Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

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

doi:10.1021/acs.jctc.5b00099

Title: Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

Journal Article · Fri Apr 10 00:00:00 EDT 2015 · Journal of Chemical Theory and Computation

DOI:https://doi.org/10.1021/acs.jctc.5b00099· OSTI ID:1392925

Ramakrishnan, Raghunathan ^[1]; Dral, Pavlo O. ^[2]; Rupp, Matthias ^[1]; von Lilienfeld, O. Anatole ^[3]

University of Basel (Switzerland)
Max-Planck-Institut für Kohlenforschung, Mülheim an der Ruhr (Germany); Friedrich-Alexander University Erlangen-Nuremberg, Bamberg (Germany)
University of Basel (Switzerland); Argonne National Laboratory (ANL), Argonne, IL (United States). Argonne Leadership Computing Facility (ALCF)

Chemically accurate and comprehensive studies of the virtual space of all possible molecules are severely limited by the computational cost of quantum chemistry. Here we introduce a composite strategy that adds machine learning corrections to computationally inexpensive approximate legacy quantum methods. After training, highly accurate predictions of enthalpies, free energies, entropies, and electron correlation energies are possible, for significantly larger molecular sets than used for training. For thermochemical properties of up to 16k isomers of C₇H₁₀O₂ we present numerical evidence that chemical accuracy can be reached. We also predict electron correlation energy in post Hartree-Fock methods, at the computational cost of HartreeFock, and we establish a qualitative relationship between molecular entropy and electron correlation. The transferability of our approach is demonstrated, using semiempirical quantum chemistry and machine learning models trained on 1 and 10% of 134k organic molecules, to reproduce enthalpies of all remaining molecules at density functional theory level of accuracy.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC); Swiss National Science Foundation (SNSF)

Grant/Contract Number:: AC02-06CH11357; PP00P2_138932

OSTI ID:: 1392925

Journal Information:: Journal of Chemical Theory and Computation, Vol. 11, Issue 5; ISSN 1549-9618

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 486 works

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References (43)

Chemical space Kirkpatrick, Peter; Ellis, Clare Nature, Vol. 432, Issue 7019 https://doi.org/10.1038/432823a	journal	December 2004
Stochastic Voyages into Uncharted Chemical Space Produce a Representative Library of All Possible Drug-Like Compounds Virshup, Aaron M.; Contreras-García, Julia; Wipf, Peter Journal of the American Chemical Society, Vol. 135, Issue 19 https://doi.org/10.1021/ja401184g	journal	May 2013
First principles view on chemical compound space: Gaining rigorous atomistic control of molecular properties von Lilienfeld, O. Anatole International Journal of Quantum Chemistry, Vol. 113, Issue 12 https://doi.org/10.1002/qua.24375	journal	February 2013
Combinatorial thinking in chemistry and biology Ellman, J.; Stoddard, B.; Wells, J. Proceedings of the National Academy of Sciences, Vol. 94, Issue 7 https://doi.org/10.1073/pnas.94.7.2779	journal	April 1997
Click Chemistry: Diverse Chemical Function from a Few Good Reactions Kolb, Hartmuth C.; Finn, M. G.; Sharpless, K. Barry Angewandte Chemie International Edition, Vol. 40, Issue 11, p. 2004-2021 https://doi.org/10.1002/1521-3773(20010601)40:11<2004::AID-ANIE2004>3.0.CO;2-5	journal	June 2001
Towards the computational design of solid catalysts Nørskov, J.; Bligaard, T.; Rossmeisl, J. Nature Chemistry, Vol. 1, Issue 1, p. 37-46 https://doi.org/10.1038/nchem.121	journal	April 2009
The Many Roles of Computation in Drug Discovery Jorgensen, W. L. Science, Vol. 303, Issue 5665 https://doi.org/10.1126/science.1096361	journal	March 2004
De novodesign: balancing novelty and confined chemical space Kutchukian, Peter S.; Shakhnovich, Eugene I. Expert Opinion on Drug Discovery, Vol. 5, Issue 8 https://doi.org/10.1517/17460441.2010.497534	journal	June 2010
The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid Hachmann, Johannes; Olivares-Amaya, Roberto; Atahan-Evrenk, Sule The Journal of Physical Chemistry Letters, Vol. 2, Issue 17 https://doi.org/10.1021/jz200866s	journal	August 2011
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
Inverse Strategies for Molecular Design Kuhn, Christoph; Beratan, David N. The Journal of Physical Chemistry, Vol. 100, Issue 25 https://doi.org/10.1021/jp960518i	journal	January 1996
The inverse band-structure problem of finding an atomic configuration with given electronic properties Franceschetti, Alberto; Zunger, Alex Nature, Vol. 402, Issue 6757 https://doi.org/10.1038/46995	journal	November 1999
Variational Particle Number Approach for Rational Compound Design von Lilienfeld, O. Anatole; Lins, Roberto D.; Rothlisberger, Ursula Physical Review Letters, Vol. 95, Issue 15 https://doi.org/10.1103/PhysRevLett.95.153002	journal	October 2005
Designing Molecules by Optimizing Potentials Wang, Mingliang; Hu, Xiangqian; Beratan, David N. Journal of the American Chemical Society, Vol. 128, Issue 10 https://doi.org/10.1021/ja0572046	journal	March 2006
A comprehensive chemical kinetic combustion model for the four butanol isomers Sarathy, S. Mani; Vranckx, Stijn; Yasunaga, Kenji Combustion and Flame, Vol. 159, Issue 6 https://doi.org/10.1016/j.combustflame.2011.12.017	journal	June 2012
Combustion and pyrolysis of iso-butanol: Experimental and chemical kinetic modeling study Merchant, Shamel S.; Zanoelo, Everton Fernando; Speth, Raymond L. Combustion and Flame, Vol. 160, Issue 10 https://doi.org/10.1016/j.combustflame.2013.04.023	journal	October 2013
Chemical Kinetic Data Base for Combustion Chemistry. Part I. Methane and Related Compounds Tsang, W.; Hampson, R. F. Journal of Physical and Chemical Reference Data, Vol. 15, Issue 3 https://doi.org/10.1063/1.555759	journal	July 1986
Ab initio quantum chemistry: Methodology and applications Friesner, R. A. Proceedings of the National Academy of Sciences, Vol. 102, Issue 19 https://doi.org/10.1073/pnas.0408036102	journal	May 2005
Gaussian‐1 theory: A general procedure for prediction of molecular energies Pople, John A.; Head‐Gordon, Martin; Fox, Douglas J. The Journal of Chemical Physics, Vol. 90, Issue 10 https://doi.org/10.1063/1.456415	journal	May 1989
Gaussian‐2 theory for molecular energies of first‐ and second‐row compounds Curtiss, Larry A.; Raghavachari, Krishnan; Trucks, Gary W. The Journal of Chemical Physics, Vol. 94, Issue 11 https://doi.org/10.1063/1.460205	journal	June 1991
Gaussian-4 theory Curtiss, Larry A.; Redfern, Paul C.; Raghavachari, Krishnan The Journal of Chemical Physics, Vol. 126, Issue 8 https://doi.org/10.1063/1.2436888	journal	February 2007
Gaussian-4 theory using reduced order perturbation theory Curtiss, Larry A.; Redfern, Paul C.; Raghavachari, Krishnan The Journal of Chemical Physics, Vol. 127, Issue 12 https://doi.org/10.1063/1.2770701	journal	September 2007
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
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
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
First-principles energetics of water clusters and ice: A many-body analysis Gillan, M. J.; Alfè, D.; Bartók, A. P. The Journal of Chemical Physics, Vol. 139, Issue 24 https://doi.org/10.1063/1.4852182	journal	December 2013
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
Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies Hansen, Katja; Montavon, Grégoire; Biegler, Franziska Journal of Chemical Theory and Computation, Vol. 9, Issue 8 https://doi.org/10.1021/ct400195d	journal	July 2013
Machine learning of molecular electronic properties in chemical compound space Montavon, Grégoire; Rupp, Matthias; Gobre, Vivekanand New Journal of Physics, Vol. 15, Issue 9 https://doi.org/10.1088/1367-2630/15/9/095003	journal	September 2013
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
Challenges for Density Functional Theory Cohen, Aron J.; Mori-Sánchez, Paula; Yang, Weitao Chemical Reviews, Vol. 112, Issue 1 https://doi.org/10.1021/cr200107z	journal	December 2011
Orthogonalization corrections for semiempirical methods Weber, Wolfgang; Thiel, Walter Theoretical Chemistry Accounts: Theory, Computation, and Modeling (Theoretica Chimica Acta), Vol. 103, Issue 6 https://doi.org/10.1007/s002149900083	journal	April 2000
Discovering chemistry with an ab initio nanoreactor Wang, Lee-Ping; Titov, Alexey; McGibbon, Robert Nature Chemistry, Vol. 6, Issue 12 https://doi.org/10.1038/nchem.2099	journal	November 2014
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
Self-consistent-charge density-functional tight-binding method for simulations of complex materials properties Elstner, M.; Porezag, D.; Jungnickel, G. Physical Review B, Vol. 58, Issue 11, p. 7260-7268 https://doi.org/10.1103/PhysRevB.58.7260	journal	September 1998
Functional designed to include surface effects in self-consistent density functional theory Armiento, R.; Mattsson, A. E. Physical Review B, Vol. 72, Issue 8 https://doi.org/10.1103/PhysRevB.72.085108	journal	August 2005
SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules Weininger, David Journal of Chemical Information and Modeling, Vol. 28, Issue 1 https://doi.org/10.1021/ci00057a005	journal	February 1988
Open Babel: An open chemical toolbox O'Boyle, Noel M.; Banck, Michael; James, Craig A. Journal of Cheminformatics, Vol. 3, Issue 1 https://doi.org/10.1186/1758-2946-3-33	journal	October 2011
From atoms and bonds to three-dimensional atomic coordinates: automatic model builders Sadowski, Jens.; Gasteiger, Johann. Chemical Reviews, Vol. 93, Issue 7 https://doi.org/10.1021/cr00023a012	journal	November 1993
Optimization of parameters for semiempirical methods VI: more modifications to the NDDO approximations and re-optimization of parameters Stewart, James J. P. Journal of Molecular Modeling, Vol. 19, Issue 1 https://doi.org/10.1007/s00894-012-1667-x	journal	November 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
Data-mined similarity function between material compositions Yang, Lusann; Ceder, Gerbrand Physical Review B, Vol. 88, Issue 22 https://doi.org/10.1103/physrevb.88.224107	journal	December 2013
Quantum chemistry structures and properties of 134 kilo molecules Raghunathan, Ramakrishnan,; O., Dral, Pavlo; Matthias, Rupp, Springer Nature https://doi.org/10.5451/unibas-ep43357	text	January 2014

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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
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Breaking the Coupled Cluster Barrier for Machine-Learned Potentials of Large Molecules: The Case of 15-Atom Acetylacetone Qu, Chen; Houston, Paul L.; Conte, Riccardo The Journal of Physical Chemistry Letters, Vol. 12, Issue 20 https://doi.org/10.1021/acs.jpclett.1c01142	journal	May 2021
Learning a Local-Variable Model of Aromatic and Conjugated Systems Matlock, Matthew K.; Dang, Na Le; Swamidass, S. Joshua ACS Central Science, Vol. 4, Issue 1 https://doi.org/10.1021/acscentsci.7b00405	journal	December 2017
Read between the Molecules: Computational Insights into Organic Semiconductors Gryn’ova, Ganna; Lin, Kun-Han; Corminboeuf, Clémence Journal of the American Chemical Society, Vol. 140, Issue 48 https://doi.org/10.1021/jacs.8b07985	journal	November 2018
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
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
Energy refinement and analysis of structures in the QM9 database via a highly accurate quantum chemical method Kim, Hyungjun; Park, Ji Young; Choi, Sunghwan Scientific Data, Vol. 6, Issue 1 https://doi.org/10.1038/s41597-019-0121-7	journal	July 2019
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Deep elastic strain engineering of bandgap through machine learning Shi, Zhe; Tsymbalov, Evgenii; Dao, Ming Proceedings of the National Academy of Sciences, Vol. 116, Issue 10 https://doi.org/10.1073/pnas.1818555116	journal	February 2019
A mixed quantum chemistry/machine learning approach for the fast and accurate prediction of biochemical redox potentials and its large-scale application to 315,000 redox reactions Jinich, Adrian; Sanchez-Lengeling, Benjamin; Ren, Haniu ACS Central Science https://doi.org/10.1101/245357	journal	April 2019
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
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Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns Cova, Tânia F. G. G.; Pais, Alberto A. C. C. Frontiers in Chemistry, Vol. 7 https://doi.org/10.3389/fchem.2019.00809	journal	November 2019
Modelling Chemical Reasoning to Predict Reactions Segler, Marwin H. S.; Waller, Mark P. arXiv https://doi.org/10.48550/arxiv.1608.07117	text	January 2016
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Resolution limit of data-driven coarse-grained models spanning chemical space Kanekal, Kiran H.; Bereau, Tristan arXiv https://doi.org/10.48550/arxiv.1907.04082	text	January 2019
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Basis set convergence and extrapolation of connected triple excitation contributions (T) in computational thermochemistry: the W4-17 benchmark with up to k functions Martin, Jan M. L. arXiv https://doi.org/10.48550/arxiv.2103.14370	text	January 2021

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