Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session S46: Emerging Trends in Molecular Dynamics Simulations and Machine Learning I
8:00 AM–11:00 AM,
Thursday, March 17, 2022
Room: McCormick Place W-470A
Sponsoring Units: DCOMP GDS DSOFT DPOLY
Chair: Priya Vashishta, University of Southern California
Abstract: S46.00005 : Many-body interatomic potential with Bayesian active learning, an application ofSiC*
9:12 AM–9:24 AM
Yu Xie, Jonathan Vandermause, Senja Ramakers, Nakib H. Protik, Anders Johansson, and Boris Kozinsky
As an application, we train a MLIP model for silicon carbide (SiC), a wide-gap semiconductor with diverse applications in power electronics, nuclear physics and astronomy. Particularly, the phase transition of SiC under high pressure is investigated, and is captured during active learning facilitated by the uncertainty prediction of the model. We demonstrate that the trained MLIP reaches excellent agreement with the density functional theory and outperforms the empirical potentials in the prediction of elastic and thermal properties of pristine bulks, as well as the enthalpy under different pressures ranging from 0-150 GPa. The highly efficient active learning workflow can be easily extended to other systems, accelerate material discovery and facilitate the development of quantum technologies.
*Y.X. is supported by the US Department of Energy (DOE) Office of Basic Energy Sciences under Award No. DE-SC0020128.
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