Journal
NATURE COMMUNICATIONS
Volume 8, Issue -, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41467-017-00839-3
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Funding
- NSF [CHE-1464795]
- Einstein Foundation
- Institute for Information & Communications Technology Promotion (IITP) - Korea government [2017-0-00451]
- US Army Research Office [W911NF-13-1-0387]
- Direct For Mathematical & Physical Scien
- Division Of Chemistry [1464795] Funding Source: National Science Foundation
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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.
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