Retrospective on a decade of machine learning for chemical discovery

Standfirst Over the last decade, we have witnessed the emergence of ever more machine learning applications in all aspects of the chemical sciences. Here, we highlight specific achievements of machine learning models in the field of computational chemistry by considering selected studies of electronic structure, interatomic potentials, and chemical compound space in chronological order.

[1]  P. Dirac.  Quantum Mechanics of Many-Electron Systems , 1929 .

[2]  W. Kohn,et al.  Self-Consistent Equations Including Exchange and Correlation Effects , 1965 .

[3]  Michele Parrinello,et al.  Generalized neural-network representation of high-dimensional potential-energy surfaces. , 2007, Physical review letters.

[4]  Anubhav Jain,et al.  Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory , 2010 .

[5]  R. Kondor,et al.  Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. , 2009, Physical review letters.

[6]  K. Müller,et al.  Fast and accurate modeling of molecular atomization energies with machine learning. , 2011, Physical review letters.

[7]  John C. Snyder,et al.  Orbital-free bond breaking via machine learning. , 2013, The Journal of chemical physics.

[8]  R. Kondor,et al.  On representing chemical environments , 2012, 1209.3140.

[9]  Pavlo O. Dral,et al.  Quantum chemistry structures and properties of 134 kilo molecules , 2014, Scientific Data.

[10]  Zhenwei Li,et al.  Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces. , 2015, Physical review letters.

[11]  M. Rupp,et al.  Machine Learning for Quantum Mechanical Properties of Atoms in Molecules , 2015, 1505.00350.

[12]  Felix A Faber,et al.  Machine Learning Energies of 2 Million Elpasolite (ABC_{2}D_{6}) Crystals. , 2015, Physical review letters.

[13]  Kun Yao,et al.  Kinetic Energy of Hydrocarbons as a Function of Electron Density and Convolutional Neural Networks. , 2015, Journal of chemical theory and computation.

[14]  Li Li,et al.  Bypassing the Kohn-Sham equations with machine learning , 2016, Nature Communications.

[15]  Klaus-Robert Müller,et al.  Machine learning of accurate energy-conserving molecular force fields , 2016, Science Advances.

[16]  Gabor Csanyi,et al.  Achieving DFT accuracy with a machine-learning interatomic potential: thermomechanics and defects in bcc ferromagnetic iron , 2017, 1706.10229.

[17]  J. Behler.  First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems. , 2017, Angewandte Chemie.

[18]  O. Isayev,et al.  ible neural network potential with DFT accuracy at force fi eld computational cost † , 2017 .

[19]  J S Smith,et al.  ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost , 2016, Chemical science.

[20]  Thomas F. Miller,et al.  Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis. , 2018, Journal of chemical theory and computation.

[21]  Kieron Burke,et al.  Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry. , 2018, The Journal of chemical physics.

[22]  Lu-Ming Duan,et al.  Machine learning meets quantum physics , 2019, Physics Today.

[23]  David Pfau,et al.  Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks , 2019, Physical Review Research.

[24]  Yang Yang,et al.  Accurate molecular polarizabilities with coupled cluster theory and machine learning , 2018, Proceedings of the National Academy of Sciences.

[25]  E Weinan,et al.  Pushing the Limit of Molecular Dynamics with Ab Initio Accuracy to 100 Million Atoms with Machine Learning , 2020, SC20: International Conference for High Performance Computing, Networking, Storage and Analysis.

[26]  G. Carleo,et al.  Fermionic neural-network states for ab-initio electronic structure , 2019, Nature Communications.

[27]  F. Noé,et al.  Deep-neural-network solution of the electronic Schrödinger equation , 2019, Nature Chemistry.

[28]  Ryo Nagai,et al.  Completing density functional theory by machine learning hidden messages from molecules , 2019, npj Computational Materials.

[29]  K. Müller,et al.  Exploring chemical compound space with quantum-based machine learning , 2019, Nature Reviews Chemistry.

[30]  A. Tkatchenko,et al.  Machine Learning Meets Quantum Physics , 2020, Lecture Notes in Physics.

[31]  Anders S. Christensen,et al.  Quantum Machine Learning with Response Operators in Chemical Compound Space , 2020, Machine Learning Meets Quantum Physics.

[32]  Clémence Corminboeuf,et al.  Simulating solvation and acidity in complex mixtures with first-principles accuracy: the case of CH3SO3H and H2O2 in phenol. , 2020, Journal of chemical theory and computation.