Cromer, Samuel; Perrotto, Nicholas; Quiroz, Jorge; Rosso, Victor

    Organic Process Research & Development (2018), 22 (11), 1500-1508CODEN: OPRDFK; ISSN:1083-6160. (American Chemical Society)

    Many workflows in Pharmaceutical R&D involve the manipulation of defined amts. of powders. Automated powder dispensing platforms are currently available; however, these existing technologies do not meet the requirements for every high-throughput experimentation powder dispensing application. A Working Group (WG) composed of pharmaceutical researchers within the Enabling Technologies Consortium (ETC) evaluated automated platforms com. available from three manufacturers using an objective, systematic testing protocol. This paper describes the selection of powders and testing conditions used in this evaluation, and it assesses the impact that the powders, testing conditions, equipment environment, and other factors had on the performance of the selected platforms.
69. 69

    Bahr, M. N.; Morris, M. A.; Tu, N. P.; Nandkeolyar, A. Recent Advances in High-Throughput Automated Powder Dispensing Platforms for Pharmaceutical Applications. Org. Process Res. Dev. 2020, 24, 2752,  DOI: 10.1021/acs.oprd.0c00411

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    Recent Advances in High-Throughput Automated Powder Dispensing Platforms for Pharmaceutical Applications

    Bahr, Matthew N.; Morris, Mark A.; Tu, Noah P.; Nandkeolyar, Aakankschit

    Organic Process Research & Development (2020), 24 (11), 2752-2761CODEN: OPRDFK; ISSN:1083-6160. (American Chemical Society)

    A wide array of pharmaceutical research studies involve dispensing a variety of powders such as active ingredients, intermediates, catalysts, and formulation excipients. Automated powder dispensing platforms are increasingly relied upon to perform the mundane task of filling vials in multi-well plates for high-throughput experimentation workflows. A small group of pharmaceutical scientists collaborated to evaluate recent advances in com. available automation platforms from two instrument manufacturers using previously reported objective and systematic testing protocols. This manuscript details the testing conditions used for the evaluation and the results obtained and assesses the impact that the powder characteristics had on the performance of the selected platforms through statistical anal.
70. 70

    Tu, N. P.; Dombrowski, A. W.; Goshu, G. M.; Vasudevan, A.; Djuric, S. W.; Wang, Y. High-Throughput Reaction Screening with Nanomoles of Solid Reagents Coated on Glass Beads. Angew. Chem., Int. Ed. 2019, 58, 7987– 7991,  DOI: 10.1002/anie.201900536

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    High-Throughput Reaction Screening with Nanomoles of Solid Reagents Coated on Glass Beads

    Tu, Noah P.; Dombrowski, Amanda W.; Goshu, Gashaw M.; Vasudevan, Anil; Djuric, Stevan W.; Wang, Ying

    Angewandte Chemie, International Edition (2019), 58 (24), 7987-7991CODEN: ACIEF5; ISSN:1433-7851. (Wiley-VCH Verlag GmbH & Co. KGaA)

    Technologies that enable rapid screening of diverse reaction conditions are of crit. importance to methodol. development and reaction optimization, esp. when mols. of high complexity and scarcity are involved. The lack of a general solid dispensing method for chem. reagents on micro- and nanomole scale prevents the full use of reaction screening technologies. The authors herein report the development of a technol. in which glass beads coated with solid chem. reagents (ChemBeads) enable the delivery of nanomole quantities of solid chem. reagents efficiently. By exploring the concept of preferred screening sets, the flexibility and generality of this technol. for high-throughput reaction screening was validated.
71. 71

    Shiri, P.; Lai, V.; Zepel, T.; Griffin, D.; Reifman, J.; Clark, S.; Grunert, S.; Yunker, L. P. E.; Steiner, S.; Situ, H.; Yang, F.; Prieto, P. L.; Hein, J. E. Automated Solubility Screening Platform Using Computer Vision. iScience 2021, 24, 102176,  DOI: 10.1016/j.isci.2021.102176

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    71

    Automated solubility screening platform using computer vision

    Shiri, Parisa; Lai, Veronica; Zepel, Tara; Griffin, Daniel; Reifman, Jonathan; Clark, Sean; Grunert, Shad; Yunker, Lars P. E.; Steiner, Sebastian; Situ, Henry; Yang, Fan; Prieto, Paloma L.; Hein, Jason E.

    iScience (2021), 24 (3), 102176CODEN: ISCICE; ISSN:2589-0042. (Elsevier B.V.)

    Soly. screening is an essential, routine process that is often labor intensive. Robotic platforms have been developed to automate some aspects of the manual labor involved. However, many of the existing systems rely on traditional analytic techniques such as high-performance liq. chromatog., which require pre-calibration for each compd. and can be resource consuming. In addn., automation is not typically end-to-end, requiring user intervention to move vials, establish anal. methods for each compd. and interpret the raw data. We developed a closed-loop, flexible robotic system with integrated solid and liq. dosing capabilities that relies on computer vision and iterative feedback to successfully measure caffeine soly. in multiple solvents. After initial researcher input (<2 min), the system ran autonomously, screening five different solvent systems (20-80 min each). The resulting soly. values matched those obtained using traditional manual techniques.
72. 72

    J3016C: Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles ; 2021; https://www.sae.org/standards/content/j3016_202104.

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    Ruiz-Castillo, P.; Buchwald, S. L. Applications of Palladium-Catalyzed C–N Cross-Coupling Reactions. Chem. Rev. 2016, 116, 12564– 12649,  DOI: 10.1021/acs.chemrev.6b00512

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    Applications of Palladium-Catalyzed C-N Cross-Coupling Reactions

    Ruiz-Castillo, Paula; Buchwald, Stephen L.

    Chemical Reviews (Washington, DC, United States) (2016), 116 (19), 12564-12649CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)

    A review. Pd-catalyzed cross-coupling reactions that form C-N bonds have become useful methods to synthesize anilines and aniline derivs., an important class of compds. throughout chem. research. A key factor in the widespread adoption of these methods has been the continued development of reliable and versatile catalysts that function under operationally simple, user-friendly conditions. This review provides an overview of Pd-catalyzed N-arylation reactions found in both basic and applied chem. research from 2008 to the present. Selected examples of C-N cross-coupling reactions between nine classes of nitrogen-based coupling partners and (pseudo)aryl halides are described for the synthesis of heterocycles, medicinally relevant compds., natural products, org. materials, and catalysts.
74. 74

    Chinchilla, R.; Nájera, C. The Sonogashira Reaction: A Booming Methodology in Synthetic Organic Chemistry. Chem. Rev. 2007, 107, 874– 922,  DOI: 10.1021/cr050992x

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    The Sonogashira reaction: a booming methodology in synthetic organic chemistry

    Chinchilla, Rafael; Najera, Carmen

    Chemical Reviews (Washington, DC, United States) (2007), 107 (3), 874-922CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)

    A review. The palladium-catalyzed sp2-sp coupling reaction between aryl or alkenyl halides or triflates and terminal alkynes, with or without the presence of copper(I) cocatalyst, was reviewed. The mechanism of Sonogashira reaction was discussed, and the catalysts and reaction conditions were explored as well. In addn., the application of Sonogashira cross-coupling reaction was also introduced.
75. 75

    Breugst, M.; Reissig, H.-U. The Huisgen Reaction: Milestones of the 1,3-Dipolar Cycloaddition. Angew. Chem., Int. Ed. 2020, 59, 12293– 12307,  DOI: 10.1002/anie.202003115

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    The Huisgen Reaction: Milestones of the 1,3-Dipolar Cycloaddition

    Breugst, Martin; Reissig, Hans-Ulrich

    Angewandte Chemie, International Edition (2020), 59 (30), 12293-12307CODEN: ACIEF5; ISSN:1433-7851. (Wiley-VCH Verlag GmbH & Co. KGaA)

    A review. The concept of 1,3-dipolar cycloaddns. was presented by Rolf Huisgen 60 years ago. Previously unknown reactive intermediates, for example azomethine ylides, were introduced to org. chem. and the (3+2) cycloaddns. of 1,3-dipoles to multiple-bond systems (Huisgen reaction) developed into one of the most versatile synthetic methods in heterocyclic chem. In this Review, the authors present the history of this research area, highlight important older reports, and describe the evolution and further development of the concept. The most important mechanistic and synthetic results are discussed. Quantum-mech. calcns. support the concerted mechanism always favored by R. Huisgen; however, in extreme cases intermediates may be involved. The impact of 1,3-dipolar cycloaddns. on the click chem. concept of K. B. Sharpless will also be discussed.
76. 76

    Molga, K.; Szymkuć, S.; Gołębiowska, P.; Popik, O.; Dittwald, P.; Moskal, M.; Roszak, R.; Mlynarski, J.; Grzybowski, B. A. A Computer Algorithm to Discover Iterative Sequences of Organic Reactions. Nat. Synth. 2022, 1, 49– 58,  DOI: 10.1038/s44160-021-00010-3

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    Eppel, S.; Xu, H.; Bismuth, M.; Aspuru-Guzik, A. Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Data Set. ACS Cent. Sci. 2020, 6, 1743– 1752,  DOI: 10.1021/acscentsci.0c00460

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    Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Data Set

    Eppel, Sagi; Xu, Haoping; Bismuth, Mor; Aspuru-Guzik, Alan

    ACS Central Science (2020), 6 (10), 1743-1752CODEN: ACSCII; ISSN:2374-7951. (American Chemical Society)

    This work presents a machine learning approach for the computer vision-based recognition of materials inside vessels in the chem. lab and other settings. In addn., we release a data set assocd. with the training of the model for further model development. The task to learn is finding the region, boundaries, and category for each material phase and vessel in an image. Handling materials inside mostly transparent containers is the main activity performed by human and robotic chemists in the lab. Visual recognition of vessels and their contents is essential for performing this task. Modern machine-vision methods learn recognition tasks by using data sets contg. a large no. of annotated images. This work presents the Vector-LabPics data set, which consists of 2187 images of materials within mostly transparent vessels in a chem. lab and other general settings. The images are annotated for both the vessels and the individual material phases inside them, and each instance is assigned one or more classes (liq., solid, foam, suspension, powder, ...). The fill level, labels, corks, and parts of the vessel are also annotated. Several convolutional nets for semantic and instance segmentation were trained on this data set. The trained neural networks achieved good accuracy in detecting and segmenting vessels and material phases, and in classifying liqs. and solids, but relatively low accuracy in segmenting multiphase systems such as phase-sepg. liqs. A computer vision system for recognition materials and vessels in the chem. lab. The system is based on the new LabPics image data set and convolutional neural nets for image segmentation.
78. 78

    Steiner, S.; Wolf, J.; Glatzel, S.; Andreou, A.; Granda, J. M.; Keenan, G.; Hinkley, T.; Aragon-Camarasa, G.; Kitson, P. J.; Angelone, D.; Cronin, L. Organic Synthesis in a Modular Robotic System Driven by a Chemical Programming Language. Science 2019, 363, 1,  DOI: 10.1126/science.aav2211

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    Mehr, S. H. M.; Craven, M.; Leonov, A. I.; Keenan, G.; Cronin, L. A Universal System for Digitization and Automatic Execution of the Chemical Synthesis Literature. Science 2020, 370, 101– 108,  DOI: 10.1126/science.abc2986

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    A universal system for digitization and automatic execution of the chemical synthesis literature

    Mehr, S. Hessam M.; Craven, Matthew; Leonov, Artem I.; Keenan, Graham; Cronin, Leroy

    Science (Washington, DC, United States) (2020), 370 (6512), 101-108CODEN: SCIEAS; ISSN:1095-9203. (American Association for the Advancement of Science)

    Robotic systems for chem. synthesis are growing in popularity but can be difficult to run and maintain because of the lack of a std. operating system or capacity for direct access to the literature through natural language processing. Here we show an extendable chem. execution architecture that can be populated by automatically reading the literature, leading to a universal autonomous workflow. The robotic synthesis code can be cor. in natural language without any programming knowledge and, because of the std., is hardware independent. This chem. code can then be combined with a graph describing the hardware modules and compiled into platform-specific, low-level robotic instructions for execution. We showcase automated syntheses of 12 compds. from the literature, including the analgesic lidocaine, the Dess-Martin periodinane oxidn. reagent, and the fluorinating agent AlkylFluor.
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    Acceleration Consortium; https://acceleration.utoronto.ca (accessed 2021-06-10).

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    There is no corresponding record for this reference.