Bypassing the Kohn-Sham equations with machine learning

By Felix Brockherde, Leslie Vogt, Li Li, Mark E. Tuckerman, Kieron Burke, K. Müller. 2016

663 citations recorded by the source corpus.

Last year, at least 30,000 scientific papers used the Kohn–Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields. Machine learning holds the promise of learning the energy functional via examples, bypassing the need to solve the Kohn–Sham equations. This should yield substantial savings in computer time, allowing larger systems and/or longer time-scales to be tackled, but attempts to machine-learn this functional have been limited by the need to find its derivative. The present work overcomes this difficulty by directly learning the density-potential and energy-density maps for test systems and various molecules. We perform the first molecular dynamics simulation with a machine-learned density functional on malonaldehyde and are able to capture the intramolecular proton transfer process. Learning density models now allows the construction of accurate density functionals for realistic molecular systems. Machine learning allows electronic structure calculations to access larger system sizes and, in dynamical simulations, longer time scales. Here, the authors perform such a simulation using a machine-learned density functional that avoids direct solution of the Kohn-Sham equations.

Subfields: Atomic and Molecular Physics, and Optics, Materials Chemistry, Physical and Theoretical Chemistry, Computational Chemistry & Drug Discovery, AI for Physical Sciences

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Citations in the corpus

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An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage (2020, positive): “One particularly promising approach is to use machine learning [33].”
Machine learning for molecular simulation (2019, background): “The seminal work of Behler and Parrinello in this direction [7] has opened the way to a now rapidly advancing area of research [8, 9, 10, 11, 12, 13, 14, 15].”
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