In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

Aldossary, Abdulrahman; Campos‐Gonzalez‐Angulo, Jorge Arturo; Pablo‐García, Sergio; Leong, Shi Xuan; Rajaonson, Ella Miray; Thiede, Luca; Tom, Gary; Wang, Andrew; Avagliano, Davide; Aspuru‐Guzik, Alán

doi:10.1002/adma.202402369

Title: In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

Journal Article · 25 May 2024 · Advanced Materials

DOI: https://doi.org/10.1002/adma.202402369 · OSTI ID:2439792

Aldossary, Abdulrahman ^[1]; Campos‐Gonzalez‐Angulo, Jorge Arturo ^[1]; Pablo‐García, Sergio ^[2]; Leong, Shi Xuan ^[1]; Rajaonson, Ella Miray ^[3]; Thiede, Luca ^[4]; Tom, Gary ^[3]; Wang, Andrew ^[1];

^[5];

^[6]

Department of Chemistry University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada
Department of Chemistry University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada, Department of Computer Science University of Toronto 40 St. George Street Toronto ON M5S 2E4 Canada
Department of Chemistry University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada, Vector Institute for Artificial Intelligence 661 University Ave. Suite 710 Toronto ON M5G 1M1 Canada
Department of Computer Science University of Toronto 40 St. George Street Toronto ON M5S 2E4 Canada, Vector Institute for Artificial Intelligence 661 University Ave. Suite 710 Toronto ON M5G 1M1 Canada
Chimie ParisTech, PSL University, CNRS Institute of Chemistry for Life and Health Sciences (iCLeHS UMR 8060) Paris F‐75005 France
Department of Chemistry University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada; Department of Computer Science University of Toronto 40 St. George Street Toronto ON M5S 2E4 Canada; Vector Institute for Artificial Intelligence 661 University Ave. Suite 710 Toronto ON M5G 1M1 Canada; Department of Materials Science & Engineering University of Toronto 184 College St. Toronto ON M5S 3E4 Canada; Department of Chemical Engineering & Applied Chemistry University of Toronto 200 College St. Toronto ON M5S 3E5 Canada

Abstract Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.

View Journal Article

Cite

Export

Save

Sponsoring Organization:: USDOE

OSTI ID:: 2439792

Journal Information:: Advanced Materials, Journal Name: Advanced Materials Journal Issue: 30 Vol. 36; ISSN 0935-9648

Publisher:: Wiley Blackwell (John Wiley & Sons)Copyright Statement

Country of Publication:: Germany

Language:: English

References (395)

Foundation of Multi-Configurational Quantum Chemistry Li Manni, Giovanni; Guther, Kai; Ma, Dongxia Quantum Chemistry and Dynamics of Excited States: Methods and Applications https://doi.org/10.1002/9781119417774.ch6	other	November 2020
Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery Pyzer-Knapp, Edward O.; Li, Kewei; Aspuru-Guzik, Alan Advanced Functional Materials, Vol. 25, Issue 41 https://doi.org/10.1002/adfm.201501919	journal	September 2015
Machine Learning Interatomic Potentials as Emerging Tools for Materials Science Deringer, Volker L.; Caro, Miguel A.; Csányi, Gábor Advanced Materials, Vol. 31, Issue 46 https://doi.org/10.1002/adma.201902765	journal	September 2019
Computational Biomaterials: Computational Simulations for Biomedicine Dai, Xinyue; Chen, Yu Advanced Materials, Vol. 35, Issue 7 https://doi.org/10.1002/adma.202204798	journal	December 2022
Artificial Intelligence and Advanced Materials López, Cefe Advanced Materials, Vol. 35, Issue 23 https://doi.org/10.1002/adma.202208683	journal	April 2023
Deep Transfer Learning: A Fast and Accurate Tool to Predict the Energy Levels of Donor Molecules for Organic Photovoltaics Moore, Gareth John; Bardagot, Olivier; Banerji, Natalie Advanced Theory and Simulations, Vol. 5, Issue 5 https://doi.org/10.1002/adts.202100511	journal	February 2022
Machine learning corrected alchemical perturbation density functional theory for catalysis applications Griego, Charles D.; Zhao, Lingyan; Saravanan, Karthikeyan AIChE Journal, Vol. 66, Issue 12 https://doi.org/10.1002/aic.17041	journal	October 2020
Multiscale Modeling of Biological Functions: From Enzymes to Molecular Machines (Nobel Lecture) Warshel, Arieh Angewandte Chemie International Edition, Vol. 53, Issue 38 https://doi.org/10.1002/anie.201403689	journal	July 2014
Birth and Future of Multiscale Modeling for Macromolecular Systems (Nobel Lecture) Levitt, Michael Angewandte Chemie International Edition, Vol. 53, Issue 38 https://doi.org/10.1002/anie.201403691	journal	August 2014
Development of Multiscale Models for Complex Chemical Systems: From H+H ₂ to Biomolecules (Nobel Lecture) Karplus, Martin Angewandte Chemie International Edition, Vol. 53, Issue 38 https://doi.org/10.1002/anie.201403924	journal	July 2014
Machine Learning for Organic Synthesis: Are Robots Replacing Chemists? Maryasin, Boris; Marquetand, Philipp; Maulide, Nuno Angewandte Chemie International Edition, Vol. 57, Issue 24 https://doi.org/10.1002/anie.201803562	journal	April 2018
Molecular Photochemistry: Recent Developments in Theory Mai, Sebastian; González, Leticia Angewandte Chemie International Edition, Vol. 59, Issue 39 https://doi.org/10.1002/anie.201916381	journal	June 2020
On the eigenfunctions of many-particle systems in quantum mechanics Kato, Tosio Communications on Pure and Applied Mathematics, Vol. 10, Issue 2 https://doi.org/10.1002/cpa.3160100201	journal	January 1957
Progress and Challenges in the Calculation of Electronic Excited States González, Leticia; Escudero, Daniel; Serrano-Andrés, Luis ChemPhysChem, Vol. 13, Issue 1 https://doi.org/10.1002/cphc.201100200	journal	September 2011
Machine learning in materials science Wei, Jing; Chu, Xuan; Sun, Xiang‐Yu InfoMat, Vol. 1, Issue 3 https://doi.org/10.1002/inf2.12028	journal	August 2019
Fast and accurate generation of ab initio quality atomic charges using nonparametric statistical regression Rai, Brajesh K.; Bakken, Gregory A. Journal of Computational Chemistry, Vol. 34, Issue 19 https://doi.org/10.1002/jcc.23308	journal	May 2013
autoCAS : A Program for Fully Automated Multiconfigurational Calculations Stein, Christopher J.; Reiher, Markus Journal of Computational Chemistry https://doi.org/10.1002/jcc.25869	journal	June 2019
How to select active space for multiconfigurational quantum chemistry? Veryazov, Valera; Malmqvist, Per Åke; Roos, Björn O. International Journal of Quantum Chemistry, Vol. 111, Issue 13 https://doi.org/10.1002/qua.23068	journal	April 2011
Hubbard-corrected DFT energy functionals: The LDA+U description of correlated systems Himmetoglu, Burak; Floris, Andrea; de Gironcoli, Stefano International Journal of Quantum Chemistry, Vol. 114, Issue 1 https://doi.org/10.1002/qua.24521	journal	July 2013
Understanding machine-learned density functionals: Understanding Machine-Learned Density Functionals Li, Li; Snyder, John C.; Pelaschier, Isabelle M. International Journal of Quantum Chemistry, Vol. 116, Issue 11 https://doi.org/10.1002/qua.25040	journal	November 2015
Ionization energies of water from PNO-CI calculations Meyer, Wilfried International Journal of Quantum Chemistry, Vol. 5, Issue S5 https://doi.org/10.1002/qua.560050839	journal	January 1971
Semiempirical quantum–chemical methods Thiel, Walter Wiley Interdisciplinary Reviews: Computational Molecular Science, Vol. 4, Issue 2 https://doi.org/10.1002/wcms.1161	journal	July 2013
Machine learning activation energies of chemical reactions Lewis‐Atwell, Toby; Townsend, Piers A.; Grayson, Matthew N. WIREs Computational Molecular Science, Vol. 12, Issue 4 https://doi.org/10.1002/wcms.1593	journal	December 2021
Density functional theory with London dispersion corrections: Density functional theory with London dispersion corrections Grimme, Stefan Wiley Interdisciplinary Reviews: Computational Molecular Science, Vol. 1, Issue 2 https://doi.org/10.1002/wcms.30	journal	March 2011
Kernel Ridge Regression Vovk, Vladimir Empirical Inference https://doi.org/10.1007/978-3-642-41136-6_11	book	January 2013
Support-vector networks Cortes, Corinna; Vapnik, Vladimir Machine Learning, Vol. 20, Issue 3 https://doi.org/10.1007/BF00994018	journal	September 1995
Data Mining with Sparse Grids Garcke, J.; Griebel, M.; Thess, M. Computing, Vol. 67, Issue 3 https://doi.org/10.1007/s006070170007	journal	October 2001
What is semiempirical molecular orbital theory approximating? Margraf, Johannes T.; Dral, Pavlo O. Journal of Molecular Modeling, Vol. 25, Issue 5 https://doi.org/10.1007/s00894-019-4005-8	journal	April 2019
Predicting Chemical Reaction Barriers with a Machine Learning Model Singh, Aayush R.; Rohr, Brian A.; Gauthier, Joseph A. Catalysis Letters, Vol. 149, Issue 9 https://doi.org/10.1007/s10562-019-02705-x	journal	March 2019
Virtual Computational Chemistry Laboratory – Design and Description Tetko, Igor V.; Gasteiger, Johann; Todeschini, Roberto Journal of Computer-Aided Molecular Design, Vol. 19, Issue 6 https://doi.org/10.1007/s10822-005-8694-y	journal	June 2005
A complete active space SCF method (CASSCF) using a density matrix formulated super-CI approach Roos, Björn O.; Taylor, Peter R.; Sigbahn, Per E. M. Chemical Physics, Vol. 48, Issue 2 https://doi.org/10.1016/0301-0104(80)80045-0	journal	May 1980
Are atoms intrinsic to molecular electronic wavefunctions? I. The FORS model Ruedenberg, Klaus; Schmidt, Michael W.; Gilbert, Mary M. Chemical Physics, Vol. 71, Issue 1 https://doi.org/10.1016/0301-0104(82)87004-3	journal	September 1982
Electron transfers in chemistry and biology Marcus, R. A.; Sutin, Norman Biochimica et Biophysica Acta (BBA) - Reviews on Bioenergetics, Vol. 811, Issue 3 https://doi.org/10.1016/0304-4173(85)90014-X	journal	August 1985
Multilayer feedforward networks are universal approximators Hornik, Kurt; Stinchcombe, Maxwell; White, Halbert Neural Networks, Vol. 2, Issue 5 https://doi.org/10.1016/0893-6080(89)90020-8	journal	January 1989
A history of ab initio computational quantum chemistry: 1950–1960 Schaefer, Henry F. Tetrahedron Computer Methodology, Vol. 1, Issue 2 https://doi.org/10.1016/0898-5529(88)90014-0	journal	January 1988
Data-driven acceleration of coupled-cluster and perturbation theory methods Jones, Grier M.; Pathirage, P. D. Varuna S.; Vogiatzis, Konstantinos D. Quantum Chemistry in the Age of Machine Learning, p. 509-529 https://doi.org/10.1016/B978-0-323-90049-2.00007-X	book	January 2023
Elementary Jacobi rotation method for generalized valence bond perfect-pairing calculations combined with simple procedure for generating reliable initial orbitals Sano, T. Journal of Molecular Structure: THEOCHEM, Vol. 528, Issue 1-3 https://doi.org/10.1016/S0166-1280(99)00484-4	journal	August 2000
Theory-Guided Machine Learning Finds Geometric Structure-Property Relationships for Chemisorption on Subsurface Alloys Esterhuizen, Jacques A.; Goldsmith, Bryan R.; Linic, Suljo Chem, Vol. 6, Issue 11 https://doi.org/10.1016/j.chempr.2020.09.001	journal	November 2020
AFLOW: An automatic framework for high-throughput materials discovery Curtarolo, Stefano; Setyawan, Wahyu; Hart, Gus L. W. Computational Materials Science, Vol. 58 https://doi.org/10.1016/j.commatsci.2012.02.005	journal	June 2012
Multi-fidelity machine learning models for accurate bandgap predictions of solids Pilania, G.; Gubernatis, J. E.; Lookman, T. Computational Materials Science, Vol. 129 https://doi.org/10.1016/j.commatsci.2016.12.004	journal	March 2017
Active learning of linearly parametrized interatomic potentials Podryabinkin, Evgeny V.; Shapeev, Alexander V. Computational Materials Science, Vol. 140 https://doi.org/10.1016/j.commatsci.2017.08.031	journal	December 2017
Accelerating high-throughput searches for new alloys with active learning of interatomic potentials Gubaev, Konstantin; Podryabinkin, Evgeny V.; Hart, Gus L. W. Computational Materials Science, Vol. 156 https://doi.org/10.1016/j.commatsci.2018.09.031	journal	January 2019
LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales Thompson, Aidan P.; Aktulga, H. Metin; Berger, Richard Computer Physics Communications, Vol. 271 https://doi.org/10.1016/j.cpc.2021.108171	journal	February 2022
A generalized exchange-correlation functional: the Neural-Networks approach Zheng, Xiao; Hu, LiHong; Wang, XiuJun Chemical Physics Letters, Vol. 390, Issue 1-3 https://doi.org/10.1016/j.cplett.2004.04.020	journal	May 2004
Predicting the HOMO-LUMO gap of benzenoid polycyclic hydrocarbons via interpretable machine learning Zheng, Fengru; Zhu, Zhiwen; Lu, Jiayi Chemical Physics Letters, Vol. 814 https://doi.org/10.1016/j.cplett.2023.140358	journal	March 2023
Multi-fidelity Gaussian process modeling for chemical energy surfaces Wiens, Avery E.; Copan, Andreas V.; Schaefer, Henry F. Chemical Physics Letters, Vol. 737 https://doi.org/10.1016/j.cpletx.2019.100022	journal	January 2019
Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials Thompson, A. P.; Swiler, L. P.; Trott, C. R. Journal of Computational Physics, Vol. 285 https://doi.org/10.1016/j.jcp.2014.12.018	journal	March 2015
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations Raissi, M.; Perdikaris, P.; Karniadakis, G. E. Journal of Computational Physics, Vol. 378 https://doi.org/10.1016/j.jcp.2018.10.045	journal	February 2019
Solving many-electron Schrödinger equation using deep neural networks Han, Jiequn; Zhang, Linfeng; E., Weinan Journal of Computational Physics, Vol. 399 https://doi.org/10.1016/j.jcp.2019.108929	journal	December 2019
Accurate bandgap predictions of solids assisted by machine learning Wang, Tao; Tan, Xiaoxing; Wei, Yadong Materials Today Communications, Vol. 29 https://doi.org/10.1016/j.mtcomm.2021.102932	journal	December 2021
The recent history of theoretical chemistry presented from a method-related perspective Mammino, Liliana Journal of Molecular Structure: THEOCHEM, Vol. 769, Issue 1-3 https://doi.org/10.1016/j.theochem.2006.03.045	journal	September 2006
Learning to Approximate Density Functionals Kalita, Bhupalee; Li, Li; McCarty, Ryan J. Accounts of Chemical Research, Vol. 54, Issue 4 https://doi.org/10.1021/acs.accounts.0c00742	journal	February 2021
Predicting the Thermodynamic Stability of Solids Combining Density Functional Theory and Machine Learning Schmidt, Jonathan; Shi, Jingming; Borlido, Pedro Chemistry of Materials, Vol. 29, Issue 12 https://doi.org/10.1021/acs.chemmater.7b00156	journal	May 2017
Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene Rajan, Arunkumar Chitteth; Mishra, Avanish; Satsangi, Swanti Chemistry of Materials, Vol. 30, Issue 12 https://doi.org/10.1021/acs.chemmater.8b00686	journal	May 2018
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals Chen, Chi; Ye, Weike; Zuo, Yunxing Chemistry of Materials, Vol. 31, Issue 9 https://doi.org/10.1021/acs.chemmater.9b01294	journal	April 2019
Lithium Ion Conduction in Cathode Coating Materials from On-the-Fly Machine Learning Wang, Chuhong; Aoyagi, Koutarou; Wisesa, Pandu Chemistry of Materials, Vol. 32, Issue 9 https://doi.org/10.1021/acs.chemmater.9b04663	journal	April 2020
Machine Learning for Electronically Excited States of Molecules Westermayr, Julia; Marquetand, Philipp Chemical Reviews https://doi.org/10.1021/acs.chemrev.0c00749	journal	November 2020
Four Generations of High-Dimensional Neural Network Potentials Behler, Jörg Chemical Reviews https://doi.org/10.1021/acs.chemrev.0c00868	journal	March 2021
Machine Learning Force Fields Unke, Oliver T.; Chmiela, Stefan; Sauceda, Huziel E. Chemical Reviews https://doi.org/10.1021/acs.chemrev.0c01111	journal	March 2021
Ab Initio Machine Learning in Chemical Compound Space Huang, Bing; von Lilienfeld, O. Anatole Chemical Reviews, Vol. 121, Issue 16 https://doi.org/10.1021/acs.chemrev.0c01303	journal	August 2021
Physics-Inspired Structural Representations for Molecules and Materials Musil, Felix; Grisafi, Andrea; Bartók, Albert P. Chemical Reviews, Vol. 121, Issue 16 https://doi.org/10.1021/acs.chemrev.1c00021	journal	July 2021
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems Keith, John A.; Vassilev-Galindo, Valentin; Cheng, Bingqing Chemical Reviews, Vol. 121, Issue 16 https://doi.org/10.1021/acs.chemrev.1c00107	journal	July 2021
Ab Initio Nonadiabatic Quantum Molecular Dynamics Curchod, Basile F. E.; Martínez, Todd J. Chemical Reviews, Vol. 118, Issue 7 https://doi.org/10.1021/acs.chemrev.7b00423	journal	February 2018
DENVIS: Scalable and High-Throughput Virtual Screening Using Graph Neural Networks with Atomic and Surface Protein Pocket Features Krasoulis, Agamemnon; Antonopoulos, Nick; Pitsikalis, Vassilis Journal of Chemical Information and Modeling, Vol. 62, Issue 19 https://doi.org/10.1021/acs.jcim.2c01057	journal	September 2022
Variational Principles in Quantum Monte Carlo: The Troubled Story of Variance Minimization Cuzzocrea, Alice; Scemama, Anthony; Briels, Wim J. Journal of Chemical Theory and Computation, Vol. 16, Issue 7 https://doi.org/10.1021/acs.jctc.0c00147	journal	May 2020
Noncovalent Quantum Machine Learning Corrections to Density Functionals Mezei, Pál D.; von Lilienfeld, O. Anatole Journal of Chemical Theory and Computation, Vol. 16, Issue 4 https://doi.org/10.1021/acs.jctc.0c00181	journal	March 2020
Incorporating Electronic Information into Machine Learning Potential Energy Surfaces via Approaching the Ground-State Electronic Energy as a Function of Atom-Based Electronic Populations Xie, Xiaowei; Persson, Kristin A.; Small, David W. Journal of Chemical Theory and Computation, Vol. 16, Issue 7 https://doi.org/10.1021/acs.jctc.0c00217	journal	June 2020
Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems Gkeka, Paraskevi; Stoltz, Gabriel; Barati Farimani, Amir Journal of Chemical Theory and Computation, Vol. 16, Issue 8 https://doi.org/10.1021/acs.jctc.0c00355	journal	June 2020
Machine Learning Approaches toward Orbital-free Density Functional Theory: Simultaneous Training on the Kinetic Energy Density Functional and Its Functional Derivative Meyer, Ralf; Weichselbaum, Manuel; Hauser, Andreas W. Journal of Chemical Theory and Computation, Vol. 16, Issue 9 https://doi.org/10.1021/acs.jctc.0c00580	journal	August 2020
A Machine Learning Approach for MP2 Correlation Energies and Its Application to Organic Compounds Han, Ruocheng; Rodríguez-Mayorga, Mauricio; Luber, Sandra Journal of Chemical Theory and Computation, Vol. 17, Issue 2 https://doi.org/10.1021/acs.jctc.0c00898	journal	January 2021
Transferable MP2-Based Machine Learning for Accurate Coupled-Cluster Energies Townsend, Jacob; Vogiatzis, Konstantinos D. Journal of Chemical Theory and Computation, Vol. 16, Issue 12 https://doi.org/10.1021/acs.jctc.0c00927	journal	November 2020
Reinforcement Learning Configuration Interaction Goings, Joshua J.; Hu, Hang; Yang, Chao Journal of Chemical Theory and Computation, Vol. 17, Issue 9 https://doi.org/10.1021/acs.jctc.1c00010	journal	August 2021
A Ranked-Orbital Approach to Select Active Spaces for High-Throughput Multireference Computation King, Daniel S.; Gagliardi, Laura Journal of Chemical Theory and Computation, Vol. 17, Issue 5 https://doi.org/10.1021/acs.jctc.1c00037	journal	April 2021
Searching Configurations in Uncertainty Space: Active Learning of High-Dimensional Neural Network Reactive Potentials Lin, Qidong; Zhang, Liang; Zhang, Yaolong Journal of Chemical Theory and Computation, Vol. 17, Issue 5 https://doi.org/10.1021/acs.jctc.1c00166	journal	April 2021
Chembot: A Machine Learning Approach to Selective Configuration Interaction Pineda Flores, Sergio D. Journal of Chemical Theory and Computation, Vol. 17, Issue 7 https://doi.org/10.1021/acs.jctc.1c00196	journal	June 2021
Machine Learning-Assisted Selection of Active Spaces for Strongly Correlated Transition Metal Systems Golub, Pavlo; Antalik, Andrej; Veis, Libor Journal of Chemical Theory and Computation, Vol. 17, Issue 10 https://doi.org/10.1021/acs.jctc.1c00235	journal	September 2021
Transfer Learning to CCSD(T): Accurate Anharmonic Frequencies from Machine Learning Models Käser, Silvan; Boittier, Eric D.; Upadhyay, Meenu Journal of Chemical Theory and Computation, Vol. 17, Issue 6 https://doi.org/10.1021/acs.jctc.1c00249	journal	May 2021
Active Learning Configuration Interaction for Excited-State Calculations of Polycyclic Aromatic Hydrocarbons Jeong, WooSeok; Gaggioli, Carlo Alberto; Gagliardi, Laura Journal of Chemical Theory and Computation, Vol. 17, Issue 12 https://doi.org/10.1021/acs.jctc.1c00769	journal	November 2021
Toward Orbital-Free Density Functional Theory with Small Data Sets and Deep Learning Ryczko, Kevin; Wetzel, Sebastian J.; Melko, Roger G. Journal of Chemical Theory and Computation, Vol. 18, Issue 2 https://doi.org/10.1021/acs.jctc.1c00812	journal	January 2022
Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-66 Achar, Siddarth K.; Wardzala, Jacob J.; Bernasconi, Leonardo Journal of Chemical Theory and Computation, Vol. 18, Issue 6 https://doi.org/10.1021/acs.jctc.2c00010	journal	June 2022
Machine Learning of Coupled Cluster (T)-Energy Corrections via Delta (Δ)-Learning Ruth, Marcel; Gerbig, Dennis; Schreiner, Peter R. Journal of Chemical Theory and Computation, Vol. 18, Issue 8 https://doi.org/10.1021/acs.jctc.2c00501	journal	July 2022
Δ-Machine Learned Potential Energy Surfaces and Force Fields Bowman, Joel M.; Qu, Chen; Conte, Riccardo Journal of Chemical Theory and Computation, Vol. 19, Issue 1 https://doi.org/10.1021/acs.jctc.2c01034	journal	December 2022
Solving the Schrödinger Equation in the Configuration Space with Generative Machine Learning Herzog, Basile; Casier, Bastien; Lebègue, Sébastien Journal of Chemical Theory and Computation, Vol. 19, Issue 9 https://doi.org/10.1021/acs.jctc.2c01216	journal	April 2023
Ab Initio Molecular Cavity Quantum Electrodynamics Simulations Using Machine Learning Models Hu, Deping; Huo, Pengfei Journal of Chemical Theory and Computation, Vol. 19, Issue 8 https://doi.org/10.1021/acs.jctc.3c00137	journal	March 2023
Machine Learning for Bridging the Gap between Density Functional Theory and Coupled Cluster Energies Ruth, Marcel; Gerbig, Dennis; Schreiner, Peter R. Journal of Chemical Theory and Computation, Vol. 19, Issue 15 https://doi.org/10.1021/acs.jctc.3c00274	journal	July 2023
Low-Data Deep Quantum Chemical Learning for Accurate MP2 and Coupled-Cluster Correlations Ng, Wai-Pan; Liang, Qiujiang; Yang, Jun Journal of Chemical Theory and Computation, Vol. 19, Issue 16 https://doi.org/10.1021/acs.jctc.3c00518	journal	July 2023
Multifidelity Machine Learning for Molecular Excitation Energies Vinod, Vivin; Maity, Sayan; Zaspel, Peter Journal of Chemical Theory and Computation, Vol. 19, Issue 21 https://doi.org/10.1021/acs.jctc.3c00882	journal	October 2023
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach Ramakrishnan, Raghunathan; Dral, Pavlo O.; Rupp, Matthias Journal of Chemical Theory and Computation, Vol. 11, Issue 5 https://doi.org/10.1021/acs.jctc.5b00099	journal	April 2015
Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations Dral, Pavlo O.; von Lilienfeld, O. Anatole; Thiel, Walter Journal of Chemical Theory and Computation, Vol. 11, Issue 5 https://doi.org/10.1021/acs.jctc.5b00141	journal	April 2015
Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules Bereau, Tristan; Andrienko, Denis; von Lilienfeld, O. Anatole Journal of Chemical Theory and Computation, Vol. 11, Issue 7 https://doi.org/10.1021/acs.jctc.5b00301	journal	June 2015
Kinetic Energy of Hydrocarbons as a Function of Electron Density and Convolutional Neural Networks Yao, Kun; Parkhill, John Journal of Chemical Theory and Computation, Vol. 12, Issue 3 https://doi.org/10.1021/acs.jctc.5b01011	journal	February 2016
Automated Selection of Active Orbital Spaces Stein, Christopher J.; Reiher, Markus Journal of Chemical Theory and Computation, Vol. 12, Issue 4 https://doi.org/10.1021/acs.jctc.6b00156	journal	March 2016
Heat-Bath Configuration Interaction: An Efficient Selected Configuration Interaction Algorithm Inspired by Heat-Bath Sampling Holmes, Adam A.; Tubman, Norm M.; Umrigar, C. J. Journal of Chemical Theory and Computation, Vol. 12, Issue 8 https://doi.org/10.1021/acs.jctc.6b00407	journal	August 2016
Automated Construction of Molecular Active Spaces from Atomic Valence Orbitals Sayfutyarova, Elvira R.; Sun, Qiming; Chan, Garnet Kin-Lic Journal of Chemical Theory and Computation, Vol. 13, Issue 9 https://doi.org/10.1021/acs.jctc.7b00128	journal	August 2017
Large-Scale Condensed Matter DFT Simulations: Performance and Capabilities of the CRYSTAL Code Erba, A.; Baima, J.; Bush, I. Journal of Chemical Theory and Computation, Vol. 13, Issue 10 https://doi.org/10.1021/acs.jctc.7b00687	journal	September 2017
Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning Kranz, Julian J.; Kubillus, Maximilian; Ramakrishnan, Raghunathan Journal of Chemical Theory and Computation, Vol. 14, Issue 5 https://doi.org/10.1021/acs.jctc.7b00933	journal	March 2018
Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks Nebgen, Benjamin; Lubbers, Nicholas; Smith, Justin S. Journal of Chemical Theory and Computation, Vol. 14, Issue 9 https://doi.org/10.1021/acs.jctc.8b00524	journal	July 2018
Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited Zaspel, Peter; Huang, Bing; Harbrecht, Helmut Journal of Chemical Theory and Computation, Vol. 15, Issue 3 https://doi.org/10.1021/acs.jctc.8b00832	journal	December 2018
Machine Learning Configuration Interaction Coe, J. P. Journal of Chemical Theory and Computation, Vol. 14, Issue 11 https://doi.org/10.1021/acs.jctc.8b00849	journal	October 2018
A Density Functional Tight Binding Layer for Deep Learning of Chemical Hamiltonians Li, Haichen; Collins, Christopher; Tanha, Matteus Journal of Chemical Theory and Computation, Vol. 14, Issue 11 https://doi.org/10.1021/acs.jctc.8b00873	journal	October 2018
From Molecular Fragments to the Bulk: Development of a Neural Network Potential for MOF-5 Eckhoff, Marco; Behler, Jörg Journal of Chemical Theory and Computation, Vol. 15, Issue 6 https://doi.org/10.1021/acs.jctc.8b01288	journal	April 2019
PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges Unke, Oliver T.; Meuwly, Markus Journal of Chemical Theory and Computation, Vol. 15, Issue 6 https://doi.org/10.1021/acs.jctc.9b00181	journal	April 2019
Machine Learning Configuration Interaction for ab Initio Potential Energy Curves Coe, Jeremy P. Journal of Chemical Theory and Computation, Vol. 15, Issue 11 https://doi.org/10.1021/acs.jctc.9b00828	journal	October 2019
Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation Jeong, WooSeok; Stoneburner, Samuel J.; King, Daniel Journal of Chemical Theory and Computation, Vol. 16, Issue 4 https://doi.org/10.1021/acs.jctc.9b01297	journal	March 2020
Transfer Learning for Drug Discovery Cai, Chenjing; Wang, Shiwei; Xu, Youjun Journal of Medicinal Chemistry, Vol. 63, Issue 16 https://doi.org/10.1021/acs.jmedchem.9b02147	journal	July 2020
Seeking the Optimal Descriptor for S_N2 Reactions through Statistical Analysis of Density Functional Theory Results Morán-González, Lucía; Besora, Maria; Maseras, Feliu The Journal of Organic Chemistry, Vol. 87, Issue 1 https://doi.org/10.1021/acs.joc.1c02387	journal	December 2021
Simpler is Better: How Linear Prediction Tasks Improve Transfer Learning in Chemical Autoencoders Iovanac, Nicolae C.; Savoie, Brett M. The Journal of Physical Chemistry A, Vol. 124, Issue 18 https://doi.org/10.1021/acs.jpca.0c00042	journal	April 2020
Bayesian Optimization for Calibrating and Selecting Hybrid-Density Functional Models Vargas−Hernández, R. A. The Journal of Physical Chemistry A, Vol. 124, Issue 20 https://doi.org/10.1021/acs.jpca.0c01375	journal	April 2020
Machine-Learning Coupled Cluster Properties through a Density Tensor Representation Peyton, Benjamin G.; Briggs, Connor; D’Cunha, Ruhee The Journal of Physical Chemistry A, Vol. 124, Issue 23 https://doi.org/10.1021/acs.jpca.0c02804	journal	May 2020
Multifidelity Statistical Machine Learning for Molecular Crystal Structure Prediction Egorova, Olga; Hafizi, Roohollah; Woods, David C. The Journal of Physical Chemistry A, Vol. 124, Issue 39 https://doi.org/10.1021/acs.jpca.0c05006	journal	September 2020
Machine Learning Interatomic Potentials and Long-Range Physics Anstine, Dylan M.; Isayev, Olexandr The Journal of Physical Chemistry A, Vol. 127, Issue 11 https://doi.org/10.1021/acs.jpca.2c06778	journal	February 2023
Efficient Machine Learning Configuration Interaction for Bond Breaking Problems Rano, Madhumita; Ghosh, Debashree The Journal of Physical Chemistry A, Vol. 127, Issue 16 https://doi.org/10.1021/acs.jpca.2c09103	journal	April 2023
Machine Learning the Quantum Mechanical Wave Function Dey, Mandira; Ghosh, Debashree The Journal of Physical Chemistry A, Vol. 127, Issue 44 https://doi.org/10.1021/acs.jpca.3c05322	journal	October 2023
Making the Coupled Cluster Correlation Energy Machine-Learnable Margraf, Johannes T.; Reuter, Karsten The Journal of Physical Chemistry A, Vol. 122, Issue 30 https://doi.org/10.1021/acs.jpca.8b04455	journal	July 2018
Performance and Cost Assessment of Machine Learning Interatomic Potentials Zuo, Yunxing; Chen, Chi; Li, Xiangguo The Journal of Physical Chemistry A, Vol. 124, Issue 4 https://doi.org/10.1021/acs.jpca.9b08723	journal	October 2019
Artificial Neural Network Potential for Encapsulated Platinum Clusters in MOF-808 Yu, Yangyang; Zhang, Weiwei; Mei, Donghai The Journal of Physical Chemistry C, Vol. 126, Issue 2 https://doi.org/10.1021/acs.jpcc.1c09178	journal	January 2022
Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics Westermayr, Julia; Gastegger, Michael; Marquetand, Philipp The Journal of Physical Chemistry Letters, Vol. 11, Issue 10 https://doi.org/10.1021/acs.jpclett.0c00527	journal	April 2020
Fast Estimation of Møller–Plesset Correlation Energies Based on Atomic Contributions Han, R.; Luber, S. The Journal of Physical Chemistry Letters, Vol. 12, Issue 22 https://doi.org/10.1021/acs.jpclett.1c00900	journal	June 2021
Machine Learning Force Fields: Recent Advances and Remaining Challenges Poltavsky, Igor; Tkatchenko, Alexandre The Journal of Physical Chemistry Letters, Vol. 12, Issue 28 https://doi.org/10.1021/acs.jpclett.1c01204	journal	July 2021
Machine-Learned Electronically Excited States with the MolOrbImage Generated from the Molecular Ground State Chen, Ziyong; Yam, Vivian Wing-Wah The Journal of Physical Chemistry Letters, Vol. 14, Issue 7 https://doi.org/10.1021/acs.jpclett.3c00014	journal	February 2023
Data-Driven Refinement of Electronic Energies from Two-Electron Reduced-Density-Matrix Theory Jones, Grier M.; Li, Run R.; DePrince, A. Eugene The Journal of Physical Chemistry Letters, Vol. 14, Issue 28 https://doi.org/10.1021/acs.jpclett.3c01382	journal	July 2023
Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan The Journal of Physical Chemistry Letters, Vol. 6, Issue 12 https://doi.org/10.1021/acs.jpclett.5b00831	journal	June 2015
Predicting the Band Gaps of Inorganic Solids by Machine Learning Zhuo, Ya; Mansouri Tehrani, Aria; Brgoch, Jakoah The Journal of Physical Chemistry Letters, Vol. 9, Issue 7 https://doi.org/10.1021/acs.jpclett.8b00124	journal	March 2018
Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation Hu, Deping; Xie, Yu; Li, Xusong The Journal of Physical Chemistry Letters, Vol. 9, Issue 11 https://doi.org/10.1021/acs.jpclett.8b00684	journal	May 2018
Discovering a Transferable Charge Assignment Model Using Machine Learning Sifain, Andrew E.; Lubbers, Nicholas; Nebgen, Benjamin T. The Journal of Physical Chemistry Letters, Vol. 9, Issue 16 https://doi.org/10.1021/acs.jpclett.8b01939	journal	July 2018
Nonadiabatic Excited-State Dynamics with Machine Learning Dral, Pavlo O.; Barbatti, Mario; Thiel, Walter The Journal of Physical Chemistry Letters, Vol. 9, Issue 19 https://doi.org/10.1021/acs.jpclett.8b02469	journal	September 2018
Data-Driven Acceleration of the Coupled-Cluster Singles and Doubles Iterative Solver Townsend, Jacob; Vogiatzis, Konstantinos D. The Journal of Physical Chemistry Letters, Vol. 10, Issue 14 https://doi.org/10.1021/acs.jpclett.9b01442	journal	June 2019
Multifidelity Information Fusion with Machine Learning: A Case Study of Dopant Formation Energies in Hafnia Batra, Rohit; Pilania, Ghanshyam; Uberuaga, Blas P. ACS Applied Materials & Interfaces, Vol. 11, Issue 28 https://doi.org/10.1021/acsami.9b02174	journal	April 2019
Open Catalyst 2020 (OC20) Dataset and Community Challenges Chanussot, Lowik; Das, Abhishek; Goyal, Siddharth ACS Catalysis, Vol. 11, Issue 10 https://doi.org/10.1021/acscatal.0c04525	journal	May 2021
Generalizing Performance Equations in Heterogeneous Catalysis from Hybrid Data and Statistical Learning Pablo-García, Sergio; Sabadell-Rendón, Albert; Saadun, Ali J. ACS Catalysis, Vol. 12, Issue 2 https://doi.org/10.1021/acscatal.1c04345	journal	January 2022
The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts Tran, Richard; Lan, Janice; Shuaibi, Muhammed ACS Catalysis, Vol. 13, Issue 5 https://doi.org/10.1021/acscatal.2c05426	journal	February 2023
Phoenics: A Bayesian Optimizer for Chemistry Häse, Florian; Roch, Loïc M.; Kreisbeck, Christoph ACS Central Science, Vol. 4, Issue 9 https://doi.org/10.1021/acscentsci.8b00307	journal	August 2018
Density Functionals with Broad Applicability in Chemistry Zhao, Yan; Truhlar, Donald G. Accounts of Chemical Research, Vol. 41, Issue 2 https://doi.org/10.1021/ar700111a	journal	February 2008
Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17 Ruddigkeit, Lars; van Deursen, Ruud; Blum, Lorenz C. Journal of Chemical Information and Modeling, Vol. 52, Issue 11 https://doi.org/10.1021/ci300415d	journal	November 2012
Fluorescence Lifetime Measurements and Biological Imaging Berezin, Mikhail Y.; Achilefu, Samuel Chemical Reviews, Vol. 110, Issue 5 https://doi.org/10.1021/cr900343z	journal	May 2010
Scalable Electron Correlation Methods I.: PNO-LMP2 with Linear Scaling in the Molecular Size and Near-Inverse-Linear Scaling in the Number of Processors Werner, Hans-Joachim; Knizia, Gerald; Krause, Christine Journal of Chemical Theory and Computation, Vol. 11, Issue 2 https://doi.org/10.1021/ct500725e	journal	January 2015
Marcus Theory: Thermodynamics CAN Control the Kinetics of Electron Transfer Reactions Silverstein, Todd P. Journal of Chemical Education, Vol. 89, Issue 9 https://doi.org/10.1021/ed1007712	journal	July 2012
Ab Initio Calculation of Vibrational Absorption and Circular Dichroism Spectra Using Density Functional Force Fields Stephens, P. J.; Devlin, F. J.; Chabalowski, C. F. The Journal of Physical Chemistry, Vol. 98, Issue 45, p. 11623-11627 https://doi.org/10.1021/j100096a001	journal	November 1994
Second-order perturbation theory with a CASSCF reference function Andersson, Kerstin.; Malmqvist, Per Aake.; Roos, Bjoern O. The Journal of Physical Chemistry, Vol. 94, Issue 14 https://doi.org/10.1021/j100377a012	journal	July 1990
The Effect of Structure upon the Reactions of Organic Compounds. Benzene Derivatives Hammett, Louis P. Journal of the American Chemical Society, Vol. 59, Issue 1 https://doi.org/10.1021/ja01280a022	journal	January 1937
970 Million Druglike Small Molecules for Virtual Screening in the Chemical Universe Database GDB-13 Blum, Lorenz C.; Reymond, Jean-Louis Journal of the American Chemical Society, Vol. 131, Issue 25 https://doi.org/10.1021/ja902302h	journal	July 2009
Machine-Learning Photodynamics Simulations Uncover the Role of Substituent Effects on the Photochemical Formation of Cubanes Li, Jingbai; Stein, Rachel; Adrion, Daniel M. Journal of the American Chemical Society, Vol. 143, Issue 48 https://doi.org/10.1021/jacs.1c07725	journal	November 2021
Encoding Hole–Particle Information in the Multi-Channel MolOrbImage for Machine-Learned Excited-State Energies of Large Photofunctional Materials Chen, Ziyong; Wing-Wah Yam, Vivian Journal of the American Chemical Society, Vol. 145, Issue 44 https://doi.org/10.1021/jacs.3c07766	journal	October 2023
Calibrating DFT Formation Enthalpy Calculations by Multifidelity Machine Learning Gong, Sheng; Wang, Shuo; Xie, Tian JACS Au, Vol. 2, Issue 9 https://doi.org/10.1021/jacsau.2c00235	journal	September 2022
Improving the Accuracy of Density-Functional Theory Calculation: The Statistical Correction Approach Wang, XiuJung; Wong, LaiHo; Hu, LiHong The Journal of Physical Chemistry A, Vol. 108, Issue 40 https://doi.org/10.1021/jp047263q	journal	October 2004
General Performance of Density Functionals Sousa, Sérgio Filipe; Fernandes, Pedro Alexandrino; Ramos, Maria João The Journal of Physical Chemistry A, Vol. 111, Issue 42 https://doi.org/10.1021/jp0734474	journal	October 2007
Learning representations by back-propagating errors Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. Nature, Vol. 323, Issue 6088 https://doi.org/10.1038/323533a0	journal	October 1986
Chemical space Kirkpatrick, Peter; Ellis, Clare Nature, Vol. 432, Issue 7019 https://doi.org/10.1038/432823a	journal	December 2004
Navigating chemical space for biology and medicine Lipinski, Christopher; Hopkins, Andrew Nature, Vol. 432, Issue 7019 https://doi.org/10.1038/nature03193	journal	December 2004
Deep learning LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey Nature, Vol. 521, Issue 7553 https://doi.org/10.1038/nature14539	journal	May 2015
Quantum-chemical insights from deep tensor neural networks Schütt, Kristof T.; Arbabzadah, Farhad; Chmiela, Stefan Nature Communications, Vol. 8, Issue 1 https://doi.org/10.1038/ncomms13890	journal	January 2017
To address surface reaction network complexity using scaling relations machine learning and DFT calculations Ulissi, Zachary W.; Medford, Andrew J.; Bligaard, Thomas Nature Communications, Vol. 8, Issue 1 https://doi.org/10.1038/ncomms14621	journal	March 2017
Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach Gómez-Bombarelli, Rafael; Aguilera-Iparraguirre, Jorge; Hirzel, Timothy D. Nature Materials, Vol. 15, Issue 10 https://doi.org/10.1038/nmat4717	journal	August 2016
The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies Kirklin, Scott; Saal, James E.; Meredig, Bryce npj Computational Materials, Vol. 1, Issue 1 https://doi.org/10.1038/npjcompumats.2015.10	journal	December 2015
Bypassing the Kohn-Sham equations with machine learning Brockherde, Felix; Vogt, Leslie; Li, Li Nature Communications, Vol. 8, Issue 1 https://doi.org/10.1038/s41467-017-00839-3	journal	October 2017
Towards exact molecular dynamics simulations with machine-learned force fields Chmiela, Stefan; Sauceda, Huziel E.; Müller, Klaus-Robert Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-06169-2	journal	September 2018
Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning Smith, Justin S.; Nebgen, Benjamin T.; Zubatyuk, Roman Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-10827-4	journal	July 2019
Revealing the intrinsic nature of the mid-gap defects in amorphous Ge2Sb2Te5 Konstantinou, Konstantinos; Mocanu, Felix C.; Lee, Tae-Hoon Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-10980-w	journal	July 2019
Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals García-Muelas, Rodrigo; López, Núria Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-12709-1	journal	October 2019
Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions Schütt, K. T.; Gastegger, M.; Tkatchenko, A. Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-12875-2	journal	November 2019
Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning Jha, Dipendra; Choudhary, Kamal; Tavazza, Francesca Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-13297-w	journal	November 2019
Machine learning accurate exchange and correlation functionals of the electronic density Dick, Sebastian; Fernandez-Serra, Marivi Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-17265-7	journal	July 2020
Designing and understanding light-harvesting devices with machine learning Häse, Florian; Roch, Loïc M.; Friederich, Pascal Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-17995-8	journal	September 2020
Quantum chemical accuracy from density functional approximations via machine learning Bogojeski, Mihail; Vogt-Maranto, Leslie; Tuckerman, Mark E. Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-19093-1	journal	October 2020
A general-purpose machine-learning force field for bulk and nanostructured phosphorus Deringer, Volker L.; Caro, Miguel A.; Csányi, Gábor Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-19168-z	journal	October 2020
Predicting materials properties without crystal structure: deep representation learning from stoichiometry Goodall, Rhys E. A.; Lee, Alpha A. Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-19964-7	journal	December 2020
A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer Ko, Tsz Wai; Finkler, Jonas A.; Goedecker, Stefan Nature Communications, Vol. 12, Issue 1 https://doi.org/10.1038/s41467-020-20427-2	journal	January 2021
Pure non-local machine-learned density functional theory for electron correlation Margraf, Johannes T.; Reuter, Karsten Nature Communications, Vol. 12, Issue 1 https://doi.org/10.1038/s41467-020-20471-y	journal	January 2021
Teaching a neural network to attach and detach electrons from molecules Zubatyuk, Roman; Smith, Justin S.; Nebgen, Benjamin T. Nature Communications, Vol. 12, Issue 1 https://doi.org/10.1038/s41467-021-24904-0	journal	August 2021
Artificial intelligence-enhanced quantum chemical method with broad applicability Zheng, Peikun; Zubatyuk, Roman; Wu, Wei Nature Communications, Vol. 12, Issue 1 https://doi.org/10.1038/s41467-021-27340-2	journal	December 2021
SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects Unke, Oliver T.; Chmiela, Stefan; Gastegger, Michael Nature Communications, Vol. 12, Issue 1 https://doi.org/10.1038/s41467-021-27504-0	journal	December 2021
Self-consistent determination of long-range electrostatics in neural network potentials Gao, Ang; Remsing, Richard C. Nature Communications, Vol. 13, Issue 1 https://doi.org/10.1038/s41467-022-29243-2	journal	March 2022
E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials Batzner, Simon; Musaelian, Albert; Sun, Lixin Nature Communications, Vol. 13, Issue 1 https://doi.org/10.1038/s41467-022-29939-5	journal	May 2022
Excited state non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential Axelrod, Simon; Shakhnovich, Eugene; Gómez-Bombarelli, Rafael Nature Communications, Vol. 13, Issue 1 https://doi.org/10.1038/s41467-022-30999-w	journal	June 2022
BIGDML—Towards accurate quantum machine learning force fields for materials Sauceda, Huziel E.; Gálvez-González, Luis E.; Chmiela, Stefan Nature Communications, Vol. 13, Issue 1 https://doi.org/10.1038/s41467-022-31093-x	journal	June 2022
Machine learning the Hohenberg-Kohn map for molecular excited states Bai, Yuanming; Vogt-Maranto, Leslie; Tuckerman, Mark E. Nature Communications, Vol. 13, Issue 1 https://doi.org/10.1038/s41467-022-34436-w	journal	November 2022
Electronic excited states in deep variational Monte Carlo Entwistle, M. T.; Schätzle, Z.; Erdman, P. A. Nature Communications, Vol. 14, Issue 1 https://doi.org/10.1038/s41467-022-35534-5	journal	January 2023
Learning local equivariant representations for large-scale atomistic dynamics Musaelian, Albert; Batzner, Simon; Johansson, Anders Nature Communications, Vol. 14, Issue 1 https://doi.org/10.1038/s41467-023-36329-y	journal	February 2023
Towards the ground state of molecules via diffusion Monte Carlo on neural networks Ren, Weiluo; Fu, Weizhong; Wu, Xiaojie Nature Communications, Vol. 14, Issue 1 https://doi.org/10.1038/s41467-023-37609-3	journal	April 2023
Efficient interatomic descriptors for accurate machine learning force fields of extended molecules Kabylda, Adil; Vassilev-Galindo, Valentin; Chmiela, Stefan Nature Communications, Vol. 14, Issue 1 https://doi.org/10.1038/s41467-023-39214-w	journal	June 2023
Machine learning coarse-grained potentials of protein thermodynamics Majewski, Maciej; Pérez, Adrià; Thölke, Philipp Nature Communications, Vol. 14, Issue 1 https://doi.org/10.1038/s41467-023-41343-1	journal	September 2023
Towards a transferable fermionic neural wavefunction for molecules Scherbela, Michael; Gerard, Leon; Grohs, Philipp Nature Communications, Vol. 15, Issue 1 https://doi.org/10.1038/s41467-023-44216-9	journal	January 2024
Solving the electronic structure problem with machine learning Chandrasekaran, Anand; Kamal, Deepak; Batra, Rohit npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0162-7	journal	February 2019
An electrostatic spectral neighbor analysis potential for lithium nitride Deng, Zhi; Chen, Chi; Li, Xiang-Guo npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0212-1	journal	July 2019
De novo exploration and self-guided learning of potential-energy surfaces Bernstein, Noam; Csányi, Gábor; Deringer, Volker L. npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0236-6	journal	October 2019
Machine learning enabled autonomous microstructural characterization in 3D samples Chan, Henry; Cherukara, Mathew; Loeffler, Troy D. npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-019-0267-z	journal	January 2020
Machine learning the Hubbard U parameter in DFT+U using Bayesian optimization Yu, Maituo; Yang, Shuyang; Wu, Chunzhi npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-00446-9	journal	November 2020
Completing density functional theory by machine learning hidden messages from molecules Nagai, Ryo; Akashi, Ryosuke; Sugino, Osamu npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-0310-0	journal	May 2020
Materials property prediction for limited datasets enabled by feature selection and joint learning with MODNet De Breuck, Pierre-Paul; Hautier, Geoffroy; Rignanese, Gian-Marco npj Computational Materials, Vol. 7, Issue 1 https://doi.org/10.1038/s41524-021-00552-2	journal	June 2021
Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials Dai, Minyi; Demirel, Mehmet F.; Liang, Yingyu npj Computational Materials, Vol. 7, Issue 1 https://doi.org/10.1038/s41524-021-00574-w	journal	July 2021
Atomistic Line Graph Neural Network for improved materials property predictions Choudhary, Kamal; DeCost, Brian npj Computational Materials, Vol. 7, Issue 1 https://doi.org/10.1038/s41524-021-00650-1	journal	November 2021
Two-dimensional Stiefel-Whitney insulators in liganded Xenes Pan, Mingxiang; Li, Dexin; Fan, Jiahao npj Computational Materials, Vol. 8, Issue 1 https://doi.org/10.1038/s41524-021-00695-2	journal	January 2022
Data-driven models for ground and excited states for Single Atoms on Ceria Geiger, Julian; Sabadell-Rendón, Albert; Daelman, Nathan npj Computational Materials, Vol. 8, Issue 1 https://doi.org/10.1038/s41524-022-00852-1	journal	August 2022
The NOMAD Artificial-Intelligence Toolkit: turning materials-science data into knowledge and understanding Sbailò, Luigi; Fekete, Ádám; Ghiringhelli, Luca M. npj Computational Materials, Vol. 8, Issue 1 https://doi.org/10.1038/s41524-022-00935-z	journal	December 2022
Computational design for 4D printing of topology optimized multi-material active composites Athinarayanarao, Darshan; Prod’hon, Romaric; Chamoret, Dominique npj Computational Materials, Vol. 9, Issue 1 https://doi.org/10.1038/s41524-022-00962-w	journal	January 2023
Machine learning potentials for metal-organic frameworks using an incremental learning approach Vandenhaute, Sander; Cools-Ceuppens, Maarten; DeKeyser, Simon npj Computational Materials, Vol. 9, Issue 1 https://doi.org/10.1038/s41524-023-00969-x	journal	February 2023
Machine learned force-fields for an Ab-initio quality description of metal-organic frameworks Wieser, Sandro; Zojer, Egbert npj Computational Materials, Vol. 10, Issue 1 https://doi.org/10.1038/s41524-024-01205-w	journal	January 2024
Deep-neural-network solution of the electronic Schrödinger equation Hermann, Jan; Schätzle, Zeno; Noé, Frank Nature Chemistry, Vol. 12, Issue 10 https://doi.org/10.1038/s41557-020-0544-y	journal	September 2020
Deep learning study of tyrosine reveals that roaming can lead to photodamage Westermayr, Julia; Gastegger, Michael; Vörös, Dóra Nature Chemistry, Vol. 14, Issue 8 https://doi.org/10.1038/s41557-022-00950-z	journal	June 2022
Living in a brave new AI era Nature Human Behaviour, Vol. 7, Issue 11, p. 1799-1799 https://doi.org/10.1038/s41562-023-01775-7	journal	November 2023
Discovering and understanding materials through computation Louie, Steven G.; Chan, Yang-Hao; da Jornada, Felipe H. Nature Materials, Vol. 20, Issue 6 https://doi.org/10.1038/s41563-021-01015-1	journal	May 2021
Free electron to electride transition in dense liquid potassium Zong, Hongxiang; Robinson, Victor Naden; Hermann, Andreas Nature Physics, Vol. 17, Issue 8 https://doi.org/10.1038/s41567-021-01244-w	journal	May 2021
Molecular excited states through a machine learning lens Dral, Pavlo O.; Barbatti, Mario Nature Reviews Chemistry, Vol. 5, Issue 6 https://doi.org/10.1038/s41570-021-00278-1	journal	May 2021
Ab initio quantum chemistry with neural-network wavefunctions Hermann, Jan; Spencer, James; Choo, Kenny Nature Reviews Chemistry, Vol. 7, Issue 10 https://doi.org/10.1038/s41570-023-00516-8	journal	August 2023
Origins of structural and electronic transitions in disordered silicon Deringer, Volker L.; Bernstein, Noam; Csányi, Gábor Nature, Vol. 589, Issue 7840 https://doi.org/10.1038/s41586-020-03072-z	journal	January 2021
Accelerated discovery of CO2 electrocatalysts using active machine learning Zhong, Miao; Tran, Kevin; Min, Yimeng Nature, Vol. 581, Issue 7807 https://doi.org/10.1038/s41586-020-2242-8	journal	May 2020
Evidence for supercritical behaviour of high-pressure liquid hydrogen Cheng, Bingqing; Mazzola, Guglielmo; Pickard, Chris J. Nature, Vol. 585, Issue 7824 https://doi.org/10.1038/s41586-020-2677-y	journal	September 2020
Bayesian reaction optimization as a tool for chemical synthesis Shields, Benjamin J.; Stevens, Jason; Li, Jun Nature, Vol. 590, Issue 7844 https://doi.org/10.1038/s41586-021-03213-y	journal	February 2021
Autonomous chemical research with large language models Boiko, Daniil A.; MacKnight, Robert; Kline, Ben Nature, Vol. 624, Issue 7992 https://doi.org/10.1038/s41586-023-06792-0	journal	December 2023
Neural network interpolation of exchange-correlation functional Ryabov, Alexander; Akhatov, Iskander; Zhilyaev, Petr Scientific Reports, Vol. 10, Issue 1 https://doi.org/10.1038/s41598-020-64619-8	journal	May 2020
Moving closer to experimental level materials property prediction using AI Jha, Dipendra; Gupta, Vishu; Liao, Wei-keng Scientific Reports, Vol. 12, Issue 1 https://doi.org/10.1038/s41598-022-15816-0	journal	July 2022
Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution Tran, Kevin; Ulissi, Zachary W. Nature Catalysis, Vol. 1, Issue 9 https://doi.org/10.1038/s41929-018-0142-1	journal	September 2018
Interpretable machine learning for knowledge generation in heterogeneous catalysis Esterhuizen, Jacques A.; Goldsmith, Bryan R.; Linic, Suljo Nature Catalysis, Vol. 5, Issue 3 https://doi.org/10.1038/s41929-022-00744-z	journal	March 2022
On scientific understanding with artificial intelligence Krenn, Mario; Pollice, Robert; Guo, Si Yue Nature Reviews Physics, Vol. 4, Issue 12 https://doi.org/10.1038/s42254-022-00518-3	journal	October 2022
Advancing molecular simulation with equivariant interatomic potentials Batzner, Simon; Musaelian, Albert; Kozinsky, Boris Nature Reviews Physics, Vol. 5, Issue 8 https://doi.org/10.1038/s42254-023-00615-x	journal	July 2023
Learning properties of ordered and disordered materials from multi-fidelity data Chen, Chi; Zuo, Yunxing; Ye, Weike Nature Computational Science, Vol. 1, Issue 1 https://doi.org/10.1038/s43588-020-00002-x	journal	January 2021
Solving the electronic Schrödinger equation for multiple nuclear geometries with weight-sharing deep neural networks Scherbela, Michael; Reisenhofer, Rafael; Gerard, Leon Nature Computational Science, Vol. 2, Issue 5 https://doi.org/10.1038/s43588-022-00228-x	journal	May 2022
Fast evaluation of the adsorption energy of organic molecules on metals via graph neural networks Pablo-García, Sergio; Morandi, Santiago; Vargas-Hernández, Rodrigo A. Nature Computational Science, Vol. 3, Issue 5 https://doi.org/10.1038/s43588-023-00437-y	journal	May 2023
The Harvard organic photovoltaic dataset Lopez, Steven A.; Pyzer-Knapp, Edward O.; Simm, Gregor N. Scientific Data, Vol. 3, Issue 1 https://doi.org/10.1038/sdata.2016.86	journal	September 2016
Machine-learned approximations to Density Functional Theory Hamiltonians Hegde, Ganesh; Bowen, R. Chris Scientific Reports, Vol. 7, Issue 1 https://doi.org/10.1038/srep42669	journal	February 2017
Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry – the Harvard Clean Energy Project Hachmann, Johannes; Olivares-Amaya, Roberto; Jinich, Adrian Energy Environ. Sci., Vol. 7, Issue 2 https://doi.org/10.1039/C3EE42756K	journal	January 2014
A Bayesian approach to calibrating high-throughput virtual screening results and application to organic photovoltaic materials Pyzer-Knapp, Edward O.; Simm, Gregor N.; Aspuru Guzik, Alán Materials Horizons, Vol. 3, Issue 3 https://doi.org/10.1039/C5MH00282F	journal	January 2016
Machine learning exciton dynamics Häse, Florian; Valleau, Stéphanie; Pyzer-Knapp, Edward Chemical Science, Vol. 7, Issue 8 https://doi.org/10.1039/C5SC04786B	journal	January 2016
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost Smith, J. S.; Isayev, O.; Roitberg, A. E. Chemical Science, Vol. 8, Issue 4 https://doi.org/10.1039/C6SC05720A	journal	January 2017
Predicting electronic structure properties of transition metal complexes with neural networks Janet, Jon Paul; Kulik, Heather J. Chemical Science, Vol. 8, Issue 7 https://doi.org/10.1039/C7SC01247K	journal	January 2017
MoleculeNet: a benchmark for molecular machine learning Wu, Zhenqin; Ramsundar, Bharath; Feinberg, Evan N. Chemical Science, Vol. 9, Issue 2 https://doi.org/10.1039/C7SC02664A	journal	January 2018
The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics Yao, Kun; Herr, John E.; Toth, David W. Chemical Science, Vol. 9, Issue 8 https://doi.org/10.1039/C7SC04934J	journal	January 2018
Application of DFT-based machine learning for developing molecular electrode materials in Li-ion batteries Allam, Omar; Cho, Byung Woo; Kim, Ki Chul RSC Advances, Vol. 8, Issue 69 https://doi.org/10.1039/C8RA07112H	journal	January 2018
Machine learning meets volcano plots: computational discovery of cross-coupling catalysts Meyer, Benjamin; Sawatlon, Boodsarin; Heinen, Stefan Chemical Science, Vol. 9, Issue 35 https://doi.org/10.1039/C8SC01949E	journal	January 2018
Constrained Bayesian optimization for automatic chemical design using variational autoencoders Griffiths, Ryan-Rhys; Hernández-Lobato, José Miguel Chemical Science, Vol. 11, Issue 2 https://doi.org/10.1039/C9SC04026A	journal	January 2020
Predicting the phase diagram of titanium dioxide with random search and pattern recognition Reinhardt, Aleks; Pickard, Chris J.; Cheng, Bingqing Physical Chemistry Chemical Physics, Vol. 22, Issue 22 https://doi.org/10.1039/D0CP02513E	journal	January 2020
QSAR without borders Muratov, Eugene N.; Bajorath, Jürgen; Sheridan, Robert P. Chemical Society Reviews, Vol. 49, Issue 11 https://doi.org/10.1039/D0CS00098A	journal	January 2020
Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization Maley, Steven M.; Kwon, Doo-Hyun; Rollins, Nick Chemical Science, Vol. 11, Issue 35 https://doi.org/10.1039/D0SC03552A	journal	January 2020
Data-powered augmented volcano plots for homogeneous catalysis Wodrich, Matthew D.; Fabrizio, Alberto; Meyer, Benjamin Chemical Science, Vol. 11, Issue 44 https://doi.org/10.1039/D0SC04289G	journal	January 2020
Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies Jorner, Kjell; Brinck, Tore; Norrby, Per-Ola Chemical Science, Vol. 12, Issue 3 https://doi.org/10.1039/D0SC04896H	journal	January 2021
Automatic discovery of photoisomerization mechanisms with nanosecond machine learning photodynamics simulations Li, Jingbai; Reiser, Patrick; Boswell, Benjamin R. Chemical Science, Vol. 12, Issue 14 https://doi.org/10.1039/D0SC05610C	journal	January 2021
Data-efficient machine learning for molecular crystal structure prediction Wengert, Simon; Csányi, Gábor; Reuter, Karsten Chemical Science, Vol. 12, Issue 12 https://doi.org/10.1039/D0SC05765G	journal	January 2021
Modern multireference methods and their application in transition metal chemistry Khedkar, Abhishek; Roemelt, Michael Physical Chemistry Chemical Physics, Vol. 23, Issue 32 https://doi.org/10.1039/D1CP02640B	journal	January 2021
Choosing the right molecular machine learning potential Pinheiro, Max; Ge, Fuchun; Ferré, Nicolas Chemical Science, Vol. 12, Issue 43 https://doi.org/10.1039/D1SC03564A	journal	January 2021
Δ-Quantum machine-learning for medicinal chemistry Atz, Kenneth; Isert, Clemens; Böcker, Markus N. A. Physical Chemistry Chemical Physics, Vol. 24, Issue 18 https://doi.org/10.1039/D2CP00834C	journal	January 2022
DFT exchange: sharing perspectives on the workhorse of quantum chemistry and materials science Teale, Andrew M.; Helgaker, Trygve; Savin, Andreas Physical Chemistry Chemical Physics, Vol. 24, Issue 47 https://doi.org/10.1039/D2CP02827A	journal	January 2022
NewtonNet: A Newtonian message passing network for deep learning of interatomic potentials and forces Haghighatlari, Mojtaba; Li, Jie; Guan, Xingyi Digital Discovery https://doi.org/10.1039/D2DD00008C	journal	January 2022
Neural network potentials for chemistry: concepts, applications and prospects Käser, Silvan; Vazquez-Salazar, Luis Itza; Meuwly, Markus Digital Discovery, Vol. 2, Issue 1 https://doi.org/10.1039/D2DD00102K	journal	January 2023
Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS Tom, Gary; Hickman, Riley J.; Zinzuwadia, Aniket Digital Discovery, Vol. 2, Issue 3 https://doi.org/10.1039/D2DD00146B	journal	January 2023
Selected machine learning of HOMO–LUMO gaps with improved data-efficiency Mazouin, Bernard; Schöpfer, Alexandre Alain; von Lilienfeld, O. Anatole Materials Advances, Vol. 3, Issue 22 https://doi.org/10.1039/D2MA00742H	journal	January 2022
The resistive nature of decomposing interfaces of solid electrolytes with alkali metal electrodes Wang, Juefan; Panchal, Abhishek A.; Sai Gautam, Gopalakrishnan Journal of Materials Chemistry A, Vol. 10, Issue 37 https://doi.org/10.1039/D2TA02202H	journal	January 2022
Δ-Machine learning for quantum chemistry prediction of solution-phase molecular properties at the ground and excited states Chen, Xu; Li, Pinyuan; Hruska, Eugen Physical Chemistry Chemical Physics, Vol. 25, Issue 19 https://doi.org/10.1039/D3CP00506B	journal	January 2023
Modeling molecular ensembles with gradient-domain machine learning force fields Maldonado, Alex M.; Poltavsky, Igor; Vassilev-Galindo, Valentin Digital Discovery, Vol. 2, Issue 3 https://doi.org/10.1039/D3DD00011G	journal	January 2023
Δ² machine learning for reaction property prediction Zhao, Qiyuan; Anstine, Dylan M.; Isayev, Olexandr Chemical Science, Vol. 14, Issue 46 https://doi.org/10.1039/D3SC02408C	journal	January 2023
Artificial design of organic emitters via a genetic algorithm enhanced by a deep neural network Nigam, AkshatKumar; Pollice, Robert; Friederich, Pascal Chemical Science, Vol. 15, Issue 7 https://doi.org/10.1039/D3SC05306G	journal	January 2024
DELFI: a computer oracle for recommending density functionals for excited states calculations Avagliano, Davide; Skreta, Marta; Arellano-Rubach, Sebastian Chemical Science, Vol. 15, Issue 12 https://doi.org/10.1039/D3SC06440A	journal	January 2024
On the accuracy of density functional theory in transition metal chemistry Harvey, Jeremy N. Annual Reports Section "C" (Physical Chemistry), Vol. 102 https://doi.org/10.1039/b419105f	journal	January 2006
Further considerations on the thermodynamics of chemical equilibria and reaction rates Evans, M. G.; Polanyi, M. Transactions of the Faraday Society, Vol. 32 https://doi.org/10.1039/tf9363201333	journal	January 1936
Neural networks and their applications Bishop, Chris M. Review of Scientific Instruments, Vol. 65, Issue 6 https://doi.org/10.1063/1.1144830	journal	June 1994
Jacob’s ladder of density functional approximations for the exchange-correlation energy Perdew, John P. Density functional theory and its application to materials, AIP Conference Proceedings https://doi.org/10.1063/1.1390175	conference	January 2001
Combined first-principles calculation and neural-network correction approach for heat of formation Hu, LiHong; Wang, XiuJun; Wong, LaiHo The Journal of Chemical Physics, Vol. 119, Issue 22 https://doi.org/10.1063/1.1630951	journal	December 2003
Iterative perturbation calculations of ground and excited state energies from multiconfigurational zeroth‐order wavefunctions Huron, B.; Malrieu, J. P.; Rancurel, P. The Journal of Chemical Physics, Vol. 58, Issue 12 https://doi.org/10.1063/1.1679199	journal	June 1973
On the Theory of Oxidation‐Reduction Reactions Involving Electron Transfer. I Marcus, R. A. The Journal of Chemical Physics, Vol. 24, Issue 5 https://doi.org/10.1063/1.1742723	journal	May 1956
Additivity Rules for the Estimation of Molecular Properties. Thermodynamic Properties Benson, Sidney W.; Buss, Jerry H. The Journal of Chemical Physics, Vol. 29, Issue 3 https://doi.org/10.1063/1.1744539	journal	September 1958
Many-electron self-interaction error in approximate density functionals Mori-Sánchez, Paula; Cohen, Aron J.; Yang, Weitao The Journal of Chemical Physics, Vol. 125, Issue 20 https://doi.org/10.1063/1.2403848	journal	November 2006
Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies Balabin, Roman M.; Lomakina, Ekaterina I. The Journal of Chemical Physics, Vol. 131, Issue 7 https://doi.org/10.1063/1.3206326	journal	August 2009
A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu Grimme, Stefan; Antony, Jens; Ehrlich, Stephan The Journal of Chemical Physics, Vol. 132, Issue 15 https://doi.org/10.1063/1.3382344	journal	April 2010
A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and charges Morawietz, Tobias; Sharma, Vikas; Behler, Jörg The Journal of Chemical Physics, Vol. 136, Issue 6 https://doi.org/10.1063/1.3682557	journal	February 2012
The orbital-specific-virtual local coupled cluster singles and doubles method Yang, Jun; Chan, Garnet Kin-Lic; Manby, Frederick R. The Journal of Chemical Physics, Vol. 136, Issue 14 https://doi.org/10.1063/1.3696963	journal	April 2012
The construction of modified virtual orbitals (MVO’s) which are suited for configuration interaction calculations Bauschlicher, Charles W. The Journal of Chemical Physics, Vol. 72, Issue 2 https://doi.org/10.1063/1.439243	journal	January 1980
A new mixing of Hartree–Fock and local density‐functional theories Becke, Axel D. The Journal of Chemical Physics, Vol. 98, Issue 2 https://doi.org/10.1063/1.464304	journal	January 1993
Density‐functional thermochemistry. III. The role of exact exchange Becke, Axel D. The Journal of Chemical Physics, Vol. 98, Issue 7, p. 5648-5652 https://doi.org/10.1063/1.464913	journal	April 1993
Neural network models of potential energy surfaces Blank, Thomas B.; Brown, Steven D.; Calhoun, August W. The Journal of Chemical Physics, Vol. 103, Issue 10 https://doi.org/10.1063/1.469597	journal	September 1995
Estimating full configuration interaction limits from a Monte Carlo selection of the expansion space Greer, J. C. The Journal of Chemical Physics, Vol. 103, Issue 5 https://doi.org/10.1063/1.469756	journal	August 1995
Perspective on density functional theory Burke, Kieron The Journal of Chemical Physics, Vol. 136, Issue 15 https://doi.org/10.1063/1.4704546	journal	April 2012
Exchange‐correlation potentials Tozer, David J.; Ingamells, Victoria E.; Handy, Nicholas C. The Journal of Chemical Physics, Vol. 105, Issue 20 https://doi.org/10.1063/1.472753	journal	November 1996
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy APL Materials, Vol. 1, Issue 1 https://doi.org/10.1063/1.4812323	journal	July 2013
Orbital-free bond breaking via machine learning Snyder, John C.; Rupp, Matthias; Hansen, Katja The Journal of Chemical Physics, Vol. 139, Issue 22 https://doi.org/10.1063/1.4834075	journal	December 2013
Adaptive multiconfigurational wave functions Evangelista, Francesco A. The Journal of Chemical Physics, Vol. 140, Issue 12 https://doi.org/10.1063/1.4869192	journal	March 2014
Electronic spectra from TDDFT and machine learning in chemical space Ramakrishnan, Raghunathan; Hartmann, Mia; Tapavicza, Enrico The Journal of Chemical Physics, Vol. 143, Issue 8 https://doi.org/10.1063/1.4928757	journal	August 2015
ω B97M-V: A combinatorially optimized, range-separated hybrid, meta-GGA density functional with VV10 nonlocal correlation Mardirossian, Narbe; Head-Gordon, Martin The Journal of Chemical Physics, Vol. 144, Issue 21 https://doi.org/10.1063/1.4952647	journal	June 2016
A deterministic alternative to the full configuration interaction quantum Monte Carlo method Tubman, Norm M.; Lee, Joonho; Takeshita, Tyler Y. The Journal of Chemical Physics, Vol. 145, Issue 4 https://doi.org/10.1063/1.4955109	journal	July 2016
Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity Huang, Bing; von Lilienfeld, O. Anatole The Journal of Chemical Physics, Vol. 145, Issue 16 https://doi.org/10.1063/1.4964627	journal	October 2016
Constructing first-principles phase diagrams of amorphous Li _x Si using machine-learning-assisted sampling with an evolutionary algorithm Artrith, Nongnuch; Urban, Alexander; Ceder, Gerbrand The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5017661	journal	June 2018
SchNet – A deep learning architecture for molecules and materials Schütt, K. T.; Sauceda, H. E.; Kindermans, P. -J. The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5019779	journal	June 2018
Less is more: Sampling chemical space with active learning Smith, Justin S.; Nebgen, Ben; Lubbers, Nicholas The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5023802	journal	June 2018
Unsupervised machine learning in atomistic simulations, between predictions and understanding Ceriotti, Michele The Journal of Chemical Physics, Vol. 150, Issue 15 https://doi.org/10.1063/1.5091842	journal	April 2019
Machine learning for potential energy surfaces: An extensive database and assessment of methods Schmitz, Gunnar; Godtliebsen, Ian Heide; Christiansen, Ove The Journal of Chemical Physics, Vol. 150, Issue 24 https://doi.org/10.1063/1.5100141	journal	June 2019
FCHL revisited: Faster and more accurate quantum machine learning Christensen, Anders S.; Bratholm, Lars A.; Faber, Felix A. The Journal of Chemical Physics, Vol. 152, Issue 4 https://doi.org/10.1063/1.5126701	journal	January 2020
Incorporating long-range physics in atomic-scale machine learning Grisafi, Andrea; Ceriotti, Michele The Journal of Chemical Physics, Vol. 151, Issue 20 https://doi.org/10.1063/1.5128375	journal	November 2019
The density matrix renormalization group in chemistry and molecular physics: Recent developments and new challenges Baiardi, Alberto; Reiher, Markus The Journal of Chemical Physics, Vol. 152, Issue 4 https://doi.org/10.1063/1.5129672	journal	January 2020
A Δ-learning strategy for interpretation of spectroscopic observables Watson, Luke; Pope, Thomas; Jay, Raphael M. Structural Dynamics, Vol. 10, Issue 6 https://doi.org/10.1063/4.0000215	journal	November 2023
Large scale and linear scaling DFT with the CONQUEST code Nakata, Ayako; Baker, Jack S.; Mujahed, Shereif Y. The Journal of Chemical Physics, Vol. 152, Issue 16 https://doi.org/10.1063/5.0005074	journal	April 2020
Hierarchical machine learning of potential energy surfaces Dral, Pavlo O.; Owens, Alec; Dral, Alexey The Journal of Chemical Physics, Vol. 152, Issue 20 https://doi.org/10.1063/5.0006498	journal	May 2020
A deep neural network for molecular wave functions in quasi-atomic minimal basis representation Gastegger, M.; McSloy, A.; Luya, M. The Journal of Chemical Physics, Vol. 153, Issue 4 https://doi.org/10.1063/5.0012911	journal	July 2020
OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features Qiao, Zhuoran; Welborn, Matthew; Anandkumar, Animashree The Journal of Chemical Physics, Vol. 153, Issue 12 https://doi.org/10.1063/5.0021955	journal	September 2020
A Bayesian inference framework for compression and prediction of quantum states Rath, Yannic; Glielmo, Aldo; Booth, George H. The Journal of Chemical Physics, Vol. 153, Issue 12 https://doi.org/10.1063/5.0024570	journal	September 2020
When do short-range atomistic machine-learning models fall short? Yue, Shuwen; Muniz, Maria Carolina; Calegari Andrade, Marcos F. The Journal of Chemical Physics, Vol. 154, Issue 3 https://doi.org/10.1063/5.0031215	journal	January 2021
Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning Fonseca, Gregory; Poltavsky, Igor; Vassilev-Galindo, Valentin The Journal of Chemical Physics, Vol. 154, Issue 12 https://doi.org/10.1063/5.0035530	journal	March 2021
Accelerating coupled cluster calculations with nonlinear dynamics and supervised machine learning Agarawal, Valay; Roy, Samrendra; Chakraborty, Anish The Journal of Chemical Physics, Vol. 154, Issue 4 https://doi.org/10.1063/5.0037090	journal	January 2021
Δ-machine learning for potential energy surfaces: A PIP approach to bring a DFT-based PES to CCSD(T) level of theory Nandi, Apurba; Qu, Chen; Houston, Paul L. The Journal of Chemical Physics, Vol. 154, Issue 5 https://doi.org/10.1063/5.0038301	journal	February 2021
CAS without SCF—Why to use CASCI and where to get the orbitals Levine, Benjamin G.; Durden, Andrew S.; Esch, Michael P. The Journal of Chemical Physics, Vol. 154, Issue 9 https://doi.org/10.1063/5.0042147	journal	March 2021
Machine learned Hückel theory: Interfacing physics and deep neural networks Zubatiuk, Tetiana; Nebgen, Benjamin; Lubbers, Nicholas The Journal of Chemical Physics, Vol. 154, Issue 24 https://doi.org/10.1063/5.0052857	journal	June 2021
A hybrid coupled cluster–machine learning algorithm: Development of various regression models and benchmark applications Agarawal, Valay; Roy, Samrendra; Shrawankar, Kapil K. The Journal of Chemical Physics, Vol. 156, Issue 1 https://doi.org/10.1063/5.0072250	journal	January 2022
Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties Nigam, Jigyasa; Willatt, Michael J.; Ceriotti, Michele The Journal of Chemical Physics, Vol. 156, Issue 1 https://doi.org/10.1063/5.0072784	journal	January 2022
Non-iterative method for constructing valence antibonding molecular orbitals and a molecule-adapted minimum basis Aldossary, Abdulrahman; Head-Gordon, Martin The Journal of Chemical Physics, Vol. 157, Issue 9 https://doi.org/10.1063/5.0095443	journal	September 2022
TBMaLT, a flexible toolkit for combining tight-binding and machine learning McSloy, A.; Fan, G.; Sun, W. The Journal of Chemical Physics, Vol. 158, Issue 3 https://doi.org/10.1063/5.0132892	journal	January 2023
Machine learning matrix product state ansatz for strongly correlated systems Ghosh, Sumanta K.; Ghosh, Debashree The Journal of Chemical Physics, Vol. 158, Issue 6 https://doi.org/10.1063/5.0133399	journal	February 2023
Ultra-fast semi-empirical quantum chemistry for high-throughput computational campaigns with Sparrow Bosia, Francesco; Zheng, Peikun; Vaucher, Alain The Journal of Chemical Physics, Vol. 158, Issue 5 https://doi.org/10.1063/5.0136404	journal	February 2023
Inverse molecular design and parameter optimization with Hückel theory using automatic differentiation Vargas–Hernández, Rodrigo A.; Jorner, Kjell; Pollice, Robert The Journal of Chemical Physics, Vol. 158, Issue 10 https://doi.org/10.1063/5.0137103	journal	March 2023
SchNetPack 2.0: A neural network toolbox for atomistic machine learning Schütt, Kristof T.; Hessmann, Stefaan S. P.; Gebauer, Niklas W. A. The Journal of Chemical Physics, Vol. 158, Issue 14 https://doi.org/10.1063/5.0138367	journal	April 2023
How to validate machine-learned interatomic potentials Morrow, Joe D.; Gardner, John L. A.; Deringer, Volker L. The Journal of Chemical Physics, Vol. 158, Issue 12 https://doi.org/10.1063/5.0139611	journal	March 2023
Synergy of semiempirical models and machine learning in computational chemistry Fedik, Nikita; Nebgen, Benjamin; Lubbers, Nicholas The Journal of Chemical Physics, Vol. 159, Issue 11 https://doi.org/10.1063/5.0151833	journal	September 2023
DeePMD-kit v2: A software package for deep potential models Zeng, Jinzhe; Zhang, Duo; Lu, Denghui The Journal of Chemical Physics, Vol. 159, Issue 5 https://doi.org/10.1063/5.0155600	journal	August 2023
Nearsightedness of electronic matter Prodan, E.; Kohn, W. Proceedings of the National Academy of Sciences, Vol. 102, Issue 33 https://doi.org/10.1073/pnas.0505436102	journal	August 2005
How van der Waals interactions determine the unique properties of water Morawietz, Tobias; Singraber, Andreas; Dellago, Christoph Proceedings of the National Academy of Sciences, Vol. 113, Issue 30 https://doi.org/10.1073/pnas.1602375113	journal	July 2016
Accurate molecular polarizabilities with coupled cluster theory and machine learning Wilkins, David M.; Grisafi, Andrea; Yang, Yang Proceedings of the National Academy of Sciences, Vol. 116, Issue 9 https://doi.org/10.1073/pnas.1816132116	journal	February 2019
Definitions, methods, and applications in interpretable machine learning Murdoch, W. James; Singh, Chandan; Kumbier, Karl Proceedings of the National Academy of Sciences, Vol. 116, Issue 44 https://doi.org/10.1073/pnas.1900654116	journal	October 2019
Thirty years of density functional theory in computational chemistry: an overview and extensive assessment of 200 density functionals Mardirossian, Narbe; Head-Gordon, Martin Molecular Physics, Vol. 115, Issue 19 https://doi.org/10.1080/00268976.2017.1333644	journal	April 2017
Applications of large-scale density functional theory in biology Cole, Daniel J.; Hine, Nicholas D. M. Journal of Physics: Condensed Matter, Vol. 28, Issue 39 https://doi.org/10.1088/0953-8984/28/39/393001	journal	August 2016
Predicting structure-dependent Hubbard U parameters via machine learning Cai, Guanghui; Cao, Zhendong; Xie, Fankai Materials Futures, Vol. 3, Issue 2 https://doi.org/10.1088/2752-5724/ad19e2	journal	January 2024
Inhomogeneous Electron Gas Hohenberg, P.; Kohn, W. Physical Review, Vol. 136, Issue 3B, p. B864-B871 https://doi.org/10.1103/PhysRev.136.B864	journal	November 1964
Ground State of Liquid He 4 McMillan, W. L. Physical Review, Vol. 138, Issue 2A https://doi.org/10.1103/PhysRev.138.A442	journal	April 1965
Self-Consistent Equations Including Exchange and Correlation Effects Kohn, W.; Sham, L. J. Physical Review, Vol. 140, Issue 4A, p. A1133-A1138 https://doi.org/10.1103/PhysRev.140.A1133	journal	November 1965
Virtual Orbitals in Hartree-Fock Theory Huzinaga, Sigeru; Arnau, Catalina Physical Review A, Vol. 1, Issue 5 https://doi.org/10.1103/PhysRevA.1.1285	journal	May 1970
Deep learning and density-functional theory Ryczko, Kevin; Strubbe, David A.; Tamblyn, Isaac Physical Review A, Vol. 100, Issue 2 https://doi.org/10.1103/PhysRevA.100.022512	journal	August 2019
Pure- N -representability conditions of two-fermion reduced density matrices Mazziotti, David A. Physical Review A, Vol. 94, Issue 3 https://doi.org/10.1103/PhysRevA.94.032516	journal	September 2016
Two-dimensional frustrated J 1 − J 2 model studied with neural network quantum states Choo, Kenny; Neupert, Titus; Carleo, Giuseppe Physical Review B, Vol. 100, Issue 12 https://doi.org/10.1103/PhysRevB.100.125124	journal	September 2019
Framework for efficient ab initio electronic structure with Gaussian Process States Rath, Yannic; Booth, George H. Physical Review B, Vol. 107, Issue 20 https://doi.org/10.1103/PhysRevB.107.205119	journal	May 2023
Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density Lee, Chengteh; Yang, Weitao; Parr, Robert G. Physical Review B, Vol. 37, Issue 2 https://doi.org/10.1103/PhysRevB.37.785	journal	January 1988
Band theory and Mott insulators: Hubbard U instead of Stoner I Anisimov, Vladimir I.; Zaanen, Jan; Andersen, Ole K. Physical Review B, Vol. 44, Issue 3, p. 943-954 https://doi.org/10.1103/PhysRevB.44.943	journal	July 1991
High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide Artrith, Nongnuch; Morawietz, Tobias; Behler, Jörg Physical Review B, Vol. 83, Issue 15 https://doi.org/10.1103/PhysRevB.83.153101	journal	April 2011
Neural network interatomic potential for the phase change material GeTe Sosso, Gabriele C.; Miceli, Giacomo; Caravati, Sebastiano Physical Review B, Vol. 85, Issue 17 https://doi.org/10.1103/PhysRevB.85.174103	journal	May 2012
On representing chemical environments Bartók, Albert P.; Kondor, Risi; Csányi, Gábor Physical Review B, Vol. 87, Issue 18 https://doi.org/10.1103/PhysRevB.87.184115	journal	May 2013
Iterative backflow renormalization procedure for many-body ground-state wave functions of strongly interacting normal Fermi liquids Taddei, Michele; Ruggeri, Michele; Moroni, Saverio Physical Review B, Vol. 91, Issue 11 https://doi.org/10.1103/PhysRevB.91.115106	journal	March 2015
Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network Ghasemi, S. Alireza; Hofstetter, Albert; Saha, Santanu Physical Review B, Vol. 92, Issue 4 https://doi.org/10.1103/PhysRevB.92.045131	journal	July 2015
Machine learning based interatomic potential for amorphous carbon Deringer, Volker L.; Csányi, Gábor Physical Review B, Vol. 95, Issue 9 https://doi.org/10.1103/PhysRevB.95.094203	journal	March 2017
Approximating quantum many-body wave functions using artificial neural networks Cai, Zi; Liu, Jinguo Physical Review B, Vol. 97, Issue 3 https://doi.org/10.1103/PhysRevB.97.035116	journal	January 2018
Atomic-position independent descriptor for machine learning of material properties Jain, Ankit; Bligaard, Thomas Physical Review B, Vol. 98, Issue 21 https://doi.org/10.1103/PhysRevB.98.214112	journal	December 2018
Machine learning density functional theory for the Hubbard model Nelson, James; Tiwari, Rajarshi; Sanvito, Stefano Physical Review B, Vol. 99, Issue 7 https://doi.org/10.1103/PhysRevB.99.075132	journal	February 2019
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons Bartók, Albert P.; Payne, Mike C.; Kondor, Risi Physical Review Letters, Vol. 104, Issue 13 https://doi.org/10.1103/PhysRevLett.104.136403	journal	April 2010
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning Rupp, Matthias; Tkatchenko, Alexandre; Müller, Klaus-Robert Physical Review Letters, Vol. 108, Issue 5 https://doi.org/10.1103/PhysRevLett.108.058301	journal	January 2012
Finding Density Functionals with Machine Learning Snyder, John C.; Rupp, Matthias; Hansen, Katja Physical Review Letters, Vol. 108, Issue 25 https://doi.org/10.1103/PhysRevLett.108.253002	journal	June 2012
Corrections to Thomas-Fermi Densities at Turning Points and Beyond Ribeiro, Raphael F.; Lee, Donghyung; Cangi, Attila Physical Review Letters, Vol. 114, Issue 5 https://doi.org/10.1103/PhysRevLett.114.050401	journal	February 2015
Strongly Constrained and Appropriately Normed Semilocal Density Functional Sun, Jianwei; Ruzsinszky, Adrienn; Perdew, John P. Physical Review Letters, Vol. 115, Issue 3 https://doi.org/10.1103/PhysRevLett.115.036402	journal	July 2015
Machine Learning Energies of 2 Million Elpasolite ( A B C 2 D 6 ) Crystals Faber, Felix A.; Lindmaa, Alexander; von Lilienfeld, O. Anatole Physical Review Letters, Vol. 117, Issue 13 https://doi.org/10.1103/PhysRevLett.117.135502	journal	September 2016
Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics Zhang, Linfeng; Han, Jiequn; Wang, Han Physical Review Letters, Vol. 120, Issue 14 https://doi.org/10.1103/PhysRevLett.120.143001	journal	April 2018
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties Xie, Tian; Grossman, Jeffrey C. Physical Review Letters, Vol. 120, Issue 14 https://doi.org/10.1103/PhysRevLett.120.145301	journal	April 2018
Growth Mechanism and Origin of High s p 3 Content in Tetrahedral Amorphous Carbon Caro, Miguel A.; Deringer, Volker L.; Koskinen, Jari Physical Review Letters, Vol. 120, Issue 16 https://doi.org/10.1103/PhysRevLett.120.166101	journal	April 2018
Nonlinear Network Description for Many-Body Quantum Systems in Continuous Space Ruggeri, Michele; Moroni, Saverio; Holzmann, Markus Physical Review Letters, Vol. 120, Issue 20 https://doi.org/10.1103/PhysRevLett.120.205302	journal	May 2018
Backflow Transformations via Neural Networks for Quantum Many-Body Wave Functions Luo, Di; Clark, Bryan K. Physical Review Letters, Vol. 122, Issue 22 https://doi.org/10.1103/PhysRevLett.122.226401	journal	June 2019
Kohn-Sham Equations as Regularizer: Building Prior Knowledge into Machine-Learned Physics Li, Li; Hoyer, Stephan; Pederson, Ryan Physical Review Letters, Vol. 126, Issue 3 https://doi.org/10.1103/PhysRevLett.126.036401	journal	January 2021
Discovering Quantum Phase Transitions with Fermionic Neural Networks Cassella, Gino; Sutterud, Halvard; Azadi, Sam Physical Review Letters, Vol. 130, Issue 3 https://doi.org/10.1103/PhysRevLett.130.036401	journal	January 2023
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces Behler, Jörg; Parrinello, Michele Physical Review Letters, Vol. 98, Issue 14 https://doi.org/10.1103/PhysRevLett.98.146401	journal	April 2007
Active learning of uniformly accurate interatomic potentials for materials simulation Zhang, Linfeng; Lin, De-Ye; Wang, Han Physical Review Materials, Vol. 3, Issue 2 https://doi.org/10.1103/PhysRevMaterials.3.023804	journal	February 2019
Design and analysis of machine learning exchange-correlation functionals via rotationally invariant convolutional descriptors Lei, Xiangyun; Medford, Andrew J. Physical Review Materials, Vol. 3, Issue 6 https://doi.org/10.1103/PhysRevMaterials.3.063801	journal	June 2019
Machine-learning-assisted prediction of magnetic double perovskites Halder, Anita; Ghosh, Aishwaryo; Dasgupta, Tanusri Saha Physical Review Materials, Vol. 3, Issue 8 https://doi.org/10.1103/PhysRevMaterials.3.084418	journal	August 2019
Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery Park, Cheol Woo; Wolverton, Chris Physical Review Materials, Vol. 4, Issue 6 https://doi.org/10.1103/PhysRevMaterials.4.063801	journal	June 2020
Machine learning for metallurgy IV: A neural network potential for Al-Cu-Mg and Al-Cu-Mg-Zn Marchand, Daniel; Curtin, W. A. Physical Review Materials, Vol. 6, Issue 5 https://doi.org/10.1103/PhysRevMaterials.6.053803	journal	May 2022
Recurrent neural network wave functions Hibat-Allah, Mohamed; Ganahl, Martin; Hayward, Lauren E. Physical Review Research, Vol. 2, Issue 2 https://doi.org/10.1103/PhysRevResearch.2.023358	journal	June 2020
Ab initio solution of the many-electron Schrödinger equation with deep neural networks Pfau, David; Spencer, James S.; Matthews, Alexander G. D. G. Physical Review Research, Vol. 2, Issue 3 https://doi.org/10.1103/PhysRevResearch.2.033429	journal	September 2020
Determinant-free fermionic wave function using feed-forward neural networks Inui, Koji; Kato, Yasuyuki; Motome, Yukitoshi Physical Review Research, Vol. 3, Issue 4 https://doi.org/10.1103/PhysRevResearch.3.043126	journal	November 2021
Quantum Gaussian process state: A kernel-inspired state with quantum support data Rath, Yannic; Booth, George H. Physical Review Research, Vol. 4, Issue 2 https://doi.org/10.1103/PhysRevResearch.4.023126	journal	May 2022
Nobel Lecture: Electronic structure of matter—wave functions and density functionals Kohn, W. Reviews of Modern Physics, Vol. 71, Issue 5 https://doi.org/10.1103/RevModPhys.71.1253	journal	October 1999
Nobel Lecture: Quantum chemical models Pople, John A. Reviews of Modern Physics, Vol. 71, Issue 5 https://doi.org/10.1103/RevModPhys.71.1267	journal	October 1999
30 years of adaptive neural networks: perceptron, Madaline, and backpropagation Widrow, B.; Lehr, M. A. Proceedings of the IEEE, Vol. 78, Issue 9 https://doi.org/10.1109/5.58323	journal	January 1990
Gradient-based learning applied to document recognition Lecun, Y.; Bottou, L.; Bengio, Y. Proceedings of the IEEE, Vol. 86, Issue 11 https://doi.org/10.1109/5.726791	journal	January 1998
Learning Hierarchical Features for Scene Labeling Farabet, Clement; Couprie, Camille; Najman, Laurent IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, Issue 8 https://doi.org/10.1109/TPAMI.2012.231	journal	August 2013
Machine learning of accurate energy-conserving molecular force fields Chmiela, Stefan; Tkatchenko, Alexandre; Sauceda, Huziel E. Science Advances, Vol. 3, Issue 5 https://doi.org/10.1126/sciadv.1603015	journal	May 2017
Machine learning unifies the modeling of materials and molecules Bartók, Albert P.; De, Sandip; Poelking, Carl Science Advances, Vol. 3, Issue 12 https://doi.org/10.1126/sciadv.1701816	journal	December 2017
Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network Zubatyuk, Roman; Smith, Justin S.; Leszczynski, Jerzy Science Advances, Vol. 5, Issue 8 https://doi.org/10.1126/sciadv.aav6490	journal	August 2019
Accurate global machine learning force fields for molecules with hundreds of atoms Chmiela, Stefan; Vassilev-Galindo, Valentin; Unke, Oliver T. Science Advances, Vol. 9, Issue 2 https://doi.org/10.1126/sciadv.adf0873	journal	January 2023
Complexity in Strongly Correlated Electronic Systems Dagotto, E. Science, Vol. 309, Issue 5732 https://doi.org/10.1126/science.1107559	journal	July 2005
Methylene: A Paradigm for Computational Quantum Chemistry Schaefer, H. F. Science, Vol. 231, Issue 4742 https://doi.org/10.1126/science.231.4742.1100	journal	March 1986
Solving the quantum many-body problem with artificial neural networks Carleo, Giuseppe; Troyer, Matthias Science, Vol. 355, Issue 6325 https://doi.org/10.1126/science.aag2302	journal	February 2017
Predicting reaction performance in C–N cross-coupling using machine learning Ahneman, Derek T.; Estrada, Jesús G.; Lin, Shishi Science, Vol. 360, Issue 6385 https://doi.org/10.1126/science.aar5169	journal	February 2018
Pushing the frontiers of density functionals by solving the fractional electron problem Kirkpatrick, James; McMorrow, Brendan; Turban, David H. P. Science, Vol. 374, Issue 6573 https://doi.org/10.1126/science.abj6511	journal	December 2021
Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials Shapeev, Alexander V. Multiscale Modeling & Simulation, Vol. 14, Issue 3 https://doi.org/10.1137/15M1054183	journal	January 2016
Accurate spin-dependent electron liquid correlation energies for local spin density calculations: a critical analysis Vosko, S. H.; Wilk, L.; Nusair, M. Canadian Journal of Physics, Vol. 58, Issue 8 https://doi.org/10.1139/p80-159	journal	August 1980
Machine learning potentials for extended systems: a perspective Behler, Jörg; Csányi, Gábor The European Physical Journal B, Vol. 94, Issue 7 https://doi.org/10.1140/epjb/s10051-021-00156-1	journal	July 2021
Polarizable Force Fields for Biomolecular Simulations: Recent Advances and Applications Jing, Zhifeng; Liu, Chengwen; Cheng, Sara Y. Annual Review of Biophysics, Vol. 48, Issue 1 https://doi.org/10.1146/annurev-biophys-070317-033349	journal	May 2019
What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery Pyzer-Knapp, Edward O.; Suh, Changwon; Gómez-Bombarelli, Rafael Annual Review of Materials Research, Vol. 45, Issue 1 https://doi.org/10.1146/annurev-matsci-070214-020823	journal	July 2015
Neural Network Potentials: A Concise Overview of Methods Kocer, Emir; Ko, Tsz Wai; Behler, Jörg Annual Review of Physical Chemistry, Vol. 73, Issue 1 https://doi.org/10.1146/annurev-physchem-082720-034254	journal	April 2022
Long Short-Term Memory Hochreiter, Sepp; Schmidhuber, Jürgen Neural Computation, Vol. 9, Issue 8 https://doi.org/10.1162/neco.1997.9.8.1735	journal	November 1997
Deep Belief Networks Are Compact Universal Approximators Le Roux, Nicolas; Bengio, Yoshua Neural Computation, Vol. 22, Issue 8 https://doi.org/10.1162/neco.2010.08-09-1081	journal	August 2010
Performance of Firth-and logF-type penalized methods in risk prediction for small or sparse binary data Rahman, M. Shafiqur; Sultana, Mahbuba BMC Medical Research Methodology, Vol. 17, Issue 1 https://doi.org/10.1186/s12874-017-0313-9	journal	February 2017
Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules Choi, Jong Youl; Zhang, Pei; Mehta, Kshitij Journal of Cheminformatics, Vol. 14, Issue 1 https://doi.org/10.1186/s13321-022-00652-1	journal	October 2022
Interatomic Potentials from First-Principles Calculations: The Force-Matching Method Ercolessi, F.; Adams, J. B. Europhysics Letters (EPL), Vol. 26, Issue 8 https://doi.org/10.1209/0295-5075/26/8/005	journal	June 1994
Die katalytische Zersetzung des Nitramids und ihre physikalisch-chemische Bedeutung Brönsted, J. N.; Pedersen, Kai Zeitschrift für Physikalische Chemie, Vol. 108U, Issue 1 https://doi.org/10.1515/zpch-1924-10814	journal	January 1924
Climbing the ladder of density functional approximations Perdew, John P. MRS Bulletin, Vol. 38, Issue 9 https://doi.org/10.1557/mrs.2013.178	journal	September 2013
Machine-learning potentials for crystal defects Freitas, Rodrigo; Cao, Yifan MRS Communications, Vol. 12, Issue 5 https://doi.org/10.1557/s43579-022-00221-5	journal	August 2022
Coupled Cluster Theory in Materials Science Zhang, Igor Ying; Grüneis, Andreas Frontiers in Materials, Vol. 6 https://doi.org/10.3389/fmats.2019.00123	journal	June 2019
A State-of-the-Art Survey on Deep Learning Theory and Architectures Alom, Md Zahangir; Taha, Tarek M.; Yakopcic, Chris Electronics, Vol. 8, Issue 3 https://doi.org/10.3390/electronics8030292	journal	March 2019
Tackling Structural Complexity in Li2S-P2S5 Solid-State Electrolytes Using Machine Learning Potentials Staacke, Carsten G.; Huss, Tabea; Margraf, Johannes T. Nanomaterials, Vol. 12, Issue 17 https://doi.org/10.3390/nano12172950	journal	August 2022
Review of multi-fidelity models Giselle Fernández-Godino, M. Advances in Computational Science and Engineering, Vol. 1, Issue 4 https://doi.org/10.3934/acse.2023015	journal	January 2023
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations Fu, Xiang; Wu, Zhenghao; Wang, Wujie arXiv https://doi.org/10.48550/ARXIV.2210.07237	text	January 2022
An Introduction to Neural Networks Anderson, James A. https://doi.org/10.7551/mitpress/3905.001.0001	book	January 1995
Solving the Bose–Hubbard Model with Machine Learning Saito, Hiroki Journal of the Physical Society of Japan, Vol. 86, Issue 9 https://doi.org/10.7566/JPSJ.86.093001	journal	September 2017
Machine Learning Technique to Find Quantum Many-Body Ground States of Bosons on a Lattice Saito, Hiroki; Kato, Masaya Journal of the Physical Society of Japan, Vol. 87, Issue 1 https://doi.org/10.7566/JPSJ.87.014001	journal	January 2018
Method to Solve Quantum Few-Body Problems with Artificial Neural Networks Saito, Hiroki Journal of the Physical Society of Japan, Vol. 87, Issue 7 https://doi.org/10.7566/JPSJ.87.074002	journal	July 2018

Similar Records

Integrating Models with Real-time Field Data for Extreme Events: From Field Sensors to Models and Back with AI in the Loop

Technical Report · 2021 · OSTI ID:1769727

Cholia, Shreyas; Varadharajan, Charuleka; Pastorello, Gilberto

AI Meets Exascale Computing: Advancing Cancer Research With Large-Scale High Performance Computing

Journal Article · 2019 · Frontiers in Oncology · OSTI ID:1637271

Bhattacharya, Tanmoy; Brettin, Thomas; Doroshow, James H.; +13 more

Automation is all you need: Faster Earth system models with AI/ML

Technical Report · 2021 · OSTI ID:1769679

Ball, Kenneth; Hineman, Jay; Voisin, Sam; +2 more

Title: In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

Citation Formats

References (395)

Similar Records

Related Subjects