Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session N49: Emerging Trends in Molecular Dynamics Simulations and Machine Learning III
11:30 AM–2:18 PM,
Wednesday, March 16, 2022
Room: McCormick Place W-471B
Sponsoring
Units:
DCOMP GDS DSOFT DPOLY
Chair: Aravind Krishnamoorthy, University of Southern California
Abstract: N49.00006 : Application of Machine Learning to the Development of Ti Interatomic Potentials*
12:54 PM–1:06 PM
Presenter:
Sean J O'Connor
(Honeywell FM&T)
Authors:
Sean J O'Connor
(Honeywell FM&T)
Volker Eyert
(Materials Design, Inc)
Jörg-Rüdiger Hill
(Materials Design, Inc)
David Reith
(Materials Design, Inc)
Erich Wimmer
(Materials Design, Inc)
Patrick R Thomas
(Honeywell FM&T)
Ben Sikora
(Honeywell FM&T)
Paul Rulis
(University of Missouri - Kansas City)
In recent years, machine learning (ML) has emerged as a versatile and powerful tool within the scope of computational physics. Here, the viability of ML to more efficiently improve the accuracy of less sophisticated IPs for a titanium system is explored. The approach uses ML to optimize less sophisticated IPs through the use of an artificial neural network (ANN) which seeks to identify and apply more fitted parameters which create a “delta”. This delta, when applied to an IP, creates a hybridized IP. The intent is for the hybridized IP to have increased usefulness, with a lower computational cost as compared to traditional methods. This work ties into a larger effort to create a streamlined and automated process for making and fitting analytic IPs to facilitate high-throughput calculations.
*The Department of Energy’s Kansas City National Security Campus is operated and managed by Honeywell Federal Manufacturing & Technologies, LLC under contract number DE-NA0002839NSC-614-4104 dated 10/21 Unlimited Uncalssified Release
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