Fast Predictions of Reaction Barrier Heights: Toward Coupled-Cluster Accuracy

Spiekermann, Kevin A.; Pattanaik, Lagnajit; Green, William H.

doi:10.1021/acs.jpca.2c02614

Fast Predictions of Reaction Barrier Heights: Toward Coupled-Cluster Accuracy

Journal Article · 21 June 2022 · Journal of Physical Chemistry. A, Molecules, Spectroscopy, Kinetics, Environment, and General Theory

DOI:https://doi.org/10.1021/acs.jpca.2c02614· OSTI ID:1977963

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Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States; OSTI
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States

Not provided.

Research Organization:: Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

DOE Contract Number:: SC0014901

OSTI ID:: 1977963

Journal Information:: Journal of Physical Chemistry. A, Molecules, Spectroscopy, Kinetics, Environment, and General Theory, Journal Name: Journal of Physical Chemistry. A, Molecules, Spectroscopy, Kinetics, Environment, and General Theory Journal Issue: 25 Vol. 126; ISSN 1089-5639

Publisher:: American Chemical Society

Country of Publication:: United States

Language:: English

References (102)

Progress towards machine learning reaction rate constants Komp, Evan; Janulaitis, Nida; Valleau, Stéphanie Physical Chemistry Chemical Physics, Vol. 24, Issue 5 https://doi.org/10.1039/D1CP04422B	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
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
Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation Liu, Mengjie; Grinberg Dana, Alon; Johnson, Matthew S. Journal of Chemical Information and Modeling, Vol. 61, Issue 6 https://doi.org/10.1021/acs.jcim.0c01480	journal	May 2021
Generating transition states of isomerization reactions with deep learning Pattanaik, Lagnajit; Ingraham, John B.; Grambow, Colin A. Physical Chemistry Chemical Physics, Vol. 22, Issue 41 https://doi.org/10.1039/D0CP04670A	journal	January 2020
TSNet: predicting transition state structures with tensor field networks and transfer learning Jackson, Riley; Zhang, Wenyuan; Pearson, Jason Chemical Science, Vol. 12, Issue 29 https://doi.org/10.1039/D1SC01206A	journal	January 2021
Optimizing transition states via kernel-based machine learning Pozun, Zachary D.; Hansen, Katja; Sheppard, Daniel The Journal of Chemical Physics, Vol. 136, Issue 17 https://doi.org/10.1063/1.4707167	journal	May 2012
Practical methods for including torsional anharmonicity in thermochemical calculations on complex molecules: The internal-coordinate multi-structural approximation Zheng, Jingjing; Yu, Tao; Papajak, Ewa Physical Chemistry Chemical Physics, Vol. 13, Issue 23 https://doi.org/10.1039/c0cp02644a	journal	January 2011
The DBH24/08 Database and Its Use to Assess Electronic Structure Model Chemistries for Chemical Reaction Barrier Heights Zheng, Jingjing; Zhao, Yan; Truhlar, Donald G. Journal of Chemical Theory and Computation, Vol. 5, Issue 4 https://doi.org/10.1021/ct800568m	journal	March 2009
Automated exploration of the low-energy chemical space with fast quantum chemical methods Pracht, Philipp; Bohle, Fabian; Grimme, Stefan Physical Chemistry Chemical Physics, Vol. 22, Issue 14 https://doi.org/10.1039/C9CP06869D	journal	January 2020
Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices Wang, Anthony Yu-Tung; Murdock, Ryan J.; Kauwe, Steven K. Chemistry of Materials, Vol. 32, Issue 12 https://doi.org/10.1021/acs.chemmater.0c01907	journal	May 2020
High Accuracy Barrier Heights, Enthalpies, and Rate Coefficients for Chemical Reactions Spiekermann, Kevin; Pattanaik, Lagnajit; Green, William H. Zenodo https://doi.org/10.5281/zenodo.6618262	dataset	January 2022
Inertia and driving force of chemical reactions Evans, M. G.; Polanyi, M. Transactions of the Faraday Society, Vol. 34 https://doi.org/10.1039/tf9383400011	journal	January 1938
Correct Symmetry Treatment for X + X Reactions Prevents Large Errors in Predicted Isotope Enrichment Goldman, Mark Jacob; Ono, Shuhei; Green, William H. The Journal of Physical Chemistry A, Vol. 123, Issue 12 https://doi.org/10.1021/acs.jpca.8b09024	journal	March 2019
Energy Levels and Thermodynamic Functions for Molecules with Internal Rotation I. Rigid Frame with Attached Tops Pitzer, Kenneth S.; Gwinn, William D. The Journal of Chemical Physics, Vol. 10, Issue 7 https://doi.org/10.1063/1.1723744	journal	July 1942
From theoretical reaction dynamics to chemical modeling of combustion Klippenstein, Stephen J. Proceedings of the Combustion Institute, Vol. 36, Issue 1 https://doi.org/10.1016/j.proci.2016.07.100	journal	January 2017
A priori rate constants for kinetic modeling Sumathi, R.; Green Jr., William H. Theoretical Chemistry Accounts: Theory, Computation, and Modeling (Theoretica Chimica Acta), Vol. 108, Issue 4 https://doi.org/10.1007/s00214-002-0368-4	journal	October 2002
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
EHreact: Extended Hasse Diagrams for the Extraction and Scoring of Enzymatic Reaction Templates Heid, Esther; Goldman, Samuel; Sankaranarayanan, Karthik Journal of Chemical Information and Modeling, Vol. 61, Issue 10 https://doi.org/10.1021/acs.jcim.1c00921	journal	September 2021
Estimation of heats of formation of organic compounds by additivity methods Cohen, N.; Benson, S. W. Chemical Reviews, Vol. 93, Issue 7 https://doi.org/10.1021/cr00023a005	journal	November 1993
Thousands of reactants and transition states for competing E2 and S N 2 reactions von Rudorff, Guido Falk; Heinen, Stefan N.; Bragato, Marco Machine Learning: Science and Technology, Vol. 1, Issue 4 https://doi.org/10.1088/2632-2153/aba822	journal	October 2020
Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space Heinen, Stefan; von Rudorff, Guido Falk; von Lilienfeld, O. Anatole The Journal of Chemical Physics, Vol. 155, Issue 6 https://doi.org/10.1063/5.0059742	journal	August 2021
Predicting Rate Constants of Hydroxyl Radical Reactions with Alkanes Using Machine Learning Lu, Junhui; Zhang, Huimin; Yu, Jinhui Journal of Chemical Information and Modeling, Vol. 61, Issue 9 https://doi.org/10.1021/acs.jcim.1c00809	journal	August 2021
Accelerated Saddle Point Refinement through Full Exploitation of Partial Hessian Diagonalization Hermes, Eric D.; Sargsyan, Khachik; Najm, Habib N. Journal of Chemical Theory and Computation, Vol. 15, Issue 11 https://doi.org/10.1021/acs.jctc.9b00869	journal	October 2019
A simple and efficient CCSD(T)-F12 approximation Adler, Thomas B.; Knizia, Gerald; Werner, Hans-Joachim The Journal of Chemical Physics, Vol. 127, Issue 22 https://doi.org/10.1063/1.2817618	journal	December 2007
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
Simplified CCSD(T)-F12 methods: Theory and benchmarks Knizia, Gerald; Adler, Thomas B.; Werner, Hans-Joachim The Journal of Chemical Physics, Vol. 130, Issue 5 https://doi.org/10.1063/1.3054300	journal	February 2009
Substructural fragments: an universal language to encode reactions, molecular and supramolecular structures Varnek, A.; Fourches, D.; Hoonakker, F. Journal of Computer-Aided Molecular Design, Vol. 19, Issue 9-10 https://doi.org/10.1007/s10822-005-9008-0	journal	September 2005
Optimization of parameters for semiempirical methods V: Modification of NDDO approximations and application to 70 elements Stewart, James J. P. Journal of Molecular Modeling, Vol. 13, Issue 12 https://doi.org/10.1007/s00894-007-0233-4	journal	September 2007
Multi-fidelity prediction of molecular optical peaks with deep learning Greenman, Kevin P.; Green, William H.; Gómez-Bombarelli, Rafael Chemical Science, Vol. 13, Issue 4 https://doi.org/10.1039/D1SC05677H	journal	January 2022
MSTor version 2013: A new version of the computer code for the multi-structural torsional anharmonicity, now with a coupled torsional potential Zheng, Jingjing; Meana-Pañeda, Rubén; Truhlar, Donald G. Computer Physics Communications, Vol. 184, Issue 8 https://doi.org/10.1016/j.cpc.2013.03.011	journal	August 2013
Methods for exploring reaction space in molecular systems: Exploring reaction space in molecular systems Dewyer, Amanda L.; Argüelles, Alonso J.; Zimmerman, Paul M. Wiley Interdisciplinary Reviews: Computational Molecular Science, Vol. 8, Issue 2 https://doi.org/10.1002/wcms.1354	journal	November 2017
Quantum chemistry structures and properties of 134 kilo molecules Ramakrishnan, Raghunathan; Dral, Pavlo O.; Rupp, Matthias Scientific Data, Vol. 1, Issue 1 https://doi.org/10.1038/sdata.2014.22	journal	August 2014
Using Machine Learning To Predict Suitable Conditions for Organic Reactions Gao, Hanyu; Struble, Thomas J.; Coley, Connor W. ACS Central Science, Vol. 4, Issue 11 https://doi.org/10.1021/acscentsci.8b00357	journal	November 2018
Rapid estimation of activation energy in heterogeneous catalytic reactions via machine learning: Rapid Estimation of Activation Energy in Heterogeneous Catalytic Reactions via Machine Learning Takahashi, Keisuke; Miyazato, Itsuki Journal of Computational Chemistry, Vol. 39, Issue 28 https://doi.org/10.1002/jcc.25567	journal	October 2018
High accuracy barrier heights, enthalpies, and rate coefficients for chemical reactions Spiekermann, Kevin; Pattanaik, Lagnajit; Green, William H. Scientific Data, Vol. 9, Issue 1 https://doi.org/10.1038/s41597-022-01529-6	journal	July 2022
Transition state geometry prediction using molecular group contributions Bhoorasingh, Pierre L.; West, Richard H. Physical Chemistry Chemical Physics, Vol. 17, Issue 48 https://doi.org/10.1039/C5CP04706D	journal	January 2015
A Decision Tree Based Machine Learning Algorithm for Rate Estimation Johnson, Matthew; Green, William 2019 AIChE Annual Meeting, Orlando, FL, Nov 10-15, 2019 https://doi.org/10.2172/1763640	conference	November 2019
A Machine Learning Approach for Rate Constants. II. Clustering, Training, and Predictions for the O(³P) + HCl → OH + Cl Reaction Nandi, Apurba; Bowman, Joel M.; Houston, Paul The Journal of Physical Chemistry A, Vol. 124, Issue 28 https://doi.org/10.1021/acs.jpca.0c04348	journal	June 2020
A Machine Learning Approach for Prediction of Rate Constants Houston, Paul L.; Nandi, Apurba; Bowman, Joel M. The Journal of Physical Chemistry Letters, Vol. 10, Issue 17 https://doi.org/10.1021/acs.jpclett.9b01810	journal	August 2019
Reactants, products, and transition states of elementary chemical reactions based on quantum chemistry Grambow, Colin A.; Pattanaik, Lagnajit; Green, William H. Scientific Data, Vol. 7, Issue 1 https://doi.org/10.1038/s41597-020-0460-4	journal	May 2020
Data enhanced Hammett-equation: reaction barriers in chemical space Bragato, Marco; von Rudorff, Guido Falk; von Lilienfeld, O. Anatole Chemical Science, Vol. 11, Issue 43 https://doi.org/10.1039/D0SC04235H	journal	January 2020
Detailed Reaction Mechanism for 350–400 °C Pyrolysis of an Alkane, Aromatic, and Long-Chain Alkylaromatic Mixture Payne, A. Mark; Spiekermann, Kevin A.; Green, William H. Energy & Fuels, Vol. 36, Issue 3 https://doi.org/10.1021/acs.energyfuels.1c03345	journal	January 2022
Quantum chemistry-augmented neural networks for reactivity prediction: Performance, generalizability, and explainability Stuyver, Thijs; Coley, Connor W. The Journal of Chemical Physics, Vol. 156, Issue 8 https://doi.org/10.1063/5.0079574	journal	February 2022
Ab initio group contribution method for activation energies for radical additions Saeys, Mark; Reyniers, Marie-Fran�oise; Marin, Guy B. AIChE Journal, Vol. 50, Issue 2 https://doi.org/10.1002/aic.10038	journal	January 2004
Acceleration of saddle-point searches with machine learning Peterson, Andrew A. The Journal of Chemical Physics, Vol. 145, Issue 7 https://doi.org/10.1063/1.4960708	journal	August 2016
Theoretical Kinetics Study of the O( ³ P) + CH ₄ /CD ₄ Hydrogen Abstraction Reaction: The Role of Anharmonicity, Recrossing Effects, and Quantum Mechanical Tunneling Gonzalez-Lavado, Eloisa; Corchado, Jose C.; Suleimanov, Yury V. The Journal of Physical Chemistry A, Vol. 118, Issue 18 https://doi.org/10.1021/jp5028965	journal	April 2014
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
Machine Learning Quantum Reaction Rate Constants Komp, Evan; Valleau, Stéphanie The Journal of Physical Chemistry A, Vol. 124, Issue 41 https://doi.org/10.1021/acs.jpca.0c05992	journal	September 2020
A gentle introduction to deep learning for graphs Bacciu, Davide; Errica, Federico; Micheli, Alessio Neural Networks, Vol. 129 https://doi.org/10.1016/j.neunet.2020.06.006	journal	September 2020
Current Status of Transition-State Theory Truhlar, Donald G.; Garrett, Bruce C.; Klippenstein, Stephen J. The Journal of Physical Chemistry, Vol. 100, Issue 31 https://doi.org/10.1021/jp953748q	journal	January 1996
Molecular Geometry Prediction using a Deep Generative Graph Neural Network Mansimov, Elman; Mahmood, Omar; Kang, Seokho Scientific Reports, Vol. 9, Issue 1 https://doi.org/10.1038/s41598-019-56773-5	journal	December 2019
Detailed Kinetic Modeling for the Pyrolysis of a Jet A Surrogate Vermeire, Florence H.; Aravindakshan, Syam Ukkandath; Jocher, Agnes Energy & Fuels, Vol. 36, Issue 3 https://doi.org/10.1021/acs.energyfuels.1c03315	journal	January 2022
Ensemble Methods in Machine Learning Dietterich, Thomas G. Multiple Classifier Systems https://doi.org/10.1007/3-540-45014-9_1	book	January 2000
A climbing image nudged elastic band method for finding saddle points and minimum energy paths Henkelman, Graeme; Uberuaga, Blas P.; Jónsson, Hannes The Journal of Chemical Physics, Vol. 113, Issue 22, p. 9901-9904 https://doi.org/10.1063/1.1329672	journal	December 2000
The 1-D hindered rotor approximation Pfaendtner, Jim; Yu, Xinrui; Broadbelt, Linda J. Theoretical Chemistry Accounts, Vol. 118, Issue 5-6 https://doi.org/10.1007/s00214-007-0376-5	journal	July 2007
Conformation Generation: The State of the Art Hawkins, Paul C. D. Journal of Chemical Information and Modeling, Vol. 57, Issue 8 https://doi.org/10.1021/acs.jcim.7b00221	journal	July 2017
Multi-structural variational transition state theory. Kinetics of the 1,4-hydrogen shift isomerization of the pentyl radical with torsional anharmonicity Yu, Tao; Zheng, Jingjing; Truhlar, Donald G. Chemical Science, Vol. 2, Issue 11 https://doi.org/10.1039/c1sc00225b	journal	January 2011
Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation Riniker, Sereina; Landrum, Gregory A. Journal of Chemical Information and Modeling, Vol. 55, Issue 12 https://doi.org/10.1021/acs.jcim.5b00654	journal	November 2015
Conformer Generation with OMEGA: Algorithm and Validation Using High Quality Structures from the Protein Databank and Cambridge Structural Database Hawkins, Paul C. D.; Skillman, A. Geoffrey; Warren, Gregory L. Journal of Chemical Information and Modeling, Vol. 50, Issue 4 https://doi.org/10.1021/ci100031x	journal	March 2010
Anharmonic zero point vibrational energies: Tipping the scales in accurate thermochemistry calculations? Pfeiffer, Florian; Rauhut, Guntram; Feller, David The Journal of Chemical Physics, Vol. 138, Issue 4 https://doi.org/10.1063/1.4777568	journal	January 2013
Generative adversarial networks for transition state geometry prediction Makoś, Małgorzata Z.; Verma, Niraj; Larson, Eric C. The Journal of Chemical Physics, Vol. 155, Issue 2 https://doi.org/10.1063/5.0055094	journal	July 2021
Intramolecular Hydrogen Migration in Alkylperoxy and Hydroperoxyalkylperoxy Radicals: Accurate Treatment of Hindered Rotors Sharma, Sandeep; Raman, Sumathy; Green, William H. The Journal of Physical Chemistry A, Vol. 114, Issue 18 https://doi.org/10.1021/jp9098792	journal	May 2010
Efficient exploration of reaction paths via a freezing string method Behn, Andrew; Zimmerman, Paul M.; Bell, Alexis T. The Journal of Chemical Physics, Vol. 135, Issue 22 https://doi.org/10.1063/1.3664901	journal	December 2011
Automated Discovery of Elementary Chemical Reaction Steps Using Freezing String and Berny Optimization Methods Suleimanov, Yury V.; Green, William H. Journal of Chemical Theory and Computation, Vol. 11, Issue 9 https://doi.org/10.1021/acs.jctc.5b00407	journal	August 2015
Unimolecular Reaction Pathways of a γ-Ketohydroperoxide from Combined Application of Automated Reaction Discovery Methods Grambow, Colin A.; Jamal, Adeel; Li, Yi-Pei Journal of the American Chemical Society, Vol. 140, Issue 3 https://doi.org/10.1021/jacs.7b11009	journal	January 2018
The importance of the Montreal Protocol in protecting climate Velders, G. J. M.; Andersen, S. O.; Daniel, J. S. Proceedings of the National Academy of Sciences, Vol. 104, Issue 12 https://doi.org/10.1073/pnas.0610328104	journal	March 2007
Exploring 1,2-Hydrogen Shift in Silicon Nanoparticles: Reaction Kinetics from Quantum Chemical Calculations and Derivation of Transition State Group Additivity Database Adamczyk, Andrew J.; Reyniers, Marie-Francoise; Marin, Guy B. The Journal of Physical Chemistry A, Vol. 113, Issue 41 https://doi.org/10.1021/jp9062516	journal	September 2009
Predictive Kinetics: A New Approach for the 21st Century Green, Jr., William H. Chemical Engineering Kinetics https://doi.org/10.1016/S0065-2377(07)32001-2	book	June 2007
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
A simple approximation for the vibrational partition function of a hindered internal rotation Truhlar, Donald G. Journal of Computational Chemistry, Vol. 12, Issue 2 https://doi.org/10.1002/jcc.540120217	journal	March 1991
Accurate Thermochemistry with Small Data Sets: A Bond Additivity Correction and Transfer Learning Approach Grambow, Colin A.; Li, Yi-Pei; Green, William H. The Journal of Physical Chemistry A, Vol. 123, Issue 27 https://doi.org/10.1021/acs.jpca.9b04195	journal	June 2019
A survey of factors contributing to accurate theoretical predictions of atomization energies and molecular structures Feller, David; Peterson, Kirk A.; Dixon, David A. The Journal of Chemical Physics, Vol. 129, Issue 20 https://doi.org/10.1063/1.3008061	journal	November 2008
Reaction Rate Prediction via Group Additivity Part 1: H Abstraction from Alkanes by H and CH₃ Sumathi, R.; Carstensen, H. -H.; Green, William H. The Journal of Physical Chemistry A, Vol. 105, Issue 28 https://doi.org/10.1021/jp010697q	journal	June 2001
Moving from postdictive to predictive kinetics in reaction engineering Green, William H. AIChE Journal, Vol. 66, Issue 11 https://doi.org/10.1002/aic.17059	journal	October 2020
Über die Reaktionsgeschwindigkeit bei der Inversion von Rohrzucker durch Säuren Arrhenius, Svante Zeitschrift für Physikalische Chemie, Vol. 4U, Issue 1 https://doi.org/10.1515/zpch-1889-0416	journal	January 1889
The Properties of Known Drugs. 1. Molecular Frameworks Bemis, Guy W.; Murcko, Mark A. Journal of Medicinal Chemistry, Vol. 39, Issue 15 https://doi.org/10.1021/jm9602928	journal	January 1996
Machine learning approaches to understand and predict rate constants for organic processes in mixtures containing ionic liquids Greaves, Tamar L.; Schaffarczyk McHale, Karin S.; Burkart-Radke, Raphael F. Physical Chemistry Chemical Physics, Vol. 23, Issue 4 https://doi.org/10.1039/D0CP04227G	journal	January 2021
KinBot: Automated stationary point search on potential energy surfaces Van de Vijver, Ruben; Zádor, Judit Computer Physics Communications, Vol. 248 https://doi.org/10.1016/j.cpc.2019.106947	journal	March 2020
A deep neural network combined with molecular fingerprints (DNN-MF) to develop predictive models for hydroxyl radical rate constants of water contaminants Zhong, Shifa; Hu, Jiajie; Fan, Xudong Journal of Hazardous Materials, Vol. 383 https://doi.org/10.1016/j.jhazmat.2019.121141	journal	February 2020
Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors Guan, Yanfei; Coley, Connor W.; Wu, Haoyang Chemical Science, Vol. 12, Issue 6 https://doi.org/10.1039/D0SC04823B	journal	January 2021
Fast and automated identification of reactions with low barriers: the decomposition of 3-hydroperoxypropanal Koerstz, Mads; Rasmussen, Maria H.; Jensen, Jan H. SciPost Chemistry, Vol. 1, Issue 1 https://doi.org/10.21468/SciPostChem.1.1.003	journal	October 2021
Deep Learning of Activation Energies Grambow, Colin A.; Pattanaik, Lagnajit; Green, William H. The Journal of Physical Chemistry Letters, Vol. 11, Issue 8 https://doi.org/10.1021/acs.jpclett.0c00500	journal	March 2020
Ab Initio Group Contribution Method for Activation Energies of Hydrogen Abstraction Reactions Saeys, Mark; Reyniers, Marie-Françoise; Van Speybroeck, Veronique ChemPhysChem, Vol. 7, Issue 1 https://doi.org/10.1002/cphc.200500206	journal	January 2006
Analyzing Learned Molecular Representations for Property Prediction Yang, Kevin; Swanson, Kyle; Jin, Wengong Journal of Chemical Information and Modeling, Vol. 59, Issue 8 https://doi.org/10.1021/acs.jcim.9b00237	journal	July 2019
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
Automatically generated model for light alkene combustion Pio, Gianmaria; Dong, Xiaorui; Salzano, Ernesto Combustion and Flame, Vol. 241 https://doi.org/10.1016/j.combustflame.2022.112080	journal	July 2022
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
Prediction of Organic Reaction Outcomes Using Machine Learning Coley, Connor W.; Barzilay, Regina; Jaakkola, Tommi S. ACS Central Science, Vol. 3, Issue 5 https://doi.org/10.1021/acscentsci.7b00064	journal	April 2017
Quantum Thermochemistry: Multistructural Method with Torsional Anharmonicity Based on a Coupled Torsional Potential Zheng, Jingjing; Truhlar, Donald G. Journal of Chemical Theory and Computation, Vol. 9, Issue 3 https://doi.org/10.1021/ct3010722	journal	February 2013
Kinetic Modeling of API Oxidation: (2) Imipramine Stress Testing Wu, Haoyang; Grinberg Dana, Alon; Ranasinghe, Duminda S. Molecular Pharmaceutics, Vol. 19, Issue 5 https://doi.org/10.1021/acs.molpharmaceut.2c00043	journal	April 2022
Machine Learning for Chemical Reactions Meuwly, Markus Chemical Reviews, Vol. 121, Issue 16 https://doi.org/10.1021/acs.chemrev.1c00033	journal	June 2021
A growing string method for determining transition states: Comparison to the nudged elastic band and string methods Peters, Baron; Heyden, Andreas; Bell, Alexis T. The Journal of Chemical Physics, Vol. 120, Issue 17 https://doi.org/10.1063/1.1691018	journal	May 2004
Computational Thermochemistry: Scale Factor Databases and Scale Factors for Vibrational Frequencies Obtained from Electronic Model Chemistries Alecu, I. M.; Zheng, Jingjing; Zhao, Yan Journal of Chemical Theory and Computation, Vol. 6, Issue 9 https://doi.org/10.1021/ct100326h	journal	August 2010
On Benchmarking of Automated Methods for Performing Exhaustive Reaction Path Search Maeda, Satoshi; Harabuchi, Yu Journal of Chemical Theory and Computation, Vol. 15, Issue 4 https://doi.org/10.1021/acs.jctc.8b01182	journal	March 2019
Low-cost prediction of molecular and transition state partition functions via machine learning Komp, Evan; Valleau, Stéphanie Chemical Science, Vol. 13, Issue 26 https://doi.org/10.1039/D2SC01334G	journal	January 2022
Harmonic Vibrational Frequencies: An Evaluation of Hartree−Fock, Møller−Plesset, Quadratic Configuration Interaction, Density Functional Theory, and Semiempirical Scale Factors Scott, Anthony P.; Radom, Leo The Journal of Physical Chemistry, Vol. 100, Issue 41 https://doi.org/10.1021/jp960976r	journal	January 1996
Assessment of basis sets for F12 explicitly-correlated molecular electronic-structure methods Bischoff, Florian A.; Wolfsegger, Sandra; Tew, David P. Molecular Physics, Vol. 107, Issue 8-12 https://doi.org/10.1080/00268970802708942	journal	April 2009
Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction Heid, Esther; Green, William H. Journal of Chemical Information and Modeling, Vol. 62, Issue 9 https://doi.org/10.1021/acs.jcim.1c00975	journal	November 2021
Best practices in machine learning for chemistry Artrith, Nongnuch; Butler, Keith T.; Coudert, François-Xavier Nature Chemistry, Vol. 13, Issue 6 https://doi.org/10.1038/s41557-021-00716-z	journal	May 2021
Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens Devereux, Christian; Smith, Justin S.; Davis, Kate K. Journal of Chemical Theory and Computation, Vol. 16, Issue 7 https://doi.org/10.1021/acs.jctc.0c00121	journal	June 2020
Transfer learning for solvation free energies: From quantum chemistry to experiments Vermeire, Florence H.; Green, William H. Chemical Engineering Journal, Vol. 418 https://doi.org/10.1016/j.cej.2021.129307	journal	August 2021

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