QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization

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1. 1

   Terayama, K.; Sumita, M.; Tamura, R.; Tsuda, K. Black-Box Optimization for Automated Discovery. Acc. Chem. Res. 2021, 54, 1334,  DOI: 10.1021/acs.accounts.0c00713

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   Black-Box Optimization for Automated Discovery

   Terayama, Kei; Sumita, Masato; Tamura, Ryo; Tsuda, Koji

   Accounts of Chemical Research (2021), 54 (6), 1334-1346CODEN: ACHRE4; ISSN:0001-4842. (American Chemical Society)

   In chem. and materials science, researchers and engineers discover, design, and optimize chem. compds. or materials with their professional knowledge and techniques. At the highest level of abstraction, this process is formulated as black-box optimization. For instance, the trial-and-error process of synthesizing various mols. for better material properties can be regarded as optimizing a black-box function describing the relation between a chem. formula and its properties. Various black-box optimization algorithms have been developed in the machine learning and statistics communities. Recently, a no. of researchers have reported successful applications of such algorithms to chem. They include the design of photofunctional mols. and medical drugs, optimization of thermal emission materials and high Li-ion conductive solid electrolytes, and discovery of a new phase in inorg. thin films for solar cells. There are a wide variety of algorithms available for black-box optimization, such as Bayesian optimization, reinforcement learning, and active learning. Practitioners need to select an appropriate algorithm or, in some cases, develop novel algorithms to meet their demands. It is also necessary to det. how to best combine machine learning techniques with quantum mechanics- and mol. mechanics-based simulations, and expts. In this Account, we give an overview of recent studies regarding automated discovery, design, and optimization based on black-box optimization. The Account covers the following algorithms: Bayesian optimization to optimize the chem. or phys. properties, an optimization method using a quantum annealer, best-arm identification, gray-box optimization, and reinforcement learning. In addn., we introduce active learning and boundless objective-free exploration, which may not fall into the category of black-box optimization. Data quality and quantity are key for the success of these automated discovery techniques. As lab. automation and robotics are put forward, automated discovery algorithms would be able to match human performance at least in some domains in the near future.

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2. 2

   Pollice, R.; dos Passos Gomes, G.; Aldeghi, M.; Hickman, R. J.; Krenn, M.; Lavigne, C.; Lindner-D’Addario, M.; Nigam, A.; Ser, C. T.; Yao, Z.; Aspuru-Guzik, A. Data-Driven Strategies for Accelerated Materials Design. Acc. Chem. Res. 2021, 54, 849– 860,  DOI: 10.1021/acs.accounts.0c00785

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   2

   Data-Driven Strategies for Accelerated Materials Design

   Pollice, Robert; dos Passos Gomes, Gabriel; Aldeghi, Matteo; Hickman, Riley J.; Krenn, Mario; Lavigne, Cyrille; Lindner-D'Addario, Michael; Nigam, AkshatKumar; Ser, Cher Tian; Yao, Zhenpeng; Aspuru-Guzik, Alan

   Accounts of Chemical Research (2021), 54 (4), 849-860CODEN: ACHRE4; ISSN:0001-4842. (American Chemical Society)

   A review. Conspectus: The ongoing revolution of the natural sciences by the advent of machine learning and artificial intelligence sparked significant interest in the material science community in recent years. The intrinsically high dimensionality of the space of realizable materials makes traditional approaches ineffective for large-scale explorations. Modern data science and machine learning tools developed for increasingly complicated problems are an attractive alternative. An imminent climate catastrophe calls for a clean energy transformation by overhauling current technologies within only several years of possible action available. Tackling this crisis requires the development of new materials at an unprecedented pace and scale. For example, org. photovoltaics have the potential to replace existing silicon-based materials to a large extent and open up new fields of application. In recent years, org. light-emitting diodes have emerged as state-of-the-art technol. for digital screens and portable devices and are enabling new applications with flexible displays. Reticular frameworks allow the atom-precise synthesis of nanomaterials and promise to revolutionize the field by the potential to realize multifunctional nanoparticles with applications from gas storage, gas sepn., and electrochem. energy storage to nanomedicine. In the recent decade, significant advances in all these fields have been facilitated by the comprehensive application of simulation and machine learning for property prediction, property optimization, and chem. space exploration enabled by considerable advances in computing power and algorithmic efficiency. In this Account, we review the most recent contributions of our group in this thriving field of machine learning for material science. We start with a summary of the most important material classes our group has been involved in, focusing on small mols. as org. electronic materials and cryst. materials. Specifically, we highlight the data-driven approaches we employed to speed up discovery and derive material design strategies. Subsequently, our focus lies on the data-driven methodologies our group has developed and employed, elaborating on high-throughput virtual screening, inverse mol. design, Bayesian optimization, and supervised learning. We discuss the general ideas, their working principles, and their use cases with examples of successful implementations in data-driven material discovery and design efforts. Furthermore, we elaborate on potential pitfalls and remaining challenges of these methods. Finally, we provide a brief outlook for the field as we foresee increasing adaptation and implementation of large scale data-driven approaches in material discovery and design campaigns.

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3. 3

   Snoek, J.; Larochelle, H.; Adams, R. P. Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems , 2012.

   Google Scholar

   There is no corresponding record for this reference.
4. 4

   Silver, D.; Huang, A.; Maddison, C. J.; Guez, A.; Sifre, L.; van den Driessche, G. V. D.; Schrittwieser, J.; Antonoglou, I.; Panneershelvam, V.; Lanctot, M.; Dieleman, S.; Grewe, D.; Nham, J.; Kalchbrenner, N.; Sutskever, I.; Lillicrap, T.; Leach, M.; Kavukcuoglu, K.; Graepel, T.; Hassabis, D. Mastering the Game of Go with Deep Neural Networks and Tree Search. Nature 2016, 529, 484– 489,  DOI: 10.1038/nature16961

   Google Scholar

   4

   Mastering the game of Go with deep neural networks and tree search

   Silver, David; Huang, Aja; Maddison, Chris J.; Guez, Arthur; Sifre, Laurent; van den Driessche, George; Schrittwieser, Julian; Antonoglou, Ioannis; Panneershelvam, Veda; Lanctot, Marc; Dieleman, Sander; Grewe, Dominik; Nham, John; Kalchbrenner, Nal; Sutskever, Ilya; Lillicrap, Timothy; Leach, Madeleine; Kavukcuoglu, Koray; Graepel, Thore; Hassabis, Demis

   Nature (London, United Kingdom) (2016), 529 (7587), 484-489CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)

   The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses 'value networks' to evaluate board positions and 'policy networks' to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.

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5. 5

   Wigley, P. B.; Everitt, P. J.; van den Hengel, A. V. D.; Bastian, J. W.; Sooriyabandara, M. A.; McDonald, G. D.; Hardman, K. S.; Quinlivan, C. D.; Manju, P.; Kuhn, C. C.; Petersen, I. R.; Luiten, A. N.; Hope, J. J.; Robins, N. P.; Hush, M. R. Fast Machine-learning Online Optimization of Ultra-cold-atom Experiments. Sci. Rep. 2016, 6, 25890,  DOI: 10.1038/srep25890

   Google Scholar

   5

   Fast machine-learning online optimization of ultra-cold-atom experiments

   Wigley, P. B.; Everitt, P. J.; van den Hengel, A.; Bastian, J. W.; Sooriyabandara, M. A.; McDonald, G. D.; Hardman, K. S.; Quinlivan, C. D.; Manju, P.; Kuhn, C. C. N.; Petersen, I. R.; Luiten, A. N.; Hope, J. J.; Robins, N. P.; Hush, M. R.

   Scientific Reports (2016), 6 (), 25890CODEN: SRCEC3; ISSN:2045-2322. (Nature Publishing Group)

   A review. We apply an online optimization process based on machine learning to the prodn. of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evapn. ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real expts. Through repeated machine-controlled scientific experimentation and observations our 'learner' discovers an optimal evapn. ramp for BEC prodn. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to det. which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system.

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6. 6

   Degrave, J.; Felici, F.; Buchli, J.; Neunert, M.; Tracey, B.; Carpanese, F.; Ewalds, T.; Hafner, R.; Abdolmaleki, A.; de las Casas, D.; Donner, C.; Fritz, L.; Galperti, C.; Huber, A.; Keeling, J.; Tsimpoukelli, M.; Kay, J.; Merle, A.; Moret, J. M.; Noury, S.; Pesamosca, F.; Pfau, D.; Sauter, O.; Sommariva, C.; Coda, S.; Duval, B.; Fasoli, A.; Kohli, P.; Kavukcuoglu, K.; Hassabis, D.; Riedmiller, M. Magnetic Control of Tokamak Plasmas through Deep Reinforcement Learning. Nature 2022, 602, 414– 419,  DOI: 10.1038/s41586-021-04301-9

   Google Scholar

   6

   Magnetic control of tokamak plasmas through deep reinforcement learning

   Degrave, Jonas; Felici, Federico; Buchli, Jonas; Neunert, Michael; Tracey, Brendan; Carpanese, Francesco; Ewalds, Timo; Hafner, Roland; Abdolmaleki, Abbas; de las Casas, Diego; Donner, Craig; Fritz, Leslie; Galperti, Cristian; Huber, Andrea; Keeling, James; Tsimpoukelli, Maria; Kay, Jackie; Merle, Antoine; Moret, Jean-Marc; Noury, Seb; Pesamosca, Federico; Pfau, David; Sauter, Olivier; Sommariva, Cristian; Coda, Stefano; Duval, Basil; Fasoli, Ambrogio; Kohli, Pushmeet; Kavukcuoglu, Koray; Hassabis, Demis; Riedmiller, Martin

   Nature (London, United Kingdom) (2022), 602 (7897), 414-419CODEN: NATUAS; ISSN:1476-4687. (Nature Portfolio)

   Abstr.: Nuclear fusion using magnetic confinement, in particular in the tokamak configuration, is a promising path towards sustainable energy. A core challenge is to shape and maintain a high-temp. plasma within the tokamak vessel. This requires high-dimensional, high-frequency, closed-loop control using magnetic actuator coils, further complicated by the diverse requirements across a wide range of plasma configurations. In this work, we introduce a previously undescribed architecture for tokamak magnetic controller design that autonomously learns to command the full set of control coils. This architecture meets control objectives specified at a high level, at the same time satisfying phys. and operational constraints. This approach has unprecedented flexibility and generality in problem specification and yields a notable redn. in design effort to produce new plasma configurations. We successfully produce and control a diverse set of plasma configurations on the Tokamak a´ Configuration Variable1,2, including elongated, conventional shapes, as well as advanced configurations, such as neg. triangularity and 'snowflake' configurations. Our approach achieves accurate tracking of the location, current and shape for these configurations. We also demonstrate sustained 'droplets' on TCV, in which two sep. plasmas are maintained simultaneously within the vessel. This represents a notable advance for tokamak feedback control, showing the potential of reinforcement learning to accelerate research in the fusion domain, and is one of the most challenging real-world systems to which reinforcement learning has been applied.

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7. 7

   Ren, F.; Ward, L.; Williams, T.; Laws, K. J.; Wolverton, C.; Hattrick-Simpers, J.; Mehta, A. Accelerated Discovery of Metallic Glasses through Iteration of Machine Learning and High-throughput Experiments. Sci. Adv. 2018, 4, eaaq1566  DOI: 10.1126/sciadv.aaq1566

   Google Scholar

   There is no corresponding record for this reference.
8. 8

   Homma, K.; Liu, Y.; Sumita, M.; Tamura, R.; Fushimi, N.; Iwata, J.; Tsuda, K.; Kaneta, C. Optimization of a Heterogeneous Ternary Li₃ PO₄–Li₃BO₃–Li₂SO₄ Mixture for Li-Ion Conductivity by Machine Learning. J. Phys. Chem. C 2020, 124, 12865– 12870,  DOI: 10.1021/acs.jpcc.9b11654

   Google Scholar

   8

   Optimization of a Heterogeneous Ternary Li3PO4-Li3BO3-Li2SO4 Mixture for Li-Ion Conductivity by Machine Learning

   Homma, Kenji; Liu, Yu; Sumita, Masato; Tamura, Ryo; Fushimi, Naoki; Iwata, Junichi; Tsuda, Koji; Kaneta, Chioko

   Journal of Physical Chemistry C (2020), 124 (24), 12865-12870CODEN: JPCCCK; ISSN:1932-7447. (American Chemical Society)

   Mixing heterogeneous Li-ion conductive materials is one potential way to enhance Li-ion cond. more than that of the parent materials. However, the huge no. of possible compns. of parent materials impedes the development of an optimal mixt. by using conventional methods. In this study, we employed machine learning to optimize the compn. ratio of ternary Li3PO4-Li3BO3-Li2SO4 for Li-ion cond. We found the optimum compn. of the ternary mixt. system to be 25:14:61 (Li3PO4:Li3BO3:Li2SO4 in mol %), whose Li-ion cond. is measured as 4.9 x 10-4 S/cm at 300°C. Our X-ray structure anal. suggested that Li-ion cond. of the mixed systems tends to be enhanced by the coexistence of two or more phases. Although the mechanism enhancing Li-ion cond. is not simple, our results demonstrate the effectiveness of machine learning for the development of materials.

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9. 9

   Sumita, M.; Tamura, R.; Homma, K.; Kaneta, K.; Tsuda, K. Li-Ion Conductive Li₃PO₄-Li₃BO₃-Li₂SO₄ Mixture :Prevision through Density Functional Molecular Dynamics and Machine Learning. Bull. Chem. Soc. Jpn. 2019, 92, 1100– 1106,  DOI: 10.1246/bcsj.20190041

   Google Scholar

   9

   Li-ion conductive Li3PO4-Li3BO3-Li2SO4 mixture: prevision through density functional molecular dynamics and machine learning

   Sumita, Masato; Tamura, Ryo; Homma, Kenji; Kaneta, Chioko; Tsuda, Koji

   Bulletin of the Chemical Society of Japan (2019), 92 (6), 1100-1106CODEN: BCSJA8; ISSN:0009-2673. (Chemical Society of Japan)

   The development of high Li-ion conductive solid electrolytes is crucial for the practical use of all solid-state Li-ion batteries. The mixing of hetero Li-ion conductive substances is a known method for enhancing the Li-ion cond. more than in the original substances. In this study, using computer simulations, we proved that a ternary Li3PO4-Li3BO3-Li2SO4 system has the potential to indicate improved Li-ion cond. based on the introduction of a pseudo-Li-ion/oxygen vacancy. The Li-ion conductivities of this ternary system were calcd. using several model systems based on the d. functional mol. dynamics under an isothermal-isobaric ensemble. However, an exploration using the d. functional mol. dynamics cannot cover the entire combinatorial space owing to a lack of computational capability. To search through a vast combinatorial space, we conducted analyses using a machine learning technique. The anal. results clarify the relationship between Li-ion cond. and phonon free energy, and allow the optimum compn. ratio with the highest Li-ion cond. to be predicted.

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10. 10

    Nicolaou, K. C. Organic Synthesis: the Art and Science of Replicating the Molecules of Living Nature and Creating Others like Them in the Laboratory. Proc. Math. Phys. Eng. Sci. 2014, 470, 20130690,  DOI: 10.1098/rspa.2013.0690

    Google Scholar

    10

    Organic synthesis: the art and science of replicating the molecules of living nature and creating others like them in the laboratory

    Nicolaou, K. C.

    Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences (2014), 470 (2163), 20130690/1-20130690/17CODEN: PRSAC4 ISSN:. (Royal Society)

    A review. This article focuses on recent advances in the field of org. synthesis with demonstrative examples of total synthesis of complex bioactive mols., natural or designed, from the author's labs., and their impact on chem., biol. and medicine.

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    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXmt1Srsbg%253D&md5=2c113a4c98ceb83d23c7ee217eda2d67
11. 11

    Szabo, A.; Ostlund, N. S. Modern Quantum Chemistry; Dover Publications, Inc. Mineola: New York, 1989.

    Google Scholar

    There is no corresponding record for this reference.
12. 12

    Lowe, J. P. Quantum Chemistry; Academic press: London, 1993.

    Google Scholar

    There is no corresponding record for this reference.
13. 13

    Friesner, R. A. Ab Initio Quantum Chemistry: Methodology and Applications. Proc. Natl. Acad. Sci. U.S.A. 2005, 102, 6648– 6653,  DOI: 10.1073/pnas.0408036102

    Google Scholar

    13

    Ab initio quantum chemistry: Methodology and applications

    Friesner, Richard A.

    Proceedings of the National Academy of Sciences of the United States of America (2005), 102 (19), 6648-6653CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)

    A review. This Perspective provides an overview of state-of-the-art ab initio quantum chem. methodol. and applications. The methods that are discussed include coupled cluster theory, localized second-order Moller-Plesset perturbation theory, multireference perturbation approaches, and d. functional theory. The accuracy of each approach for key chem. properties is summarized, and the computational performance is analyzed, emphasizing significant advances in algorithms and implementation over the past decade. Incorporation of a condensed-phase environment by means of mixed quantum mech./mol. mechanics or self-consistent reaction field techniques, is presented. A wide range of illustrative applications, focusing on materials science and biol., are discussed briefly.

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14. 14

    Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Petersson, G. A.; Nakatsuji, H.; Li, X.; Caricato, M.; Marenich, A. V.; Bloino, J.; Janesko, B. G.; Gomperts, R.; Mennucci, B.; Hratchian, H. P.; Ortiz, J. V.; Izmaylov, A. F.; Sonnenberg, J. L.; Williams-Young, D.; Ding, F.; Lipparini, F.; Egidi, F.; Goings, J.; Peng, B.; Petrone, A.; Henderson, T.; Ranasinghe, D.; Zakrzewski, V. G.; Gao, J.; Rega, N.; Zheng, G.; Liang, W.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Vreven, T.; Throssell, K.; Montgomery, J. A., Jr.; Peralta, J. E.; Ogliaro, F.; Bearpark, M. J.; Heyd, J. J.; Brothers, E. N.; Kudin, K. N.; Staroverov, V. N.; Keith, T. A.; Kobayashi, R.; Normand, J.; Raghavachari, K.; Rendell, A. P.; Burant, J. C.; Iyengar, S. S.; Tomasi, J.; Cossi, M.; Millam, J. M.; Klene, M.; Adamo, C.; Cammi, R.; Ochterski, J. W.; Martin, R. L.; Morokuma, K.; Farkas, O.; Foresman, J. B.; Fox, D. J. Gaussian 16, Revision C.01; Gaussian Inc.: Wallingford CT, 2016.

    Google Scholar

    There is no corresponding record for this reference.
15. 15

    Barca, G. M. J.; Bertoni, C.; Carrington, L.; Datta, D.; De Silva, N.; Deustua, J. E.; Fedorov, D. G.; Gour, J. R.; Gunina, A. O.; Guidez, E.; Harville, T.; Irle, S.; Ivanic, J.; Kowalski, K.; Leang, S. S.; Li, H.; Li, W.; Lutz, J. J.; Magoulas, I.; Mato, J.; Mironov, V.; Nakata, H.; Pham, B. Q.; Piecuch, P.; Poole, D.; Pruitt, S. R.; Rendell, A. P.; Roskop, L. B.; Ruedenberg, K.; Sattasathuchana, T.; Schmidt, M. W.; Shen, J.; Slipchenko, L.; Sosonkina, M.; Sundriyal, V.; Tiwari, A.; Galvez Vallejo, J. L.; Westheimer, B.; Włoch, M.; Xu, P.; Zahariev, F.; Gordon, M. S. Recent Developments in the General Atomic and Molecular Electronic Structure System. J. Chem. Phys. 2020, 152, 154102,  DOI: 10.1063/5.0005188

    Google Scholar

    15

    Recent developments in the general atomic and molecular electronic structure system

    Barca, Giuseppe M. J.; Bertoni, Colleen; Carrington, Laura; Datta, Dipayan; De Silva, Nuwan; Deustua, J. Emiliano; Fedorov, Dmitri G.; Gour, Jeffrey R.; Gunina, Anastasia O.; Guidez, Emilie; Harville, Taylor; Irle, Stephan; Ivanic, Joe; Kowalski, Karol; Leang, Sarom S.; Li, Hui; Li, Wei; Lutz, Jesse J.; Magoulas, Ilias; Mato, Joani; Mironov, Vladimir; Nakata, Hiroya; Pham, Buu Q.; Piecuch, Piotr; Poole, David; Pruitt, Spencer R.; Rendell, Alistair P.; Roskop, Luke B.; Ruedenberg, Klaus; Sattasathuchana, Tosaporn; Schmidt, Michael W.; Shen, Jun; Slipchenko, Lyudmila; Sosonkina, Masha; Sundriyal, Vaibhav; Tiwari, Ananta; Galvez Vallejo, Jorge L.; Westheimer, Bryce; Wloch, Marta; Xu, Peng; Zahariev, Federico; Gordon, Mark S.

    Journal of Chemical Physics (2020), 152 (15), 154102CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)

    A discussion of many of the recently implemented features of GAMESS (General Atomic and Mol. Electronic Structure System) and LibCChem (the C + + CPU/GPU library assocd. with GAMESS) is presented. These features include fragmentation methods such as the fragment MO, effective fragment potential and effective fragment MO methods, hybrid MPI/OpenMP approaches to Hartree-Fock, and resoln. of the identity second order perturbation theory. Many new coupled cluster theory methods have been implemented in GAMESS, as have multiple levels of d. functional/tight binding theory. The role of accelerators, esp. graphical processing units, is discussed in the context of the new features of LibCChem, as it is the assocd. problem of power consumption as the power of computers increases dramatically. The process by which a complex program suite such as GAMESS is maintained and developed is considered. Future developments are briefly summarized. (c) 2020 American Institute of Physics.

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16. 16

    Aprà, E.; Bylaska, E. J.; de Jong, W. A.; Govind, N.; Kowalski, K.; Straatsma, T. P.; Valiev, M.; van Dam, H. J. J.; Alexeev, Y.; Anchell, J.; Anisimov, V.; Aquino, F. W.; Atta-Fynn, R.; Autschbach, J.; Bauman, N. P.; Becca, J. C.; Bernholdt, D. E.; Bhaskaran-Nair, K.; Bogatko, S.; Borowski, P.; Boschen, J.; Brabec, J.; Bruner, A.; Cauët, E.; Chen, Y.; Chuev, G. N.; Cramer, C. J.; Daily, J.; Deegan, M. J. O.; Dunning, T. H.; Dupuis, M.; Dyall, K. G.; Fann, G. I.; Fischer, S. A.; Fonari, A.; Früchtl, H.; Gagliardi, L.; Garza, J.; Gawande, N.; Ghosh, S.; Glaesemann, K.; Götz, A. W.; Hammond, J.; Helms, V.; Hermes, E. D.; Hirao, K.; Hirata, S.; Jacquelin, M.; Jensen, L.; Johnson, B. G.; Jónsson, H.; Kendall, R. A.; Klemm, M.; Kobayashi, R.; Konkov, V.; Krishnamoorthy, S.; Krishnan, M.; Lin, Z.; Lins, R. D.; Littlefield, R. J.; Logsdail, A. J.; Lopata, K.; Ma, W.; Marenich, A. V.; Martin del Campo, J.; Mejia-Rodriguez, D.; Moore, J. E.; Mullin, J. M.; Nakajima, T.; Nascimento, D. R.; Nichols, J. A.; Nichols, P. J.; Nieplocha, J.; Otero-de-la-Roza, A.; Palmer, B.; Panyala, A.; Pirojsirikul, T.; Peng, B.; Peverati, R.; Pittner, J.; Pollack, L.; Richard, R. M.; Sadayappan, P.; Schatz, G. C.; Shelton, W. A.; Silverstein, D. W.; Smith, D. M. A.; Soares, T. A.; Song, D.; Swart, M.; Taylor, H. L.; Thomas, G. S.; Tipparaju, V.; Truhlar, D. G.; Tsemekhman, K.; Van Voorhis, T.; Vázquez-Mayagoitia, A.; Verma, P.; Villa, O.; Vishnu, A.; Vogiatzis, K. D.; Wang, D.; Weare, J. H.; Williamson, M. J.; Windus, T. L.; Woliński, K.; Wong, A. T.; Wu, Q.; Yang, C.; Yu, Q.; Zacharias, M.; Zhang, Z.; Zhao, Y.; Harrison, R. NWChem: Past, Present, and Future. J. Chem. Phys. 2020, 152, 184102,  DOI: 10.1063/5.0004997

    Google Scholar

    16

    NWChem: Past, present, and future

    Apra, E.; Bylaska, E. J.; de Jong, W. A.; Govind, N.; Kowalski, K.; Straatsma, T. P.; Valiev, M.; van Dam, H. J. J.; Alexeev, Y.; Anchell, J.; Anisimov, V.; Aquino, F. W.; Atta-Fynn, R.; Autschbach, J.; Bauman, N. P.; Becca, J. C.; Bernholdt, D. E.; Bhaskaran-Nair, K.; Bogatko, S.; Borowski, P.; Boschen, J.; Brabec, J.; Bruner, A.; Cauet, E.; Chen, Y.; Chuev, G. N.; Cramer, C. J.; Daily, J.; Deegan, M. J. O.; Dunning, T. H.; Dupuis, M.; Dyall, K. G.; Fann, G. I.; Fischer, S. A.; Fonari, A.; Fruchtl, H.; Gagliardi, L.; Garza, J.; Gawande, N.; Ghosh, S.; Glaesemann, K.; Gotz, A. W.; Hammond, J.; Helms, V.; Hermes, E. D.; Hirao, K.; Hirata, S.; Jacquelin, M.; Jensen, L.; Johnson, B. G.; Jonsson, H.; Kendall, R. A.; Klemm, M.; Kobayashi, R.; Konkov, V.; Krishnamoorthy, S.; Krishnan, M.; Lin, Z.; Lins, R. D.; Littlefield, R. J.; Logsdail, A. J.; Lopata, K.; Ma, W.; Marenich, A. V.; Martin del Campo, J.; Mejia-Rodriguez, D.; Moore, J. E.; Mullin, J. M.; Nakajima, T.; Nascimento, D. R.; Nichols, J. A.; Nichols, P. J.; Nieplocha, J.; Otero-de-la-Roza, A.; Palmer, B.; Panyala, A.; Pirojsirikul, T.; Peng, B.; Peverati, R.; Pittner, J.; Pollack, L.; Richard, R. M.; Sadayappan, P.; Schatz, G. C.; Shelton, W. A.; Silverstein, D. W.; Smith, D. M. A.; Soares, T. A.; Song, D.; Swart, M.; Taylor, H. L.; Thomas, G. S.; Tipparaju, V.; Truhlar, D. G.; Tsemekhman, K.; Van Voorhis, T.; Vazquez-Mayagoitia, A.; Verma, P.; Villa, O.; Vishnu, A.; Vogiatzis, K. D.; Wang, D.; Weare, J. H.; Williamson, M. J.; Windus, T. L.; Wolinski, K.; Wong, A. T.; Wu, Q.; Yang, C.; Yu, Q.; Zacharias, M.; Zhang, Z.; Zhao, Y.; Harrison, R. J.

    Journal of Chemical Physics (2020), 152 (18), 184102CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)

    A review. Specialized computational chem. packages have permanently reshaped the landscape of chem. and materials science by providing tools to support and guide exptl. efforts and for the prediction of atomistic and electronic properties. In this regard, electronic structure packages have played a special role by using first-principle-driven methodologies to model complex chem. and materials processes. Over the past few decades, the rapid development of computing technologies and the tremendous increase in computational power have offered a unique chance to study complex transformations using sophisticated and predictive many-body techniques that describe correlated behavior of electrons in mol. and condensed phase systems at different levels of theory. In enabling these simulations, novel parallel algorithms have been able to take advantage of computational resources to address the polynomial scaling of electronic structure methods. In this paper, we briefly review the NWChem computational chem. suite, including its history, design principles, parallel tools, current capabilities, outreach, and outlook. (c) 2020 American Institute of Physics.

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    Grimme, S.; Schreiner, P. R. Computational Chemistry: The Fate of Current Methods and Future Challenges. Angew. Chem., Int. Ed. 2018, 57, 4170– 4176,  DOI: 10.1002/anie.201709943

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    17

    Computational Chemistry: The Fate of Current Methods and Future Challenges

    Grimme, Stefan; Schreiner, Peter R.

    Angewandte Chemie, International Edition (2018), 57 (16), 4170-4176CODEN: ACIEF5; ISSN:1433-7851. (Wiley-VCH Verlag GmbH & Co. KGaA)

    In this essay, we attempt to make predictions about the fate and development of the computational mol. sciences. Of course, it is not the first time that the future challenges for computational org. chem. and biochem. are considered; these were outlined in complementary contexts recently. In this article, the authors take a somewhat different perspective and emphasize the changes expected for chem. that are triggered by the rapid developments and increasingly stronger influences from theory, algorithms, and data-driven technologies. The authors of this essay are about the same age and have a general overview of a period of about 25 years during which they have actively contributed to the field of computational chem. Hence, it appears sensible to make predictions extending 25 years into the future, roughly to the year 2043 (when both authors will long be retired).

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18. 18

    Sumiya, M.; Sumita, Y.; Tsuda, M.; Sakamoto, Y.; Sang, T.; Harada, L.; Yoshigoe, A. Y. High Reactivity of H₂O Vapor on GaN Surfaces. Sci. Technol. Adv. Mater. 2022, 23, 189– 198,  DOI: 10.1080/14686996.2022.2052180

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    18

    High reactivity of H2O vapor on GaN surfaces

    Sumiya, Masatomo; Sumita, Masato; Tsuda, Yasutaka; Sakamoto, Tetsuya; Sang, Liwen; Harada, Yoshitomo; Yoshigoe, Akitaka

    Science and Technology of Advanced Materials (2022), 23 (1), 189-198CODEN: STAMCV; ISSN:1878-5514. (Taylor & Francis Ltd.)

    Understanding the process of oxidn. on the surface of GaN is important for improving metal-oxide-semiconductor (MOS) devices. Real-time XPS was used to observe the dynamic adsorption behavior of GaN surfaces upon irradn. of H2O, O2, N2O, and NO gases. It was found that H2O vapor has the highest reactivity on the surface despite its lower oxidn. power. The adsorption behavior of H2O was explained by the d. functional mol. dynamic calcn. including the spin state of the surfaces. Two types of adsorbed H2O mols. were present on the (0001) (+c) surface: non-dissociatively adsorbed H2O (physisorption), and dissociatively adsorbed H2O (chemisorption) mols. that were dissocd. with OH and H adsorbed on Ga atoms. H2O mols. attacked the back side of three-fold Ga atoms on the (0001) (-c) GaN surface, and the bond length between the Ga and N was broken. The chemisorption on the (1010) m-plane of GaN, which is the channel of a trench-type GaN MOS power transistor, was dominant, and a stable Ga-O bond was formed due to the elongated bond length of Ga on the surface. In the at. layer deposition process of the Al2O3 layer using H2O vapor, the reactions caused at the interface were more remarkable for p-GaN. If unintentional oxidn. can be resulted in the generation of the defects at the MOS interface, these results suggest that oxidant gases other than H2O and O2 should be used to avoid uncontrollable oxidn. on GaN surfaces.

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19. 19

    Sumita, M.; Tanaka, Y.; Ohno, T. Possible Polymerization of PS₄ at a Li₃PS₄/FePO₄ Interface with Reduction of the FePO₄ Phase. J. Phys. Chem. C 2017, 121, 9698– 9704,  DOI: 10.1021/acs.jpcc.7b01009

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    19

    Possible Polymerization of PS4 at a Li3PS4/FePO4 Interface with Reduction of the FePO4 Phase

    Sumita, Masato; Tanaka, Yoshinori; Ohno, Takahisa

    Journal of Physical Chemistry C (2017), 121 (18), 9698-9704CODEN: JPCCCK; ISSN:1932-7447. (American Chemical Society)

    An important issue about developing all solid-state Li-ion batteries is to lower the high ionic interfacial resistance between a cathode and an electrolyte. An origin of the interfacial resistance is hypothesized due to a Li-depleted layer at the interface. Our computation has shown that the Li-depleted layer was the result of redox reaction at the interface in the charging process. In this subsequent theor. study, we validate this redox reaction between the FePO4 phase and the Li3PS4 phase from the viewpoint of their band alignment through the d. functional theory with the hybrid functional (HSE06). In addn., we demonstrate that the Li-depleted layer grows up to a defective layer at a Li3PS4/FePO4 interface by exothermic radical polymn. of PS4 anions in the oxidized Li3PS4 phase with the vol. redn. This decrease in Li-ion sites due to the PS4 polymn. makes the Li-depleted region long-lived and has the potential as an origin of the resistance against the Li-ion diffusion near the interface.

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20. 20

    Sumita, M.; Tanaka, Y.; Ikeda, M.; Ohno, T. Charged and Discharged States of Cathode/Sulfide-Electrolyte Interfaces in All-Solid-State Lithium-Ion Batteries. J. Phys. Chem. C 2016, 120, 13332– 13339,  DOI: 10.1021/acs.jpcc.6b01207

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    20

    Charged and Discharged States of Cathode/Sulfide Electrolyte Interfaces in All-Solid-State Lithium Ion Batteries

    Sumita, Masato; Tanaka, Yoshinori; Ikeda, Minoru; Ohno, Takahisa

    Journal of Physical Chemistry C (2016), 120 (25), 13332-13339CODEN: JPCCCK; ISSN:1932-7447. (American Chemical Society)

    Interfaces between cathodes and sulfide electrolytes exhibit high resistance in all-solid-state lithium ion batteries. In this paper, to elucidate the origin of the high interface resistance we have theor. investigated the properties of the cathode interfaces with the sulfide electrolyte and oxide electrolyte for comparison. From the d. functional mol. dynamics simulations of the LiFePO4/Li3PS4 interface in both discharged and charged states, we have demonstrated the instability of the sulfide interface in the charged state, i.e., the lithium depletion and oxidn. on the sulfide side near the interface, in contrast to the oxide interfaces. The obtained results imply the formation of a Li-depleted layer around the sulfide interfaces during charging and support the validity of the insertion of oxide buffer layers at the interface to reduce the interface resistance.

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21. 21

    Sumita, M.; Morihashi, K. Theoretical Study of Singlet Oxygen Molecule Generation via an Exciplex with Valence-Excited Thiophene. J. Phys. Chem. A 2015, 119, 876– 883,  DOI: 10.1021/jp5123129

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    21

    Theoretical Study of Singlet Oxygen Molecule Generation via an Exciplex with Valence-Excited Thiophene

    Sumita, Masato; Morihashi, Kenji

    Journal of Physical Chemistry A (2015), 119 (5), 876-883CODEN: JPCAFH; ISSN:1089-5639. (American Chemical Society)

    Singlet-oxygen [O2(1Δg)] generation by valence-excited thiophene (TPH) has been investigated using multireference Moller-Plesset second-order perturbation (MRMP2) theory of geometries optimized at the complete active space SCF (CASSCF) theory level. The results indicate that triplet TPH(13B2) is produced via photoinduced singlet TPH(21A1) because 21A1 TPH shows a large spin-orbit coupling const. with the first triplet excited state (13B2). The relaxed TPH in the 13B2 state can form an exciplex with O2(3Σg-) because this exciplex is energetically more stable than the relaxed TPH. The formation of the TPH(13B2) exciplex with O2(3Σg-) whose total spin multiplicity is triplet (T1 state) increases the likelihood of transition from the T1 state to the singlet ground or first excited singlet state. After the transition, O2(1Δg) is emitted easily although the favorable product is that from a 2 + 4 cycloaddn. reaction.

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22. 22

    Sumita, M.; Ryazantsev, N.; Saito, K. Acceleration of the Z to E Photoisomerization of Penta-2, 4-dieniminium by Hydrogen Out-of-plane Motion : Theoretical Study on a Model System of Retinal Protonated Schiff Base. Phys. Chem. Chem. Phys. 2009, 11, 6406– 6414,  DOI: 10.1039/b900882a

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    22

    Acceleration of the Z to E photoisomerization of penta-2,4-dieniminium by hydrogen out-of-plane motion: theoretical study on a model system of retinal protonated Schiff base

    Sumita, Masato; Ryazantsev, Mikhail N.; Saito, Kazuya

    Physical Chemistry Chemical Physics (2009), 11 (30), 6406-6414CODEN: PPCPFQ; ISSN:1463-9076. (Royal Society of Chemistry)

    We report the result of comparison between two reaction coordinates [on the potential energy surface of the first excited state (S1)] produced by CASSCF and these energies recalcd. by MRMP2 in the Z to E photoisomerization of penta-2,4-dieniminium (PDI) as the minimal model of the retinal protonated Schiff base (RPSB). One coordinate is the S1 state min.-energy-path (MEP) in mass-weighted coordinates from the S1 vertically excited point, where a strong hydrogen-out-of plane (HOOP) motion is not exhibited. The energy profile of the S1 MEP at the MRMP2//CASSCF level shows a barrier for the rotation around the reactive C-C and hits the S1/S0 degeneracy space where the central C-C-C-C dihedral angle is distorted by 65°. The other coordinate is an S1 coordinate obtained by the relaxed scan strategy. The relaxed coordinate along the central C-C-C-C dihedral angle, which we call the HOOP coordinate, shows strong HOOP motion. According to the MRMP2//CASSCF calcn., there is no barrier on the HOOP coordinate. Furthermore, the S1 to S0 transition may be possible without the large skeletal deformation by HOOP motion because the HOOP coordinate encounters the S1/S0 degeneracy space where the central C-C-C-C dihedral angle is distorted by only 40°. Consequently, if PDI is a suitable model mol. for the RPSB as often assumed, the 11-cis to all-trans photoisomerization is predicted to be accelerated by the HOOP motion.

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23. 23

    Sumita, M.; Saito, K. Ab initio Study on One-way Photoisomerization of the Maleic Acid and Fumaric Acid Anion Radical System as a Model System of Their Esters. J. Phys. Chem. A 2006, 110, 12276– 12281,  DOI: 10.1021/jp064377o

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    23

    Ab Initio Study on One-Way Photoisomerization of the Maleic Acid and Fumaric Acid Anion Radical System as a Model System of Their Esters

    Sumita, Masato; Saito, Kazuya

    Journal of Physical Chemistry A (2006), 110 (44), 12276-12281CODEN: JPCAFH; ISSN:1089-5639. (American Chemical Society)

    Potential energy surfaces (PESs) of the maleic acid anion radical (MA-•: cis isomer)/fumaric acid anion radical (FA-•: trans isomer) system as a model system of their esters have been studied in detail using CASSCF method. The results suggest the following: The photoisomerization is initiated with the H-C-C-H dihedral angle distortion [hydrogen out of plain (HOOP) motion] on the D1 PES. The C-C-C-C dihedral angle distortion occurs on the D0 PES after the deactivation from D1 to D0. A large fraction of the net motion along the isomerization coordinate occurs on the D0 PES. The D0 state is responsible for the one-way nature of the photoisomerization.

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24. 24

    Sumita, M.; Yoshikawa, N. Augmented Lagrangian Method for Spin-coupled Wave Function. Int. J. Quantum Chem. 2021, 121, e26746  DOI: 10.1002/qua.26746

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    24

    Augmented Lagrangian method for spin-coupled wave function

    Sumita, Masato; Yoshikawa, Naruki

    International Journal of Quantum Chemistry (2021), 121 (18), e26746CODEN: IJQCB2; ISSN:0020-7608. (John Wiley & Sons, Inc.)

    We applied augmented Lagrangian method coupled with deriv.-free methods to optimize mol. wave function based on non-orthogonal orbitals, that is called spin-coupled generalized valence bond (SCGVB), for its ground-state energy. In contrast to the orthogonal-orbital-based electronic structure theory, the SCGVB includes spin eigenfunctions to satisfy the eigenstates as the operator of the square of the spin. To obtain the ground-state energy of SCGVB, therefore, it is necessary to optimize the orbital and the spin-coupling coeffs. simultaneously. In this study, we validated feasibility of the deriv.-free augmented Lagrangian method for optimizing the spin-coupling and the orbital coeffs. with the constraint of normality of the wave function. We employed this SCGVB method to compute dissociative potential energy curves (PECs) of H2, H2-, He2+, and LiH. The obtained PECs by the SCGVB method are close to these by full CI theory. These results indicate that the augmented Lagrangian method is effective to optimize the wave function of SCGVB.

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25. 25

    O’Boyle, N. M.; Tenderholt, A. L.; Langner, K. M. cclib: A Library for Package-Independent Computational Chemistry Algorithms. J. Comput. Chem. 2008, 29, 839– 845,  DOI: 10.1002/jcc.20823

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    25

    Software news and updates cclib: a library for package-independent computational chemistry algorithms

    O'Boyle, Noel M.; Tenderholt, Adam L.; Langner, Karol M.

    Journal of Computational Chemistry (2008), 29 (5), 839-845CODEN: JCCHDD; ISSN:0192-8651. (John Wiley & Sons, Inc.)

    There are now a wide variety of packages for electronic structure calcns., each of which differs in the algorithms implemented and the output format. Many computational chem. algorithms are only available to users of a particular package despite being generally applicable to the results of calcns. by any package. Here we present cclib, a platform for the development of package-independent computational chem. algorithms. Files from several versions of multiple electronic structure packages are automatically detected, parsed, and the extd. information converted to a std. internal representation. A no. of population anal. algorithms have been implemented as a proof of principle. In addn., cclib is currently used as an input filter for two GUI applications that analyze output files: PyMOlyze and GaussSum.

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26. 26

    Larsen, A. H.; Mortensen, J. J.; Blomqvist, J.; Castelli, I. E.; Christensen, R.; Dułak, M.; Friis, J.; Groves, M. N.; Hammer, B.; Hargus, C.; Hermes, E. D.; Jennings, P. C.; Jensen, P. B.; Kermode, J.; Kitchin, J. R.; Kolsbjerg, E. L.; Kubal, J.; Kaasbjerg, K.; Lysgaard, S.; Maronsson, J. B.; Maxson, T.; Olsen, T.; Pastewka, L.; Peterson, A.; Rostgaard, C.; Schiøtz, J.; Schütt, O.; Strange, M.; Thygesen, K. S.; Vegge, T.; Vilhelmsen, L.; Walter, M.; Zeng, Z.; Jacobsen, K. W. The Atomic Simulation Environment─a Python Library for Working with Atoms. J. Phys. Condens. Matter 2017, 29, 273002,  DOI: 10.1088/1361-648x/aa680e

    Google Scholar

    26

    The atomic simulation environment - a Python library for working with atoms

    Larsen, Ask Hjorth; Mortensen, Jens Joergen; Blomqvist, Jakob; Castelli, Ivano E.; Christensen, Rune; Dulak, Marcin; Friis, Jesper; Groves, Michael N.; Hammer, Bjoerk; Hargus, Cory; Hermes, Eric D.; Jennings, Paul C.; Jensen, Peter Bjerre; Kermode, James; Kitchin, John R.; Kolsbjerg, Esben Leonhard; Kubal, Joseph; Kaasbjerg, Kristen; Lysgaard, Steen; Maronsson, Jon Bergmann; Maxson, Tristan; Olsen, Thomas; Pastewka, Lars; Peterson, Andrew; Rostgaard, Carsten; Schioetz, Jakob; Schutt, Ole; Strange, Mikkel; Thygesen, Kristian S.; Vegge, Tejs; Vilhelmsen, Lasse; Walter, Michael; Zeng, Zhenhua; Jacobsen, Karsten W.

    Journal of Physics: Condensed Matter (2017), 29 (27), 273002/1-273002/30CODEN: JCOMEL; ISSN:0953-8984. (IOP Publishing Ltd.)

    A review. The at. simulation environment (ASE) is a software package written in the Python programming language with the aim of setting up, steering, and analyzing atomistic simulations. In ASE, tasks are fully scripted in Python. The powerful syntax of Python combined with the NumPy array library make it possible to perform very complex simulation tasks. For example, a sequence of calcns. may be performed with the use of a simple 'for-loop' construction. Calcns. of energy, forces, stresses and other quantities are performed through interfaces to many external electronic structure codes or force fields using a uniform interface. On top of this calculator interface, ASE provides modules for performing many std. simulation tasks such as structure optimization, mol. dynamics, handling of constraints and performing nudged elastic band calcns.

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27. 27

    Hruska, E.; Gale, A.; Huang, X.; Liu, F. AutoSolvate A toolkit for Automating Quantum Chemistry Design and Discovery of Solvated Molecules. J. Chem. Phys. 2022, 156, 124801,  DOI: 10.1063/5.0084833

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    27

    AutoSolvate: A toolkit for automating quantum chemistry design and discovery of solvated molecules

    Hruska, Eugen; Gale, Ariel; Huang, Xiao; Liu, Fang

    Journal of Chemical Physics (2022), 156 (12), 124801CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)

    The availability of large, high-quality datasets is crucial for artificial intelligence design and discovery in chem. Despite the essential roles of solvents in chem., the rapid computational dataset generation of soln.-phase mol. properties at the quantum mech. level of theory was previously hampered by the complicated simulation procedure. Software toolkits that can automate the procedure to set up high-throughput explicit-solvent quantum chem. (QC) calcns. for arbitrary solutes and solvents in an open-source framework are still lacking. We developed AutoSolvate, an open-source toolkit, to streamline the workflow for QC calcn. of explicitly solvated mols. It automates the solvated-structure generation, force field fitting, configuration sampling, and the final extn. of microsolvated cluster structures that QC packages can readily use to predict mol. properties of interest. AutoSolvate is available through both a command line interface and a graphical user interface, making it accessible to the broader scientific community. To improve the quality of the initial structures generated by AutoSolvate, we investigated the dependence of solute-solvent closeness on solute/solvent identities and trained a machine learning model to predict the closeness and guide initial structure generation. Finally, we tested the capability of AutoSolvate for rapid dataset curation by calcg. the outer-sphere reorganization energy of a large dataset of 166 redox couples, which demonstrated the promise of the AutoSolvate package for chem. discovery efforts. (c) 2022 American Institute of Physics.

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    Ingman, V. M.; Shaefer, A. J.; Andreola, L. R. QChASM: Quantum Chemistry Automation and Structure Manipulation. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2021, 11, e1510  DOI: 10.1002/wcms.1510

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    28

    QChASM : Quantum chemistry automation and structure manipulation

    Ingman, Victoria M.; Schaefer, Anthony J.; Andreola, Laura R.; Wheeler, Steven E.

    Wiley Interdisciplinary Reviews: Computational Molecular Science (2021), 11 (4), e1510CODEN: WIRCAH; ISSN:1759-0884. (Wiley-Blackwell)

    As the tools of computational quantum chem. have continued to mature, larger and more complex mol. systems have become amenable to computational study. However, studies of these complex systems often require the execution of enormous nos. of computations, which can be a tedious and error-prone process if done manually. We have developed a suite of free, open-source tools to facilitate the automation of quantum chem. workflows. These tools are collected under the organization QChASM (Quantum Chem. Automation and Structure Manipulation) and include functionality for building and manipulating complex mol. structures and performing routine tasks (AaronTools), a toolkit for automating TS optimizations and predictions of the outcomes of selective homogeneous catalytic reactions, and a plug-in for UCSF ChimeraX that provides a graphical interface for building complex mol. structures and representing output from quantum chem. computations. These tools are described below, with a focus on the recent Python implementation of AaronTools.

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29. 29

    Cohen, A. J.; Mori-Sánchez, P.; Yang, W. Challenges for Density Functional Theory. Chem. Rev. 2012, 112, 289– 320,  DOI: 10.1021/cr200107z

    Google Scholar

    29

    Challenges for Density Functional Theory

    Cohen, Aron J.; Mori-Sanchez, Paula; Yang, Weitao

    Chemical Reviews (Washington, DC, United States) (2012), 112 (1), 289-320CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)

    A review includes the following topics: the entrance of DFT into chem., constructing approx. functionals and minimizing the total energy, insight into large systematic errors of functionals, and strong correlation.

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30. 30

    Landrum, G. RDKit: Open-Source Cheminformatics Software , 2016. https://github.com/rdkit/rdkit/releases/tag/Release_2016_09_4.

    Google Scholar

    There is no corresponding record for this reference.
31. 31

    Terayama, K.; Sumita, M.; Katouda, M.; Tsuda, K.; Okuno, Y. Efficient Search for Energetically Favorable Molecular Conformations against Metastable States via Gray-Box Optimization. J. Chem. Theory Comput. 2021, 17, 5419– 5427,  DOI: 10.1021/acs.jctc.1c00301

    Google Scholar

    31

    Efficient Search for Energetically Favorable Molecular Conformations against Metastable States via Gray-Box Optimization

    Terayama, Kei; Sumita, Masato; Katouda, Michio; Tsuda, Koji; Okuno, Yasushi

    Journal of Chemical Theory and Computation (2021), 17 (8), 5419-5427CODEN: JCTCCE; ISSN:1549-9618. (American Chemical Society)

    In order to accurately understand and est. mol. properties, finding energetically favorable mol. conformations is the most fundamental task for atomistic computational research on mols. and materials. Geometry optimization based on quantum chem. calcns. has enabled the conformation prediction of arbitrary mols., including de novo ones. However, it is computationally expensive to perform geometry optimizations for enormous conformers. In this study, we introduce the gray-box optimization (GBO) framework, which enables optimal control over the entire geometry optimization process, among multiple conformers. Algorithms designed for GBO roughly est. energetically preferable conformers during their geometry optimization iterations. They then preferentially compute promising conformers. To evaluate the performance of the GBO framework, we applied it to a test set consisting of seven dipeptides and mycophenolic acid to det. their stable conformations at the d. functional theory level. We thus preferentially obtained energetically favorable conformations. Furthermore, the computational costs required to find the most stable conformation were significantly reduced (approx. 1% on av., compared to the naive approach for the dipeptides).

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32. 32

    Hagfeldt, A.; Boschloo, G.; Sun, L.; Kloo, L.; Pettersson, H. Dye-sensitized Solar Cells. Chem. Rev. 2010, 110, 6595– 6663,  DOI: 10.1021/cr900356p

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    32

    Dye-Sensitized Solar Cells

    Hagfeldt, Anders; Boschloo, Gerrit; Sun, Licheng; Kloo, Lars; Pettersson, Henrik

    Chemical Reviews (Washington, DC, United States) (2010), 110 (11), 6595-6663CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)

    A review on dye-sensitized solar cells (DSCs). Some brief notes on solar energy in general and DSC in particular are given, followed by a discussion of the operational principles of DSC (energetics and kinetics). Then, the development of material components and some specific exptl. techniques to characterize DSC are described. The current status of module development is also discussed, and finally a brief future outlook is given.

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33. 33

    Lu, M.; Liang, M.; Han, H.-Y.; Sun, Z.; Xue, S. Organic Dyes Incorporating Bis-hexapropyltruxeneamin Moiety for Efficient Dye-Sensitized Solar Cells. J. Phys. Chem. C 2011, 115, 274– 281,  DOI: 10.1021/jp107439d

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    33

    Organic Dyes Incorporating Bis-hexapropyltruxeneamino Moiety for Efficient Dye-Sensitized Solar Cells

    Lu, Meng; Liang, Mao; Han, Hong-Yu; Sun, Zhe; Xue, Song

    Journal of Physical Chemistry C (2011), 115 (1), 274-281CODEN: JPCCCK; ISSN:1932-7447. (American Chemical Society)

    The authors report here on the synthesis and photophys./electrochem. properties of three functional triarylamine org. dyes (MXD5-7) as well as their application in dye-sensitized nanocryst. TiO2 solar cells (DSSCs). For the designed dyes, the nonplanar structures of bis-hexapropyltruxeneamino take the role of electron donor. The introduction of bis-hexapropyltruxeneamino units brought about superior performance over the simple triphenylamine dye, in terms of light-capturing abilities and suppressing dye aggregation. Among three dyes, the DSSCs based on the dye MXD7 showed the best photovoltaic performance: a short-circuit photocurrent d. (JSC) of 11.8 mA/cm2, an open-circuit photovoltage (VOC) of 772 mV, and a fill factor (ff) of 0.68, corresponding to an overall conversion efficiency of 6.18% under 100 mW/cm2 irradn. These dyes exhibited high VOC values, possible origin for which was studied regarding the TiO2 surface blocking, conduction band movement, and electrolyte-dye interaction.

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34. 34

    Kranthiraja, K.; Saeki, A. Experiment-oriented Machine Learning of Polymer:Non-Fullerene Organic Solar Cells. Adv. Funct. Mater. 2021, 31, 2011168,  DOI: 10.1002/adfm.202170168

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    34

    Experiment-Oriented Machine Learning of Polymer:Non-Fullerene Organic Solar Cells

    Kranthiraja, Kakaraparthi; Saeki, Akinori

    Advanced Functional Materials (2021), 31 (23), 2011168CODEN: AFMDC6; ISSN:1616-301X. (Wiley-VCH Verlag GmbH & Co. KGaA)

    Despite the capacity of conjugated materials for enhanced power conversion efficiency (PCE) of org. photovoltaics (OPV), a comprehensive survey of unexplored materials is beyond the reach of most researchers' resources. In such instances, a data-driven approach using machine learning (ML) is an efficient alternative; however, bridging the gap between exptl. observations and data science requires a no. of refinements. In this investigation, using a random forest model based on an exptl. dataset, a high correlation coeff. of 0.85 is achieved for the ML of polymer and non-fullerene small mol. acceptor OPVs and performed virtual screening of 200,932 conjugated polymers generated by the combinatorial coupling of donor and acceptor units. Further, to evaluate the effectiveness of the ML model, a series of conjugated polymers (based on benzodithiophene and thiazolothiazole) were designed, synthesized, and characterized with different alkyl chains. Among these, PBDTTzEH:IT-4F showed a PCE of 10.10%, which is in good correspondence with ML predictions with respect to the choice of alkyl chains. Thus, the current study demonstrates how ML can be utilized for developing OPVs using a relatively small no. of exptl. data points (566) and screening numerous mol. structures.

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35. 35

    Atkins, P. Atkins’ Physical Chemistry; Oxford University Press, 2017.

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    There is no corresponding record for this reference.
36. 36

    Uoyama, H.; Goushi, K.; Shizu, K.; Nomura, H.; Adachi, C. Highly Efficient Organic Light-emitting Diodes from Delayed Fluorescence. Nature 2012, 492, 234– 238,  DOI: 10.1038/nature11687

    Google Scholar

    36

    Highly efficient organic light-emitting diodes from delayed fluorescence

    Uoyama, Hiroki; Goushi, Kenichi; Shizu, Katsuyuki; Nomura, Hiroko; Adachi, Chihaya

    Nature (London, United Kingdom) (2012), 492 (7428), 234-238CODEN: NATUAS; ISSN:0028-0836. (Nature Publishing Group)

    The inherent flexibility afforded by mol. design has accelerated the development of a wide variety of org. semiconductors over the past 2 decades. In particular, great advances were made in the development of materials for org. light-emitting diodes (OLEDs), from early devices based on fluorescent mols. to those using phosphorescent mols. In OLEDs, elec. injected charge carriers recombine to form singlet and triplet excitons in a 1:3 ratio; the use of phosphorescent metal-org. complexes exploits the normally nonradiative triplet excitons and so enhances the overall electroluminescence efficiency. Here the authors report a class of metal-free org. electroluminescent mols. in which the energy gap between the singlet and triplet excited states is minimized by design, thereby promoting highly efficient spin up-conversion from nonradiative triplet states to radiative singlet states while maintaining high radiative decay rates, of >106 decays per s. These mols. harness both singlet and triplet excitons for light emission through fluorescence decay channels, leading to an intrinsic fluorescence efficiency >90% and a very high external electroluminescence efficiency, of >19%, which is comparable to that achieved in high-efficiency phosphorescence-based OLEDs.

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37. 37

    Boldyrev, A. I.; Simons, J.; Zakrzewski, V. G.; von Niessen, W. Vertical and Adiabatic Ionization Energies and Electron Affinities of New Silicon-carbon (SinC) and Silicon-oxygen (SinO) (n = 1-3) Molecules. J. Phys. Chem. 1994, 98, 1427,  DOI: 10.1021/j100056a010

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    37

    Vertical and adiabatic ionization energies and electron affinities of new silicon-carbon (SinC) and silicon-oxygen (SinO) (n = 1-3) molecules

    Boldyrev, A. I.; Simons, J.; Zakrzewski, V. G.; von Niessen, W.

    Journal of Physical Chemistry (1994), 98 (5), 1427-35CODEN: JPCHAX; ISSN:0022-3654.

    Vertical and adiabatic ionization potentials (IPs) as well as electron affinities have been calcd. for SiC, Si2C, Si3C, SiO, Si2O, and Si3O using five different sophisticated ab initio methods with large basis sets. The geometry and harmonic frequencies have been calcd. at the second-order Moeller-Plesset level. Results of the calcns. using all five methods are in good agreement among themselves (±0.3 eV). The calcd. vertical first IPs of SiC, Si2C, Si3C, and SiO mols. agree within 0.2 eV with exptl. appearance potentials for these species.

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38. 38

    Yang, X.; Zhang, J.; Yoshizoe, K.; Terayama, K.; Tsuda, K. ChemTS: an Efficient Python Library for De Novo Molecular Generation. Sci. Technol. Adv. Mater. 2017, 18, 972– 976,  DOI: 10.1080/14686996.2017.1401424

    Google Scholar

    38

    ChemTS: an efficient python library for de novo molecular generation

    Yang, Xiufeng; Zhang, Jinzhe; Yoshizoe, Kazuki; Terayama, Kei; Tsuda, Koji

    Science and Technology of Advanced Materials (2017), 18 (1), 972-976CODEN: STAMCV; ISSN:1878-5514. (Taylor & Francis Ltd.)

    Automatic design of org. materials requires black-box optimization in a vast chem. space. In conventional mol. design algorithms, a mol. is built as a combination of predetd. fragments. Recently, deep neural network models such as variational autoencoders and recurrent neural networks (RNNs) are shown to be effective in de novo design of mols. without any predetd. fragments. This paper presents a novel Python library ChemTS that explores the chem. space by combining Monte Carlo tree search and an RNN. In a benchmarking problem of optimizing the octanol-water partition coeff. and synthesizability, our algorithm showed superior efficiency in finding high-scoring mols.

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39. 39

    Sumita, M.; Yang, X.; Ishihara, S.; Tamura, R.; Tsuda, K. Hunting for Organic Molecules with Artificial Intelligence: Molecules Optimized for Desired Excitation Energies. ACS Cent. Sci. 2018, 4, 1126,  DOI: 10.1021/acscentsci.8b00213

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    39

    Hunting for Organic Molecules with Artificial Intelligence: Molecules Optimized for Desired Excitation Energies

    Sumita, Masato; Yang, Xiufeng; Ishihara, Shinsuke; Tamura, Ryo; Tsuda, Koji

    ACS Central Science (2018), 4 (9), 1126-1133CODEN: ACSCII; ISSN:2374-7951. (American Chemical Society)

    This work presents a proof-of-concept study in artificial-intelligence-assisted (AI-assisted) chem. where a machine-learning-based mol. generator is coupled with d. functional theory (DFT) calcns., synthesis, and measurement. Although deep-learning-based mol. generators have shown promise, it is unclear to what extent they can be useful in real-world materials development. To assess the reliability of AI-assisted chem., we prepd. a platform using a mol. generator and a DFT simulator, and attempted to generate novel photofunctional mols. whose lowest excited states lie at desired energetic levels. A 10 day run on the 12-core server discovered 86 potential photofunctional mols. around target lowest excitation levels, designated as 200, 300, 400, 500, and 600 nm. Among the mols. discovered, six were synthesized, and five were confirmed to reproduce DFT predictions in UV visible absorption measurements. This result shows the potential of AI-assisted chem. to discover ready-to-synthesize novel mols. with modest computational resources.

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40. 40

    Fujita, T.; Terayama, K.; Sumita, M.; Tamura, R.; Nakamura, Y.; Naito, M.; Tsuda, K. Understanding the Evolution of a De Novo Molecule Generator via Characteristic Functional Group Monitoring. Sci. Technol. Adv. Mater. 2022, 23, 352– 360,  DOI: 10.1080/14686996.2022.2075240

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    40

    Understanding the evolution of a de novo molecule generator via characteristic functional group monitoring

    Fujita, Takehiro; Terayama, Kei; Sumita, Masato; Tamura, Ryo; Nakamura, Yasuyuki; Naito, Masanobu; Tsuda, Koji

    Science and Technology of Advanced Materials (2022), 23 (1), 352-360CODEN: STAMCV; ISSN:1878-5514. (Taylor & Francis Ltd.)

    Recently, artificial intelligence (AI)-enabled de novo mol. generators (DNMGs) have automated mol. design based on data-driven or simulation-based property ests. In some domains like the game of Go where AI surpassed human intelligence, humans are trying to learn from AI about the best strategy of the game. To understand DNMG's strategy of mol. optimization, we propose an algorithm called characteristic functional group monitoring (CFGM). Given a time series of generated mols., CFGM monitors statistically enriched functional groups in comparison to the training data. In the task of absorption wavelength maximization of pure org. mols. (consisting of H, C, N, and O), we successfully identified a strategic change from diketone and aniline derivs. to quinone derivs. In addn., CFGM led us to a hypothesis that 1,2-quinone is an unconventional chromophore, which was verified with chem. synthesis. This study shows the possibility that human experts can learn from DNMGs to expand their ability to discover functional mols.

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41. 41

    Sumita, M.; Terayama, K.; Suzuki, N.; Ishihara, S.; Tamura, M. K.; Chahal, D. T.; Payne, K.; Yoshizoe, K. De Novo Creation of a Naked Eye–detectable Fluorescent Molecule Based on Quantum Chemical Computation and Machine Learning. Sci. Adv. 2022, 8, eabj3906  DOI: 10.1126/sciadv.abj3906

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    There is no corresponding record for this reference.
42. 42

    Zhang, Y.; Zhang, J.; Suzuki, K.; Sumita, M.; Terayama, K.; Li, J.; Mao, Z.; Tsuda, K.; Suzuki, Y. Discovery of Polymer Electret Material via De Novo Molecule Generation and Functional Group Enrichment Analysis. Appl. Phys. Lett. 2021, 118, 223904,  DOI: 10.1063/5.0051902

    Google Scholar

    42

    Discovery of polymer electret material via de novo molecule generation and functional group enrichment analysis

    Zhang, Yucheng; Zhang, Jinzhe; Suzuki, Kuniko; Sumita, Masato; Terayama, Kei; Li, Jiawen; Mao, Zetian; Tsuda, Koji; Suzuki, Yuji

    Applied Physics Letters (2021), 118 (22), 223904CODEN: APPLAB; ISSN:0003-6951. (American Institute of Physics)

    We designed a high-performance polymer electret material using a deep-learning-based de novo mol. generator. By statistically analyzing the enrichment of the functional groups of the generated mols., the hydroxyl group was detd. to be crucial for enhancing the electron gain energy. Incorporating such acquired knowledge, we designed a mol. using cyclic transparent optical polymer (CYTOP; perfluoro-3-butenyl-vinyl ether). The mol. was synthesized, and its surface potential for a 15-μm-thick film is kept at -3 kV for more than 800 h. Its performance was significantly better than all commercialized CYTOP polymer electrets, indicating great potential for its application in vibration-based energy harvesting. Our results demonstrate the application of machine learning in polymer electret design and confirm the combination of mol. generation and functional group enrichment anal. to be a promising chem. discovery method achieved via human-artificial intelligence collaboration. (c) 2021 American Institute of Physics.

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43. 43

    Zhang, J.; Terayama, K.; Sumita, M.; Yoshizoe, K.; Ito, K.; Kikuchi, J. NMR-TS: de novo molecule identification from NMR spectra. Sci. Technol. Adv. Mater. 2020, 21, 552– 561,  DOI: 10.1080/14686996.2020.1793382

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    43

    NMR-TS: de novo molecule identification from NMR spectra

    Zhang, Jinzhe; Terayama, Kei; Sumita, Masato; Yoshizoe, Kazuki; Ito, Kengo; Kikuchi, Jun; Tsuda, Koji

    Science and Technology of Advanced Materials (2020), 21 (1), 552-561CODEN: STAMCV; ISSN:1878-5514. (Taylor & Francis Ltd.)

    NMR (NMR) spectroscopy is an effective tool for identifying mols. in a sample. Although many previously obsd. NMR spectra are accumulated in public databases, they cover only a tiny fraction of the chem. space, and mol. identification is typically accomplished manually based on expert knowledge. Herein, we propose NMR-TS, a machine-learning-based python library, to automatically identify a mol. from its NMR spectrum. NMR-TS discovers candidate mols. whose NMR spectra match the target spectrum by using deep learning and d. functional theory (DFT)-computed spectra. As a proofof- concept, we identify prototypical metabolites from their computed spectra. After an av. 5451 DFT runs for each spectrum, six of the nine mols. are identified correctly, and proximal mols. are obtained in the other cases. This encouraging result implies that de novo mol. generation can contribute to the fully automated identification of chem. structures.

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44. 44

    Terayama, K.; Sumita, M.; Tamura, R.; Payne, D. T.; Chahal, M. K.; Ishihara, S.; Tsuda, K. Pushing Property Limits in Materials Discovery via Boundless Objective-free Exploration. Chem. Sci. 2020, 11, 5959– 5968,  DOI: 10.1039/d0sc00982b

    Google Scholar

    44

    Pushing property limits in materials discovery via boundless objective-free exploration

    Terayama, Kei; Sumita, Masato; Tamura, Ryo; Payne, Daniel T.; Chahal, Mandeep K.; Ishihara, Shinsuke; Tsuda, Koji

    Chemical Science (2020), 11 (23), 5959-5968CODEN: CSHCCN; ISSN:2041-6520. (Royal Society of Chemistry)

    Materials chemists develop chem. compds. to meet often conflicting demands of industrial applications. This process may not be properly modeled by black-box optimization because the target property is not well defined in some cases. Herein, we propose a new algorithm for automated materials discovery called BoundLess Objective-free eXploration (BLOX) that uses a novel criterion based on kernel-based Stein discrepancy in the property space. Unlike other objective-free exploration methods, a boundary for the materials properties is not needed; hence, BLOX is suitable for open-ended scientific endeavors. We demonstrate the effectiveness of BLOX by finding light-absorbing mols. from a drug database. Our goal is to minimize the no. of d. functional theory calcns. required to discover out-of-trend compds. in the intensity-wavelength property space. Using absorption spectroscopy, we exptl. verified that eight compds. identified as outstanding exhibit the expected optical properties. Our results show that BLOX is useful for chem. repurposing, and we expect this search method to have numerous applications in various scientific disciplines.

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45. 45

    Kim, S.; Chen, J.; Cheng, T.; Gindulyte, A.; He, J.; He, S.; Li, Q.; Shoemaker, B. A.; Thiessen, P. A.; Yu, B.; Zaslavsky, L.; Zhang, J.; Bolton, E. E. PubChem in 2021: New Data Content and Improved Web Interfaces. Nucleic Acids Res. 2021, 49, D1388– D1395,  DOI: 10.1093/nar/gkaa971

    Google Scholar

    45

    PubChem in 2021: new data content and improved web interfaces

    Kim, Sunghwan; Chen, Jie; Cheng, Tiejun; Gindulyte, Asta; He, Jia; He, Siqian; Li, Qingliang; Shoemaker, Benjamin A.; Thiessen, Paul A.; Yu, Bo; Zaslavsky, Leonid; Zhang, Jian; Bolton, Evan E.

    Nucleic Acids Research (2021), 49 (D1), D1388-D1395CODEN: NARHAD; ISSN:1362-4962. (Oxford University Press)

    PubChem is a popular chem. information resource that serves the scientific community as well as the general public, with millions of unique users per mo. In the past 2 yr, PubChem made substantial improvements. Data from >100 new data sources were added to PubChem, including chem.-literature links from Thieme Chem., chem. and phys. property links from SpringerMaterials, and patent links from the World Intellectual Properties Organization (WIPO). PubChem's homepage and individual record pages were updated to help users find desired information faster. This update involved a data model change for the data objects used by these pages as well as by programmatic users. Several new services were introduced, including the PubChem Periodic Table and Element pages, Pathway pages, and Knowledge panels. Addnl., in response to the coronavirus disease 2019 (COVID-19) outbreak, PubChem created a special data collection that contains PubChem data related to COVID-19 and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

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46. 46

    Joung, J. F.; Han, M.; Jeong, M.; Park, S. Experimental Database of Optical Properties of Organic Compounds. Sci. Data 2020, 7, 295,  DOI: 10.1038/s41597-020-00634-8

    Google Scholar

    46

    Experimental database of optical properties of organic compounds

    Joung, Joonyoung F.; Han, Minhi; Jeong, Minseok; Park, Sungnam

    Scientific Data (2020), 7 (1), 295CODEN: SDCABS; ISSN:2052-4463. (Nature Research)

    Exptl. databases on the optical properties of org. chromophores are important for the implementation of data-driven chem. using machine learning. Herein, we present a series of exptl. data including various optical properties such as the first absorption and emission max. wavelengths and their bandwidths (full width at half max.), extinction coeff., photoluminescence quantum yield, and fluorescence lifetime. A database of 20,236 data points was developed by collecting the optical properties of org. compds. already reported in the literature. A dataset of 7,016 unique org. chromophores in 365 solvents or in solid state is available in CSV format.

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47. 47

    Irwin, J. J.; Sterling, T.; Mysinger, M. M.; Bolstad, E. S.; Coleman, R. G. ZINC: A Free Tool to Discover Chemistry for Biology. J. Chem. Inf. Model. 2012, 52, 1757,  DOI: 10.1021/ci3001277

    Google Scholar

    47

    ZINC: A Free Tool to Discover Chemistry for Biology

    Irwin, John J.; Sterling, Teague; Mysinger, Michael M.; Bolstad, Erin S.; Coleman, Ryan G.

    Journal of Chemical Information and Modeling (2012), 52 (7), 1757-1768CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)

    ZINC is a free public resource for ligand discovery. The database contains over twenty million com. available mols. in biol. relevant representations that may be downloaded in popular ready-to-dock formats and subsets. The Web site also enables searches by structure, biol. activity, phys. property, vendor, catalog no., name, and CAS no. Small custom subsets may be created, edited, shared, docked, downloaded, and conveyed to a vendor for purchase. The database is maintained and curated for a high purchasing success rate and is freely available at zinc.docking.org.

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48. 48

    Nakata, M.; Shimazaki, T. PubChemQC Project: A Large-Scale First-Principles Electronic Structure Database for Data-Driven Chemistry. J. Chem. Inf. Model. 2017, 57, 1300,  DOI: 10.1021/acs.jcim.7b00083

    Google Scholar

    48

    PubChemQC Project: A Large-Scale First-Principles Electronic Structure Database for Data-Driven Chemistry

    Nakata, Maho; Shimazaki, Tomomi

    Journal of Chemical Information and Modeling (2017), 57 (6), 1300-1308CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)

    Large-scale mol. databases play an essential role in the investigation of various subjects such as the development of org. materials, in-silico drug designs, and data-driven studies with machine learning, among others. We developed a large-scale quantum chem. database based on the first-principles method without performing any expt. Our database currently contains three million mol. electronic structures based on the d. functional theory method at the B3LYP/6-31G* level, and we successively calcd. 10 low-lying excited states of over two million mols. by the time-dependent DFT method with the 6-31+G* basis set. To select the mols. calcd. in our project, we mainly referred to the PubChem project, and it was used as a source of the mol. structures in short strings using the InChI and the SMILES representations. Accordingly, we named our quantum chem. database project as "PubChemQC" (http://pubchemqc.riken.jp/) and placed it in the public domain. In this paper, we showed the fundamental features of the PubChemQC database and dis- cussed the techniques used to construct the dataset for large-scale quantum chem. calcns. We also presented a machine-learning approach to predict the electronic structure of mols. as an example to demonstrate the suitability of the large-scale quantum chem. database.

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49. 49

    Ramakrishnan, R.; Dral, P. O.; Rupp, M.; von Lilienfeld, O. A. Quantum Chemistry Structures and Properties of 134 Kilo Molecules. Sci. Data 2014, 1, 140022,  DOI: 10.1038/sdata.2014.22

    Google Scholar

    49

    Quantum chemistry structures and properties of 134 kilo molecules

    Ramakrishnan, Raghunathan; Dral, Pavlo O.; Rupp, Matthias; von Lilienfeld, O. Anatole

    Scientific Data (2014), 1 (), 140022CODEN: SDCABS; ISSN:2052-4463. (Nature Publishing Group)

    Computational de novo design of new drugs and materials requires rigorous and unbiased exploration of chem. compd. space. However, large uncharted territories persist due to its size scaling combinatorially with mol. size. We report computed geometric, energetic, electronic, and thermodn. properties for 134k stable small org. mols. made up of CHONF. These mols. correspond to the subset of all 133,885 species with up to nine heavy atoms (CONF) out of the GDB-17 chem. universe of 166 billion org. mols. We report geometries minimal in energy, corresponding harmonic frequencies, dipole moments, polarizabilities, along with energies, enthalpies, and free energies of atomization. All properties were calcd. at the B3LYP/6-31G(2df,p) level of quantum chem. Furthermore, for the predominant stoichiometry, C7H10O2, there are 6,095 constitutional isomers among the 134k mols. We report energies, enthalpies, and free energies of atomization at the more accurate G4MP2 level of theory for all of them. As such, this data set provides quantum chem. properties for a relevant, consistent, and comprehensive chem. space of small org. mols. This database may serve the benchmarking of existing methods, development of new methods, such as hybrid quantum mechanics/machine learning, and systematic identification of structure-property relationships.

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    Ruddigkeit, L.; van Deursen, R.; Blum, L. C.; Reymond, J. L. Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17. J. Chem. Inf. Model. 2012, 52, 2864– 2875,  DOI: 10.1021/ci300415d

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    50

    Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17

    Ruddigkeit, Lars; van Deursen, Ruud; Blum, Lorenz C.; Reymond, Jean-Louis

    Journal of Chemical Information and Modeling (2012), 52 (11), 2864-2875CODEN: JCISD8; ISSN:1549-9596. (American Chemical Society)

    Drug mols. consist of a few tens of atoms connected by covalent bonds. How many such mols. are possible in total and what is their structure. This question is of pressing interest in medicinal chem. to help solve the problems of drug potency, selectivity, and toxicity and reduce attrition rates by pointing to new mol. series. To better define the unknown chem. space, we have enumerated 166.4 billion mols. of up to 17 atoms of C, N, O, S, and halogens forming the chem. universe database GDB-17, covering a size range contg. many drugs and typical for lead compds. GDB-17 contains millions of isomers of known drugs, including analogs with high shape similarity to the parent drug. Compared to known mols. in PubChem, GDB-17 mols. are much richer in nonarom. heterocycles, quaternary centers, and stereoisomers, densely populate the third dimension in shape space, and represent many more scaffold types.

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51. 51

    von Lilienfeld, O. A.; Müller, K. R.; Tkatchenko, A. Exploring Chemical Compound Space with Quantum-Based Machine learning. Nat. Rev. Chem. 2020, 4, 347– 358,  DOI: 10.1038/s41570-020-0189-9

    Google Scholar

    51

    Exploring chemical compound space with quantum-based machine learning

    von Lilienfeld O Anatole; Muller Klaus-Robert; Muller Klaus-Robert; Muller Klaus-Robert; Tkatchenko Alexandre

    Nature reviews. Chemistry (2020), 4 (7), 347-358 ISSN:.

    Rational design of compounds with specific properties requires understanding and fast evaluation of molecular properties throughout chemical compound space - the huge set of all potentially stable molecules. Recent advances in combining quantum-mechanical calculations with machine learning provide powerful tools for exploring wide swathes of chemical compound space. We present our perspective on this exciting and quickly developing field by discussing key advances in the development and applications of quantum-mechanics-based machine-learning methods to diverse compounds and properties, and outlining the challenges ahead. We argue that significant progress in the exploration and understanding of chemical compound space can be made through a systematic combination of rigorous physical theories, comprehensive synthetic data sets of microscopic and macroscopic properties, and modern machine-learning methods that account for physical and chemical knowledge.

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52. 52

    Cai, J.; Chu, X.; Xu, K.; Li, H.; Wei, J. Machine Learning-driven New Material Discovery. Nanoscale Adv. 2020, 2, 3115– 3130,  DOI: 10.1039/d0na00388c

    Google Scholar

    52

    Machine learning-driven new material discovery

    Cai Jiazhen; Chu Xuan; Xu Kun; Wei Jing; Li Hongbo; Wei Jing

    Nanoscale advances (2020), 2 (8), 3115-3130 ISSN:.

    New materials can bring about tremendous progress in technology and applications. However, the commonly used trial-and-error method cannot meet the current need for new materials. Now, a newly proposed idea of using machine learning to explore new materials is becoming popular. In this paper, we review this research paradigm of applying machine learning in material discovery, including data preprocessing, feature engineering, machine learning algorithms and cross-validation procedures. Furthermore, we propose to assist traditional DFT calculations with machine learning for material discovery. Many experiments and literature reports have shown the great effects and prospects of this idea. It is currently showing its potential and advantages in property prediction, material discovery, inverse design, corrosion detection and many other aspects of life.

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53. 53

    Huber, S. P. Automated Reproducible Workflows and Data Provenance with AiiDA. Nat. Rev. Phys. 2022, 4, 431,  DOI: 10.1038/s42254-022-00463-1

    Google Scholar

    There is no corresponding record for this reference.
54. 54

    Lundberg, M.; Siegbahn, P. E. M. Quantifying the Effects of the Self-interaction Error in DFT: When Do the Delocalized States Appear?. J. Chem. Phys. 2005, 122, 224103,  DOI: 10.1063/1.1926277

    Google Scholar

    54

    Quantifying the effects of the self-interaction error in DFT: When do the delocalized states appear?

    Lundberg, Marcus; Siegbahn, Per E. M.

    Journal of Chemical Physics (2005), 122 (22), 224103/1-224103/9CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)

    The self-interaction error in d.-functional theory leads to artificial stabilization of delocalized states, most evident in systems with an odd no. of electrons. Clear examples are dissocns. of carbocation radicals that often give delocalized states at long distances and large errors in computed binding energies. On the other hand, many mixed-valence transition-metal dimers known to exhibit valence trapping are correctly predicted to be localized. To understand the effects of the self-interaction error on these different systems, energy differences between delocalized and localized states are calcd. with B3LYP. In the dissocn. of radicals into sym. fragments at infinite distance, this energy difference equals the error of the d.-functional treatment. The energy difference decreases with increasing size of the system, from 55 kcal/mol in H2+ to 15 kcal/mol for C12H26+. Solvent corrections stabilize the localized state and result in smaller errors. Most reactions are asym. and this decreases the effect of the self-interaction error. In many systems, delocalization will not occur if the cost to move the electron from one fragment to the other is 70-80 kcal/mol (3.0-3.5 eV). This est. refers to a situation where the distance between the fragments is infinite. The limit decreases with decreasing fragment distance. B3LYP calcns. on the ferromagnetic state of a Mn(III,IV) dimer predict that the correct localized state is 22 kcal/mol more stable than the incorrect delocalized state. At short metal-metal distances the effect of the self-interaction error is predicted to be small. However, as the distance between the two manganese centers is increased to 7 Å, the dimer starts to delocalize and the energy artificially decreases. In the dissocn. limit, the error is 10 kcal/mol. This is interpreted as an artifact originating from the self-interaction error. Delocalization is not encountered in many systems due to relatively short metal-metal distances and asym. ligand environments. However, some charge-transfer complexes cannot be properly calcd. and delocalized states may become a problem in large models of enzyme systems with multiple transition-metal complexes.

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    Sanchez-Lengeling, B.; Aspuru-Guzik, A. Inverse Molecular Design Using Machine Learning: Generative Models for Matter Engineering. Science 2018, 361, 360– 365,  DOI: 10.1126/science.aat2663

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    55

    Inverse molecular design using machine learning: Generative models for matter engineering

    Sanchez-Lengeling, Benjamin; Aspuru-Guzik, Alan

    Science (Washington, DC, United States) (2018), 361 (6400), 360-365CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)

    The discovery of new materials can bring enormous societal and technol. progress. In this context, exploring completely the large space of potential materials is computationally intractable. Here, we review methods for achieving inverse design, which aims to discover tailored materials from the starting point of a particular desired functionality. Recent advances from the rapidly growing field of artificial intelligence, mostly from the subfield of machine learning, have resulted in a fertile exchange of ideas, where approaches to inverse mol. design are being proposed and employed at a rapid pace. Among these, deep generative models have been applied to numerous classes of materials: rational design of prospective drugs, synthetic routes to org. compds., and optimization of photovoltaics and redox flow batteries, as well as a variety of other solid-state materials.

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    Kim, K.; Kang, S.; Yoo, J.; Kwon, Y.; Nam, Y.; Lee, D.; Kim, I.; Choi, Y.-s.; Jung, Y.; Kim, S.; Son, W.-j.; Son, J.; Lee, H. S.; Kim, S.; Shin, J.; Hwang, S. Deep-learning-based Inverse Design Model for Intelligent Discovery of Organic Molecules. npj Comput. Mater. 2018, 4, 67,  DOI: 10.1038/s41524-018-0128-1

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