Journal
JOURNAL OF CHEMICAL PHYSICS
Volume 148, Issue 24, Pages -Publisher
AIP Publishing
DOI: 10.1063/1.5019779
Keywords
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Funding
- Federal Ministry of Education and Research (BMBF) [01IS14013A]
- DFG [MU 987/20-1]
- European Union [657679]
- BK21 program - Korean National Research Foundation [2012-005741]
- Institute for Information & Communications Technology Promotion (IITP) grant - Korea government [2017-0-00451]
- European Research Council (ERC-CoG grant BeStMo)
- Marie Curie Actions (MSCA) [657679] Funding Source: Marie Curie Actions (MSCA)
- Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea [2017-0-00451-002] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning, in general, and deep learning, in particular, are ideally suitable for representing quantum-mechanical interactions, enabling us to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for molecules and materials, where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study on the quantum-mechanical properties of C-20-fullerene that would have been infeasible with regular ab initio molecular dynamics. Published by AIP Publishing.
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