TY - JOUR
T1 - End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems
AU - Zhang, Linfeng
AU - Han, Jiequn
AU - Wang, Han
AU - Saidi, Wissam A.
AU - Car, Roberto
AU - E, Weinan
N1 - Funding Information:
We thank the anonymous reviewers for their careful reading of our manuscript and insightful comments and suggestions. The work of L. Z., J. H., and W. E is supported in part by ONR grant N00014-13-1-0338, DOE grants DE-SC0008626 and DE-SC0009248, and NSFC grants U1430237 and 91530322. The work of R. C. is supported in part by DOE grant DE-SC0008626. The work of H. W. is supported by the National Science Foundation of China under Grants 11501039 and 91530322, the National Key Research and Development Program of China under Grants 2016YFB0201200 and 2016YFB0201203, and the Science Challenge Project No. JCKY2016212A502. W.A.S. acknowledges financial support from National Science Foundation (DMR-1809085). We are grateful for computing time provided in part by the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation (# NSF OCI-1053575), the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357, the National Energy Research Scientific Computing Center (NERSC), which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, and the Terascale Infrastructure for Groundbreaking Research in Science and Engineering (TIGRESS) High Performance Computing Center and Visualization Laboratory at Princeton University.
Publisher Copyright:
© 2018 Curran Associates Inc..All rights reserved.
PY - 2018
Y1 - 2018
N2 - Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES of a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserves all the natural symmetries of the system. Further, we show that DeepPot-SE describes finite and extended systems including organic molecules, metals, semiconductors, and insulators with high fidelity.
AB - Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES of a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserves all the natural symmetries of the system. Further, we show that DeepPot-SE describes finite and extended systems including organic molecules, metals, semiconductors, and insulators with high fidelity.
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M3 - Conference article
AN - SCOPUS:85064839298
SN - 1049-5258
VL - 2018-December
SP - 4436
EP - 4446
JO - Advances in Neural Information Processing Systems
JF - Advances in Neural Information Processing Systems
T2 - 32nd Conference on Neural Information Processing Systems, NeurIPS 2018
Y2 - 2 December 2018 through 8 December 2018
ER -