Work

Atomistic learning in the electronically grand-canonical ensemble

Year: 2023

Type: article

Abstract: A strategy is presented for the machine-learning emulation of electronic structure calculations carried out in the electronically grand-canonical ensemble. The approach relies upon a dual-learning scheme, where both the system charge and the system energy are predicted for each image. The scheme is shown to be capable of emulating basic electrochemical reactions at a range of potentials, and coupling it with a bootstrap-ensemble approach gives reasonable estimates of the prediction uncertainty. The method is also demonstrated to accelerate saddle-point searches, and to extrapolate to systems with one to five water layers. We anticipate that this method will allow for larger length- and time-scale simulations necessary for electrochemical simulations.

Source: npj Computational Materials

DOI: 10.1038/s41524-023-01007-6

Authors: Xi Chen, Muammar El Khatib, Per Lindgren, Adam Willard, Andrew J. Medford, Andrew A. Peterson

Institutions: Brown University, Massachusetts Institute of Technology, Georgia Institute of Technology

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,190

Funders: National Science Foundation, U.S. Department of Energy, Brown University, Basic Energy Sciences