Atomistic learning in the electronically grand-canonical ensemble

• Published in: npj computational materials Vol. 9; no. 1; pp. 73 - 9
• Main Authors: Chen, Xi, El Khatib, Muammar, Lindgren, Per, Willard, Adam, Medford, Andrew J., Peterson, Andrew A.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 11-05-2023; Nature Publishing Group; Nature Portfolio
• Subjects: 639/301/1034/1037; 639/301/299/161; 639/638/563; Characterization and Evaluation of Materials; Chemical reactions; Chemistry; Chemistry and Materials Science; Computational Intelligence; Computational methods; Electrochemistry; Electrodes; Electronic structure; Electrons; Energy; Energy charge; Machine learning; MATERIALS SCIENCE; Mathematical and Computational Engineering; Mathematical and Computational Physics; Mathematical Modeling and Industrial Mathematics; Neural networks; Saddle points; Simulation; Solvents; Teaching methods; Theoretical; Theoretical chemistry
• ISSN: 2057-3960; 2057-3960
• DOI: 10.1038/s41524-023-01007-6

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.