Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session N53: AI and Materials III
11:30 AM–2:06 PM,
Wednesday, March 8, 2023
Room: Room 307
Sponsoring
Units:
GDS DMP
Chair: Trevor Rhone; Ayana Ghosh, Oak Ridge National Lab
Abstract: N53.00009 : Proton dynamics simulations of solid-acid electrolytes using active learning and equivariant neural network force fields.
1:06 PM–1:18 PM
Presenter:
Menghang (David) Wang
(Harvard University)
Authors:
Menghang (David) Wang
(Harvard University)
Cameron J Owen
(Harvard University)
Yu Xie
(Harvard University)
Simon L Batzner
(Harvard University)
Albert Musaelian
(Harvard University)
Anders Johansson
(Harvard University)
Boris Kozinsky
(Harvard University)
In this work, we develop machine learning interatomic force fields for CsH2PO4 and CsHSO4 superprotonic conductors combining ab-initio accuracy with scalability to large system sizes and nanosecond time scale. First, we use Bayesian active learning framework FLARE[1] to generate training data containing various atomic configurations. Then, we train an equivariant neural network potential Allegro[2] and deploy it in large-scale molecular dynamics. After demonstrating the fidelity of the machine learning potential by comparing with experimental diffusivities and activation energies, we study the dynamics of oxyanions and protons to identify the rate-limiting step for conduction.
[1] arXiv:2203.03824
[2] arXiv:2204.05249
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