Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session P22: Emerging Trends in MD Simulations and Machine Learning V
3:00 PM–5:48 PM,
Wednesday, March 17, 2021
Sponsoring
Units:
DCOMP GDS DSOFT DPOLY
Chair: Tao Wei, Howard University
Abstract: P22.00010 : Investigation of global charge distributions for constructing non-local machine learning potentials*
5:12 PM–5:24 PM
Live
Presenter:
Tsz Wai Ko
(Theoretische Chemie, Georg-August-Universität Göttingen)
Authors:
Tsz Wai Ko
(Theoretische Chemie, Georg-August-Universität Göttingen)
Jonas Finkler
(Physics, University of Basel)
Stefan A Goedecker
(Physics, University of Basel)
Jorg Behler
(Theoretische Chemie, Georg-August-Universität Göttingen)
Machine learning potentials (MLPs) play a vital role in atomistic simulations due to their nearly ab-initio accuracy and their high computational efficiency similar to empirical force fields. However, most MLPs depend on local atomic environments only without taking global charge distributions into account thus neglecting non-local effects. Recently, some non-local MLPs have been proposed [1-3] for tackling these problems. They are not only based on local environments but also on non-local electronic information, i.e. atomic charges depending on long-range charge transfer and the global charge. Here we use fourth-generation high-dimensional neural network potentials [3] to illustrate the role of non-local effects and suggest possible improvements for current state-of-the-art MLPs.
References:
1: Xie, X.; Persson, K. A.; Small, D. W. J. Chem. Theory Comput. 2020, 16, 4256–4270
2: Zubatyuk, R.; Smith, J. S.; Leszczynski, J.; Isayev, O. Sci. Adv. 2019, 5, eaav6490
3: Ko, T. W.; Finkler, J. A.; Goedecker, S.; Behler, J. arXiv:2009.064842020
*
## Funding Acknowledgement
We acknowledge financial support from the Deutsche
Forschungsgemeinschaft (DFG) (BE3264/13-1, project number 411538199) and
the Swiss National Science Foundation (SNF) (project number 182877).
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