Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide
Year: 2020
Type: article
Abstract: 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 (HfO 2 ) 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 HfO 2 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.
Source: npj Computational Materials
Authors: Ganesh Sivaraman, Anand Narayanan Krishnamoorthy, Matthias Baur, Christian Holm, Marius Stan, Gábor Csányi, Chris Benmore, Álvaro Vázquez-Mayagoitia
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,680
Awards: G102163711, G107546177, G1286251239, G1313983767, G1486546336, G1505794299, G1664757510, G2101401529, G2287611682, G262962599, G2784524989, G2887060908, G2888069876, G3075337988, G340565428, G3479632140, G4030212589, G426311843, G498139845, G5106849484, G5387317844, G5910536577, G6848031779, G6918803902, G7249683039, G7368700025, G7583137537, G7822115964, G788322952, EXC 2075: Datenintegrierte Simulationswissenschaft (SimTech), G969889393, G993149817
OpenAlex ID: W2981603605