CUED Publications database

Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide

Sivaraman, G and Krishnamoorthy, AN and Baur, M and Holm, C and Stan, M and Csányi, G and Benmore, C and Vázquez-Mayagoitia, Á (2020) Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide. npj Computational Materials, 6.

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© 2020, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply. We propose an active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP). Our active learning scheme consists of an unsupervised machine learning (ML) scheme coupled with a Bayesian optimization technique that evaluates the GAP model. We apply this scheme to a Hafnium dioxide (HfO2) dataset generated from a “melt-quench” ab initio molecular dynamics (AIMD) protocol. Our results show that the active learning scheme, with no prior knowledge of the dataset, is able to extract a configuration that reaches the required energy fit tolerance. Further, molecular dynamics (MD) simulations performed using this active learned GAP model on 6144 atom systems of amorphous and liquid state elucidate the structural properties of HfO2 with near ab initio precision and quench rates (i.e., 1.0 K/ps) not accessible via AIMD. The melt and amorphous X-ray structural factors generated from our simulation are in good agreement with experiment. In addition, the calculated diffusion constants are in good agreement with previous ab initio studies.

Item Type: Article
Divisions: Div C > Applied Mechanics
Depositing User: Cron Job
Date Deposited: 31 Jul 2020 23:19
Last Modified: 04 Mar 2021 04:16
DOI: 10.1038/s41524-020-00367-7