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 B22: Emerging Trends in MD Simulations and Machine Learning I
11:30 AM–2:30 PM,
Monday, March 15, 2021
Sponsoring
Units:
DCOMP GDS DSOFT DPOLY
Chair: Rajiv Kalia, Univ of Southern California
Abstract: B22.00005 : Accurate and Efficient ML Force Fields for Hundreds of Atoms
12:42 PM–12:54 PM
Live
Presenter:
Stefan Chmiela
(Tech Univ Berlin)
Authors:
Stefan Chmiela
(Tech Univ Berlin)
Valentin Vassilev Galindo
(Univ Luxembourg)
Huziel Sauceda
(Tech Univ Berlin)
Klaus-Robert Muller
(Tech Univ Berlin)
Alexandre Tkatchenko
(Univ Luxembourg)
To overcome this limitation, we develop an efficient iterative, parameter-free solver to train symmetric gradient domain machine learning (sGDML) [Chmiela et al., 2018] potentials for systems with several hundred atoms. Our approach keeps all correlations of this global model intact, allowing the accurate description of complex molecules, materials and molecular assemblies.
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