Pair your accounts.

Export articles to Mendeley

Get article recommendations from ACS based on references in your Mendeley library.

Pair your accounts.

Export articles to Mendeley

Get article recommendations from ACS based on references in your Mendeley library.

You’ve supercharged your research process with ACS and Mendeley!

STEP 1:
Click to create an ACS ID

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

MENDELEY PAIRING EXPIRED
Your Mendeley pairing has expired. Please reconnect
ACS Publications. Most Trusted. Most Cited. Most Read
My Activity
CONTENT TYPES

Figure 1Loading Img

The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid

View Author Information
Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, Massachusetts 02138, United States
Facultad de Química, Universidad Nacional Autónoma de México, México, DF 04510, México
§ Department of Chemistry, Haverford College, 370 Lancaster Avenue, Haverford, Pennsylvania 19041, United States
Electronic address: [email protected] (J.H.); [email protected] (A.A.-G.).
Cite this: J. Phys. Chem. Lett. 2011, 2, 17, 2241–2251
Publication Date (Web):August 22, 2011
https://doi.org/10.1021/jz200866s
Copyright © 2011 American Chemical Society

    Article Views

    8495

    Altmetric

    -

    Citations

    LEARN ABOUT THESE METRICS
    Other access options

    Abstract

    Abstract Image

    This Perspective introduces the Harvard Clean Energy Project (CEP), a theory-driven search for the next generation of organic solar cell materials. We give a broad overview of its setup and infrastructure, present first results, and outline upcoming developments. CEP has established an automated, high-throughput, in silico framework to study potential candidate structures for organic photovoltaics. The current project phase is concerned with the characterization of millions of molecular motifs using first-principles quantum chemistry. The scale of this study requires a correspondingly large computational resource, which is provided by distributed volunteer computing on IBM’s World Community Grid. The results are compiled and analyzed in a reference database and will be made available for public use. In addition to finding specific candidates with certain properties, it is the goal of CEP to illuminate and understand the structure–property relations in the domain of organic electronics. Such insights can open the door to a rational and systematic design of future high-performance materials. The computational work in CEP is tightly embedded in a collaboration with experimentalists, who provide valuable input and feedback to the project.

    Read this article

    To access this article, please review the available access options below.

    Get instant access

    Purchase Access

    Read this article for 48 hours. Check out below using your ACS ID or as a guest.

    Recommended

    Access through Your Institution

    You may have access to this article through your institution.

    Your institution does not have access to this content. You can change your affiliated institution below.

    Cited By

    This article is cited by 460 publications.

    1. Chao Pang, Jianbo Qiao, Xiangxiang Zeng, Quan Zou, Leyi Wei. Deep Generative Models in De Novo Drug Molecule Generation. Journal of Chemical Information and Modeling 2024, 64 (7) , 2174-2194. https://doi.org/10.1021/acs.jcim.3c01496
    2. Cheng-Wei Ju, Yili Shen, Ethan J. French, Jun Yi, Hongshan Bi, Aaron Tian, Zhou Lin. Accurate Electronic and Optical Properties of Organic Doublet Radicals Using Machine Learned Range-Separated Functionals. The Journal of Physical Chemistry A 2024, 128 (12) , 2457-2471. https://doi.org/10.1021/acs.jpca.3c07437
    3. Qiaomu Yang, Aikaterini Vriza, Cesar A. Castro Rubio, Henry Chan, Yukun Wu, Jie Xu. Artificial Intelligence for Conjugated Polymers. Chemistry of Materials 2024, 36 (6) , 2602-2622. https://doi.org/10.1021/acs.chemmater.3c02358
    4. Yanqiao Zhu, Dingshuo Chen, Yuanqi Du, Yingze Wang, Qiang Liu, Shu Wu. Molecular Contrastive Pretraining with Collaborative Featurizations. Journal of Chemical Information and Modeling 2024, 64 (4) , 1112-1122. https://doi.org/10.1021/acs.jcim.3c01468
    5. Kyle R. Stoltz, Mario F. Borunda. Benchmarking DFT and Supervised Machine Learning: An Organic Semiconducting Polymer Investigation. The Journal of Physical Chemistry A 2024, 128 (4) , 709-715. https://doi.org/10.1021/acs.jpca.3c04905
    6. Aaron L. Liu, Myeongyeon Lee, Rahul Venkatesh, Jessica A. Bonsu, Ron Volkovinsky, J. Carson Meredith, Elsa Reichmanis, Martha A. Grover. Conjugated Polymer Process Ontology and Experimental Data Repository for Organic Field-Effect Transistors. Chemistry of Materials 2023, 35 (21) , 8816-8826. https://doi.org/10.1021/acs.chemmater.3c01842
    7. Damian M. Wilary, Jacqueline M. Cole. ReactionDataExtractor 2.0: A Deep Learning Approach for Data Extraction from Chemical Reaction Schemes. Journal of Chemical Information and Modeling 2023, 63 (19) , 6053-6067. https://doi.org/10.1021/acs.jcim.3c00422
    8. Ali Hashemi, Sana Bougueroua, Marie-Pierre Gaigeot, Evgeny A. Pidko. HiREX: High-Throughput Reactivity Exploration for Extended Databases of Transition-Metal Catalysts. Journal of Chemical Information and Modeling 2023, 63 (19) , 6081-6094. https://doi.org/10.1021/acs.jcim.3c00660
    9. Maho Nakata, Toshiyuki Maeda. PubChemQC B3LYP/6-31G*//PM6 Data Set: The Electronic Structures of 86 Million Molecules Using B3LYP/6-31G* Calculations. Journal of Chemical Information and Modeling 2023, 63 (18) , 5734-5754. https://doi.org/10.1021/acs.jcim.3c00899
    10. Azzaya Khasbaatar, Zhuang Xu, Jong-Hoon Lee, Gonzalo Campillo-Alvarado, Changhyun Hwang, Brandon N. Onusaitis, Ying Diao. From Solution to Thin Film: Molecular Assembly of π-Conjugated Systems and Impact on (Opto)electronic Properties. Chemical Reviews 2023, 123 (13) , 8395-8487. https://doi.org/10.1021/acs.chemrev.2c00905
    11. Vinayak Bhat, Connor P. Callaway, Chad Risko. Computational Approaches for Organic Semiconductors: From Chemical and Physical Understanding to Predicting New Materials. Chemical Reviews 2023, 123 (12) , 7498-7547. https://doi.org/10.1021/acs.chemrev.2c00704
    12. Dmitrij Rappoport. Statistics and Bias-Free Sampling of Reaction Mechanisms from Reaction Network Models. The Journal of Physical Chemistry A 2023, 127 (24) , 5252-5263. https://doi.org/10.1021/acs.jpca.3c01430
    13. Susanne Fürst, Martin Kaupp. Accurate Ionization Potentials, Electron Affinities, and Band Gaps from the ωLH22t Range-Separated Local Hybrid Functional: No Tuning Required. Journal of Chemical Theory and Computation 2023, 19 (11) , 3146-3158. https://doi.org/10.1021/acs.jctc.3c00173
    14. Andrew R. Cameron, Adam J. Proud, Jason K. Pearson. Machine Learned Composite Methods for Electronic Structure Theory. Journal of Chemical Theory and Computation 2023, 19 (1) , 51-60. https://doi.org/10.1021/acs.jctc.2c00564
    15. Bas van Beek, Juliette Zito, Lucas Visscher, Ivan Infante. CAT: A Compound Attachment Tool for the Construction of Composite Chemical Compounds. Journal of Chemical Information and Modeling 2022, 62 (22) , 5525-5535. https://doi.org/10.1021/acs.jcim.2c00690
    16. Man Li, Lingyun Dai, Yongjie Hu. Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization. ACS Energy Letters 2022, 7 (10) , 3204-3226. https://doi.org/10.1021/acsenergylett.2c01836
    17. Tatsuhito Ando, Naoto Shimizu, Norihisa Yamamoto, Nobuyuki N. Matsuzawa, Hiroyuki Maeshima, Hiromasa Kaneko. Design of Molecules with Low Hole and Electron Reorganization Energy Using DFT Calculations and Bayesian Optimization. The Journal of Physical Chemistry A 2022, 126 (36) , 6336-6347. https://doi.org/10.1021/acs.jpca.2c05229
    18. Kakaraparthi Kranthiraja, Akinori Saeki. Machine Learning-Assisted Polymer Design for Improving the Performance of Non-Fullerene Organic Solar Cells. ACS Applied Materials & Interfaces 2022, 14 (25) , 28936-28944. https://doi.org/10.1021/acsami.2c06077
    19. Bingke Li, Yongsheng Yang, Hangbo Qi, Zhehao Sun, Fan Yang, Kexin Huang, Zhaoyang Chen, Bing He, Xiuchan Xiao, Chen Shen, Ning Wang. Monolayer Sc2I2S2: An Excellent n-Type Thermoelectric Material with Significant Anisotropy. ACS Applied Energy Materials 2022, 5 (6) , 7230-7239. https://doi.org/10.1021/acsaem.2c00785
    20. Devon P. Holst, Pascal Friederich, Alán Aspuru-Guzik, Timothy P. Bender. Updated Calibrated Model for the Prediction of Molecular Frontier Orbital Energies and Its Application to Boron Subphthalocyanines. Journal of Chemical Information and Modeling 2022, 62 (4) , 829-840. https://doi.org/10.1021/acs.jcim.1c01048
    21. Connor P. Callaway, Aaron L. Liu, Rahul Venkatesh, Yulong Zheng, Myeongyeon Lee, J. Carson Meredith, Martha Grover, Chad Risko, Elsa Reichmanis. The Solution is the Solution: Data-Driven Elucidation of Solution-to-Device Feature Transfer for π-Conjugated Polymer Semiconductors. ACS Applied Materials & Interfaces 2022, 14 (3) , 3613-3620. https://doi.org/10.1021/acsami.1c20994
    22. Tobias Gensch, Gabriel dos Passos Gomes, Pascal Friederich, Ellyn Peters, Théophile Gaudin, Robert Pollice, Kjell Jorner, AkshatKumar Nigam, Michael Lindner-D’Addario, Matthew S. Sigman, Alán Aspuru-Guzik. A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis. Journal of the American Chemical Society 2022, 144 (3) , 1205-1217. https://doi.org/10.1021/jacs.1c09718
    23. Yuta Miyake, Akinori Saeki. Machine Learning-Assisted Development of Organic Solar Cell Materials: Issues, Analyses, and Outlooks. The Journal of Physical Chemistry Letters 2021, 12 (51) , 12391-12401. https://doi.org/10.1021/acs.jpclett.1c03526
    24. Kareesa J. Kron, Andres Rodriguez-Katakura, Rachelle Elhessen, Shaama Mallikarjun Sharada. Photoredox Chemistry with Organic Catalysts: Role of Computational Methods. ACS Omega 2021, 6 (49) , 33253-33264. https://doi.org/10.1021/acsomega.1c05787
    25. Tiago Sousa, João Correia, Vítor Pereira, Miguel Rocha. Generative Deep Learning for Targeted Compound Design. Journal of Chemical Information and Modeling 2021, 61 (11) , 5343-5361. https://doi.org/10.1021/acs.jcim.0c01496
    26. Damian M. Wilary, Jacqueline M. Cole. ReactionDataExtractor: A Tool for Automated Extraction of Information from Chemical Reaction Schemes. Journal of Chemical Information and Modeling 2021, 61 (10) , 4962-4974. https://doi.org/10.1021/acs.jcim.1c01017
    27. Cheng-Wei Ju, Ethan J. French, Nadav Geva, Alexander W. Kohn, Zhou Lin. Stacked Ensemble Machine Learning for Range-Separation Parameters. The Journal of Physical Chemistry Letters 2021, 12 (39) , 9516-9524. https://doi.org/10.1021/acs.jpclett.1c02506
    28. Steven Bennett, Filip T. Szczypiński, Lukas Turcani, Michael E. Briggs, Rebecca L. Greenaway, Kim E. Jelfs. Materials Precursor Score: Modeling Chemists’ Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors. Journal of Chemical Information and Modeling 2021, 61 (9) , 4342-4356. https://doi.org/10.1021/acs.jcim.1c00375
    29. Andreas Eibeck, Daniel Nurkowski, Angiras Menon, Jiaru Bai, Jinkui Wu, Li Zhou, Sebastian Mosbach, Jethro Akroyd, Markus Kraft. Predicting Power Conversion Efficiency of Organic Photovoltaics: Models and Data Analysis. ACS Omega 2021, 6 (37) , 23764-23775. https://doi.org/10.1021/acsomega.1c02156
    30. Gabriel Marques, Karl Leswing, Tim Robertson, David Giesen, Mathew D. Halls, Alexander Goldberg, Kyle Marshall, Joshua Staker, Tsuguo Morisato, Hiroyuki Maeshima, Hideyuki Arai, Masaru Sasago, Eiji Fujii, Nobuyuki N. Matsuzawa. De Novo Design of Molecules with Low Hole Reorganization Energy Based on a Quarter-Million Molecule DFT Screen. The Journal of Physical Chemistry A 2021, 125 (33) , 7331-7343. https://doi.org/10.1021/acs.jpca.1c04587
    31. John A. Keith, Valentin Vassilev-Galindo, Bingqing Cheng, Stefan Chmiela, Michael Gastegger, Klaus-Robert Müller, Alexandre Tkatchenko. Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems. Chemical Reviews 2021, 121 (16) , 9816-9872. https://doi.org/10.1021/acs.chemrev.1c00107
    32. Carles Martí, Sarah Blanck, Ruben Staub, Sophie Loehlé, Carine Michel, Stephan N. Steinmann. DockOnSurf: A Python Code for the High-Throughput Screening of Flexible Molecules Adsorbed on Surfaces. Journal of Chemical Information and Modeling 2021, 61 (7) , 3386-3396. https://doi.org/10.1021/acs.jcim.1c00256
    33. Hamza Jnane, Nicolas P. D. Sawaya, Borja Peropadre, Alan Aspuru-Guzik, Raul Garcia-Patron, Joonsuk Huh. Analog Quantum Simulation of Non-Condon Effects in Molecular Spectroscopy. ACS Photonics 2021, 8 (7) , 2007-2016. https://doi.org/10.1021/acsphotonics.1c00059
    34. Masoud Fetanat, Mohammadali Keshtiara, Ze-Xian Low, Ramazan Keyikoglu, Alireza Khataee, Yasin Orooji, Vicki Chen, Gregory Leslie, Amir Razmjou. Machine Learning for Advanced Design of Nanocomposite Ultrafiltration Membranes. Industrial & Engineering Chemistry Research 2021, 60 (14) , 5236-5250. https://doi.org/10.1021/acs.iecr.0c05446
    35. Robert Pollice, Gabriel dos Passos Gomes, Matteo Aldeghi, Riley J. Hickman, Mario Krenn, Cyrille Lavigne, Michael Lindner-D’Addario, AkshatKumar Nigam, Cher Tian Ser, Zhenpeng Yao, Alán Aspuru-Guzik. Data-Driven Strategies for Accelerated Materials Design. Accounts of Chemical Research 2021, 54 (4) , 849-860. https://doi.org/10.1021/acs.accounts.0c00785
    36. Maho Nakata, Tomomi Shimazaki, Masatomo Hashimoto, Toshiyuki Maeda. PubChemQC PM6: Data Sets of 221 Million Molecules with Optimized Molecular Geometries and Electronic Properties. Journal of Chemical Information and Modeling 2020, 60 (12) , 5891-5899. https://doi.org/10.1021/acs.jcim.0c00740
    37. J. A. Arzola-Flores, A. L. González. Machine Learning for Predicting the Surface Plasmon Resonance of Perfect and Concave Gold Nanocubes. The Journal of Physical Chemistry C 2020, 124 (46) , 25447-25454. https://doi.org/10.1021/acs.jpcc.0c05995
    38. B. Christopher Rinderspacher. Heuristic Global Optimization in Chemical Compound Space. The Journal of Physical Chemistry A 2020, 124 (43) , 9044-9060. https://doi.org/10.1021/acs.jpca.0c05941
    39. Jinkui Wu, Shihui Wang, Li Zhou, Xu Ji, Yiyang Dai, Yagu Dang, Markus Kraft. Deep-Learning Architecture in QSPR Modeling for the Prediction of Energy Conversion Efficiency of Solar Cells. Industrial & Engineering Chemistry Research 2020, 59 (42) , 18991-19000. https://doi.org/10.1021/acs.iecr.0c03880
    40. Erin Antono, Nobuyuki N. Matsuzawa, Julia Ling, James Edward Saal, Hideyuki Arai, Masaru Sasago, Eiji Fujii. Machine-Learning Guided Quantum Chemical and Molecular Dynamics Calculations to Design Novel Hole-Conducting Organic Materials. The Journal of Physical Chemistry A 2020, 124 (40) , 8330-8340. https://doi.org/10.1021/acs.jpca.0c05769
    41. Bao-Xin Xue, Mario Barbatti, Pavlo O. Dral. Machine Learning for Absorption Cross Sections. The Journal of Physical Chemistry A 2020, 124 (35) , 7199-7210. https://doi.org/10.1021/acs.jpca.0c05310
    42. Tom A. Young, Razvan Gheorghe, Fernanda Duarte. cgbind: A Python Module and Web App for Automated Metallocage Construction and Host–Guest Characterization. Journal of Chemical Information and Modeling 2020, 60 (7) , 3546-3557. https://doi.org/10.1021/acs.jcim.0c00519
    43. Anna M. Hiszpanski, Brian Gallagher, Karthik Chellappan, Peggy Li, Shusen Liu, Hyojin Kim, Jinkyu Han, Bhavya Kailkhura, David J. Buttler, Thomas Yong-Jin Han. Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge. Journal of Chemical Information and Modeling 2020, 60 (6) , 2876-2887. https://doi.org/10.1021/acs.jcim.0c00199
    44. Yue Huang, Jingtian Zhang, Edwin S. Jiang, Yutaka Oya, Akinori Saeki, Gota Kikugawa, Tomonaga Okabe, Fumio S. Ohuchi. Structure–Property Correlation Study for Organic Photovoltaic Polymer Materials Using Data Science Approach. The Journal of Physical Chemistry C 2020, 124 (24) , 12871-12882. https://doi.org/10.1021/acs.jpcc.0c00517
    45. Edward J. Beard, Jacqueline M. Cole. ChemSchematicResolver: A Toolkit to Decode 2D Chemical Diagrams with Labels and R-Groups into Annotated Chemical Named Entities. Journal of Chemical Information and Modeling 2020, 60 (4) , 2059-2072. https://doi.org/10.1021/acs.jcim.0c00042
    46. Andrew Z. Summers, Justin B. Gilmer, Christopher R. Iacovella, Peter T. Cummings, Clare McCabe. MoSDeF, a Python Framework Enabling Large-Scale Computational Screening of Soft Matter: Application to Chemistry-Property Relationships in Lubricating Monolayer Films. Journal of Chemical Theory and Computation 2020, 16 (3) , 1779-1793. https://doi.org/10.1021/acs.jctc.9b01183
    47. Shi-Ping Peng, Yi Zhao. Convolutional Neural Networks for the Design and Analysis of Non-Fullerene Acceptors. Journal of Chemical Information and Modeling 2019, 59 (12) , 4993-5001. https://doi.org/10.1021/acs.jcim.9b00732
    48. Biruk G. Abreha, Snigdha Agarwal, Ian Foster, Ben Blaiszik, Steven A. Lopez. Virtual Excited State Reference for the Discovery of Electronic Materials Database: An Open-Access Resource for Ground and Excited State Properties of Organic Molecules. The Journal of Physical Chemistry Letters 2019, 10 (21) , 6835-6841. https://doi.org/10.1021/acs.jpclett.9b02577
    49. Sule Atahan-Evrenk, F. Betul Atalay. Prediction of Intramolecular Reorganization Energy Using Machine Learning. The Journal of Physical Chemistry A 2019, 123 (36) , 7855-7863. https://doi.org/10.1021/acs.jpca.9b02733
    50. Kamal Choudhary, Marnik Bercx, Jie Jiang, Ruth Pachter, Dirk Lamoen, Francesca Tavazza. Accelerated Discovery of Efficient Solar Cell Materials Using Quantum and Machine-Learning Methods. Chemistry of Materials 2019, 31 (15) , 5900-5908. https://doi.org/10.1021/acs.chemmater.9b02166
    51. Yutaka Imamura, Marina Suganuma, Masahiko Hada. Computational Study on the Search for Non-Fullerene Acceptors, Examination of Interface Geometry, and Investigation of Electron Transfer. The Journal of Physical Chemistry C 2019, 123 (29) , 17678-17685. https://doi.org/10.1021/acs.jpcc.9b02933
    52. Ruimin Ma, Zeyu Liu, Quanwei Zhang, Zhiyu Liu, Tengfei Luo. Evaluating Polymer Representations via Quantifying Structure–Property Relationships. Journal of Chemical Information and Modeling 2019, 59 (7) , 3110-3119. https://doi.org/10.1021/acs.jcim.9b00358
    53. Felipe Zapata, Lars Ridder, Johan Hidding, Christoph R. Jacob, Ivan Infante, Lucas Visscher. QMflows: A Tool Kit for Interoperable Parallel Workflows in Quantum Chemistry. Journal of Chemical Information and Modeling 2019, 59 (7) , 3191-3197. https://doi.org/10.1021/acs.jcim.9b00384
    54. Wesley Beckner, Jim Pfaendtner. Fantastic Liquids and Where To Find Them: Optimizations of Discrete Chemical Space. Journal of Chemical Information and Modeling 2019, 59 (6) , 2617-2625. https://doi.org/10.1021/acs.jcim.9b00087
    55. Murat Mesta, Jin Hyun Chang, Suranjan Shil, Kristian S. Thygesen, Juan Maria Garcia Lastra. A Protocol for Fast Prediction of Electronic and Optical Properties of Donor–Acceptor Polymers Using Density Functional Theory and the Tight-Binding Method. The Journal of Physical Chemistry A 2019, 123 (23) , 4980-4989. https://doi.org/10.1021/acs.jpca.9b02391
    56. Mohammad Atif Faiz Afzal, Mojtaba Haghighatlari, Sai Prasad Ganesh, Chong Cheng, Johannes Hachmann. Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining. The Journal of Physical Chemistry C 2019, 123 (23) , 14610-14618. https://doi.org/10.1021/acs.jpcc.9b01147
    57. Erick J. Braham, Junsang Cho, Kristel M. Forlano, David F. Watson, Raymundo Arròyave, Sarbajit Banerjee. Machine Learning-Directed Navigation of Synthetic Design Space: A Statistical Learning Approach to Controlling the Synthesis of Perovskite Halide Nanoplatelets in the Quantum-Confined Regime. Chemistry of Materials 2019, 31 (9) , 3281-3292. https://doi.org/10.1021/acs.chemmater.9b00212
    58. Lukas Turcani, Rebecca L. Greenaway, Kim E. Jelfs. Machine Learning for Organic Cage Property Prediction. Chemistry of Materials 2019, 31 (3) , 714-727. https://doi.org/10.1021/acs.chemmater.8b03572
    59. Daniel Sylvinson M. R., Hsiao-Fan Chen, Lauren M. Martin, Patrick J. G. Saris, Mark E. Thompson. Rapid Multiscale Computational Screening for OLED Host Materials. ACS Applied Materials & Interfaces 2019, 11 (5) , 5276-5288. https://doi.org/10.1021/acsami.8b16225
    60. Liam Wilbraham, Enrico Berardo, Lukas Turcani, Kim E. Jelfs, Martijn A. Zwijnenburg. High-Throughput Screening Approach for the Optoelectronic Properties of Conjugated Polymers. Journal of Chemical Information and Modeling 2018, 58 (12) , 2450-2459. https://doi.org/10.1021/acs.jcim.8b00256
    61. Hang Liu, Jia-Tao Sun, Miao Liu, Sheng Meng. Screening Magnetic Two-Dimensional Atomic Crystals with Nontrivial Electronic Topology. The Journal of Physical Chemistry Letters 2018, 9 (23) , 6709-6715. https://doi.org/10.1021/acs.jpclett.8b02783
    62. Aditya Nandy, Chenru Duan, Jon Paul Janet, Stefan Gugler, Heather J. Kulik. Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry. Industrial & Engineering Chemistry Research 2018, 57 (42) , 13973-13986. https://doi.org/10.1021/acs.iecr.8b04015
    63. Alessandro Landi, Alessandro Troisi. Rapid Evaluation of Dynamic Electronic Disorder in Molecular Semiconductors. The Journal of Physical Chemistry C 2018, 122 (32) , 18336-18345. https://doi.org/10.1021/acs.jpcc.8b05511
    64. Andrew E. Sifain, Nicholas Lubbers, Benjamin T. Nebgen, Justin S. Smith, Andrey Y. Lokhov, Olexandr Isayev, Adrian E. Roitberg, Kipton Barros, Sergei Tretiak. Discovering a Transferable Charge Assignment Model Using Machine Learning. The Journal of Physical Chemistry Letters 2018, 9 (16) , 4495-4501. https://doi.org/10.1021/acs.jpclett.8b01939
    65. Shinji Nagasawa, Eman Al-Naamani, Akinori Saeki. Computer-Aided Screening of Conjugated Polymers for Organic Solar Cell: Classification by Random Forest. The Journal of Physical Chemistry Letters 2018, 9 (10) , 2639-2646. https://doi.org/10.1021/acs.jpclett.8b00635
    66. Jon Paul Janet, Lydia Chan, and Heather J. Kulik . Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network. The Journal of Physical Chemistry Letters 2018, 9 (5) , 1064-1071. https://doi.org/10.1021/acs.jpclett.8b00170
    67. Rafael Gómez-Bombarelli, Jennifer N. Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, and Alán Aspuru-Guzik . Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. ACS Central Science 2018, 4 (2) , 268-276. https://doi.org/10.1021/acscentsci.7b00572
    68. Martín A. Mosquera, Bo Fu, Kevin L. Kohlstedt, George C. Schatz, Mark A. Ratner. Wave Functions, Density Functionals, and Artificial Intelligence for Materials and Energy Research: Future Prospects and Challenges. ACS Energy Letters 2018, 3 (1) , 155-162. https://doi.org/10.1021/acsenergylett.7b01058
    69. Yutaka Imamura, Motomichi Tashiro, Michio Katouda, and Masahiko Hada . Automatic High-Throughput Screening Scheme for Organic Photovoltaics: Estimating the Orbital Energies of Polymers from Oligomers and Evaluating the Photovoltaic Characteristics. The Journal of Physical Chemistry C 2017, 121 (51) , 28275-28286. https://doi.org/10.1021/acs.jpcc.7b08446
    70. Youjun Xu, Jianfeng Pei, and Luhua Lai . Deep Learning Based Regression and Multiclass Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction. Journal of Chemical Information and Modeling 2017, 57 (11) , 2672-2685. https://doi.org/10.1021/acs.jcim.7b00244
    71. Jon Paul Janet and Heather J. Kulik . Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships. The Journal of Physical Chemistry A 2017, 121 (46) , 8939-8954. https://doi.org/10.1021/acs.jpca.7b08750
    72. Edward Kim, Kevin Huang, Adam Saunders, Andrew McCallum, Gerbrand Ceder, and Elsa Olivetti . Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning. Chemistry of Materials 2017, 29 (21) , 9436-9444. https://doi.org/10.1021/acs.chemmater.7b03500
    73. Michael G. Mavros, James J. Shepherd, Takashi Tsuchimochi, Alexandra R. McIsaac, and Troy Van Voorhis . Computational Design Principles of Two-Center First-Row Transition Metal Oxide Oxygen Evolution Catalysts. The Journal of Physical Chemistry C 2017, 121 (29) , 15665-15674. https://doi.org/10.1021/acs.jpcc.7b02424
    74. Maho Nakata and Tomomi Shimazaki . PubChemQC Project: A Large-Scale First-Principles Electronic Structure Database for Data-Driven Chemistry. Journal of Chemical Information and Modeling 2017, 57 (6) , 1300-1308. https://doi.org/10.1021/acs.jcim.7b00083
    75. Kun Yao, John E. Herr, Seth N. Brown, and John Parkhill . Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network. The Journal of Physical Chemistry Letters 2017, 8 (12) , 2689-2694. https://doi.org/10.1021/acs.jpclett.7b01072
    76. Loïc M. Roch and Kim K. Baldridge . Dispersion-Corrected Spin-Component-Scaled Double-Hybrid Density Functional Theory: Implementation and Performance for Non-covalent Interactions. Journal of Chemical Theory and Computation 2017, 13 (6) , 2650-2666. https://doi.org/10.1021/acs.jctc.7b00220
    77. Jon Paul Janet, Terry Z. H. Gani, Adam H. Steeves, Efthymios I. Ioannidis, and Heather J. Kulik . Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design. Industrial & Engineering Chemistry Research 2017, 56 (17) , 4898-4910. https://doi.org/10.1021/acs.iecr.7b00808
    78. Alain C. Vaucher and Markus Reiher . Steering Orbital Optimization out of Local Minima and Saddle Points Toward Lower Energy. Journal of Chemical Theory and Computation 2017, 13 (3) , 1219-1228. https://doi.org/10.1021/acs.jctc.7b00011
    79. Evan D. Miller, Matthew L. Jones, and Eric Jankowski . Enhanced Computational Sampling of Perylene and Perylothiophene Packing with Rigid-Body Models. ACS Omega 2017, 2 (1) , 353-362. https://doi.org/10.1021/acsomega.6b00371
    80. Florbela Pereira, Kaixia Xiao, Diogo A. R. S. Latino, Chengcheng Wu, Qingyou Zhang, and Joao Aires-de-Sousa . Machine Learning Methods to Predict Density Functional Theory B3LYP Energies of HOMO and LUMO Orbitals. Journal of Chemical Information and Modeling 2017, 57 (1) , 11-21. https://doi.org/10.1021/acs.jcim.6b00340
    81. Adam G. Gagorik, Brett Savoie, Nick Jackson, Ankit Agrawal, Alok Choudhary, Mark A. Ratner, George C. Schatz, and Kevin L. Kohlstedt . Improved Scaling of Molecular Network Calculations: The Emergence of Molecular Domains. The Journal of Physical Chemistry Letters 2017, 8 (2) , 415-421. https://doi.org/10.1021/acs.jpclett.6b02921
    82. Kenley M. Pelzer, Lei Cheng, and Larry A. Curtiss . Effects of Functional Groups in Redox-Active Organic Molecules: A High-Throughput Screening Approach. The Journal of Physical Chemistry C 2017, 121 (1) , 237-245. https://doi.org/10.1021/acs.jpcc.6b11473
    83. Joseph S. Manser, Jeffrey A. Christians, and Prashant V. Kamat . Intriguing Optoelectronic Properties of Metal Halide Perovskites. Chemical Reviews 2016, 116 (21) , 12956-13008. https://doi.org/10.1021/acs.chemrev.6b00136
    84. Sridevi Krishnan, Vinit Sharma, Prabhakar Singh, and Rampi Ramprasad . Dopants in Lanthanum Manganite: Insights from First-Principles Chemical Space Exploration. The Journal of Physical Chemistry C 2016, 120 (39) , 22126-22133. https://doi.org/10.1021/acs.jpcc.6b04524
    85. Alain C. Vaucher and Markus Reiher . Molecular Propensity as a Driver for Explorative Reactivity Studies. Journal of Chemical Information and Modeling 2016, 56 (8) , 1470-1478. https://doi.org/10.1021/acs.jcim.6b00264
    86. Siddharth Deshpande, John R. Kitchin, and Venkatasubramanian Viswanathan . Quantifying Uncertainty in Activity Volcano Relationships for Oxygen Reduction Reaction. ACS Catalysis 2016, 6 (8) , 5251-5259. https://doi.org/10.1021/acscatal.6b00509
    87. Diptarka Hait, Tianyu Zhu, David P. McMahon, and Troy Van Voorhis . Prediction of Excited-State Energies and Singlet–Triplet Gaps of Charge-Transfer States Using a Restricted Open-Shell Kohn–Sham Approach. Journal of Chemical Theory and Computation 2016, 12 (7) , 3353-3359. https://doi.org/10.1021/acs.jctc.6b00426
    88. Nicolas Chéron, Naveen Jasty, and Eugene I. Shakhnovich . OpenGrowth: An Automated and Rational Algorithm for Finding New Protein Ligands. Journal of Medicinal Chemistry 2016, 59 (9) , 4171-4188. https://doi.org/10.1021/acs.jmedchem.5b00886
    89. Ross E. Larsen . Simple Extrapolation Method To Predict the Electronic Structure of Conjugated Polymers from Calculations on Oligomers. The Journal of Physical Chemistry C 2016, 120 (18) , 9650-9660. https://doi.org/10.1021/acs.jpcc.6b02138
    90. Nitin Kumar and Donald J. Siegel . Interface-Induced Renormalization of Electrolyte Energy Levels in Magnesium Batteries. The Journal of Physical Chemistry Letters 2016, 7 (5) , 874-881. https://doi.org/10.1021/acs.jpclett.6b00091
    91. Lung Wa Chung, W. M. C. Sameera, Romain Ramozzi, Alister J. Page, Miho Hatanaka, Galina P. Petrova, Travis V. Harris, Xin Li, Zhuofeng Ke, Fengyi Liu, Hai-Bei Li, Lina Ding, and Keiji Morokuma . The ONIOM Method and Its Applications. Chemical Reviews 2015, 115 (12) , 5678-5796. https://doi.org/10.1021/cr5004419
    92. Thomas J. Penfold . On Predicting the Excited-State Properties of Thermally Activated Delayed Fluorescence Emitters. The Journal of Physical Chemistry C 2015, 119 (24) , 13535-13544. https://doi.org/10.1021/acs.jpcc.5b03530
    93. Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, and O. Anatole von Lilienfeld . Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach. Journal of Chemical Theory and Computation 2015, 11 (5) , 2087-2096. https://doi.org/10.1021/acs.jctc.5b00099
    94. Pavlo O. Dral, O. Anatole von Lilienfeld, and Walter Thiel . Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations. Journal of Chemical Theory and Computation 2015, 11 (5) , 2120-2125. https://doi.org/10.1021/acs.jctc.5b00141
    95. Olexandr Isayev, Denis Fourches, Eugene N. Muratov, Corey Oses, Kevin Rasch, Alexander Tropsha, and Stefano Curtarolo . Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints. Chemistry of Materials 2015, 27 (3) , 735-743. https://doi.org/10.1021/cm503507h
    96. Lei Cheng, Rajeev S. Assary, Xiaohui Qu, Anubhav Jain, Shyue Ping Ong, Nav Nidhi Rajput, Kristin Persson, and Larry A. Curtiss . Accelerating Electrolyte Discovery for Energy Storage with High-Throughput Screening. The Journal of Physical Chemistry Letters 2015, 6 (2) , 283-291. https://doi.org/10.1021/jz502319n
    97. Wei-Chih Chen and Ito Chao . Molecular Orbital-Based Design of π-Conjugated Organic Materials with Small Internal Reorganization Energy: Generation of Nonbonding Character in Frontier Orbitals. The Journal of Physical Chemistry C 2014, 118 (35) , 20176-20183. https://doi.org/10.1021/jp5056655
    98. Jessica E. Coughlin, Andriy Zhugayevych, Ronald C. Bakus, II, Thomas S. van der Poll, Gregory C. Welch, Simon J. Teat, Guillermo C. Bazan, and Sergei Tretiak . A Combined Experimental and Theoretical Study of Conformational Preferences of Molecular Semiconductors. The Journal of Physical Chemistry C 2014, 118 (29) , 15610-15623. https://doi.org/10.1021/jp506172a
    99. Michael G. Mavros, Takashi Tsuchimochi, Tim Kowalczyk, Alexandra McIsaac, Lee-Ping Wang, and Troy Van Voorhis . What Can Density Functional Theory Tell Us about Artificial Catalytic Water Splitting?. Inorganic Chemistry 2014, 53 (13) , 6386-6397. https://doi.org/10.1021/ic5002557
    100. Bradley D. Rose, Natalie J. Sumner, Alexander S. Filatov, Steven J. Peters, Lev N. Zakharov, Marina A. Petrukhina, and Michael M. Haley . Experimental and Computational Studies of the Neutral and Reduced States of Indeno[1,2-b]fluorene. Journal of the American Chemical Society 2014, 136 (25) , 9181-9189. https://doi.org/10.1021/ja503870z
    Load more citations