Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials

TL;DR

This paper develops an on-the-fly machine-learned interatomic potential for zirconia, accurately capturing phase transitions and thermal transport properties, demonstrating the potential of such methods for simulating anharmonic materials.

Contribution

It introduces a novel on-the-fly learning approach to generate interatomic potentials that accurately model phase transitions and thermal conductivity in zirconia.

Findings

Successfully predicts temperature-induced phase transitions in zirconia.

Accurately calculates thermal conductivity using Green-Kubo theory.

Demonstrates the effectiveness of on-the-fly machine learning for anharmonic materials.

Abstract

Machine-learned interatomic potentials enable realistic finite temperature calculations of complex materials properties with first-principles accuracy. It is not yet clear, however, how accurately they describe anharmonic properties, which are crucial for predicting the lattice thermal conductivity and phase transitions in solids and, thus, shape their technological applications. Here we employ a recently developed on-the-fly learning technique based on molecular dynamics and Bayesian inference in order to generate an interatomic potential capable to describe the thermodynamic properties of zirconia, an important transition metal oxide. This machine-learned potential accurately captures the temperature-induced phase transitions below the melting point. We further showcase the predictive power of the potential by calculating the heat transport on the basis of Green-Kubo theory, which allows to account for anharmonic effects to all orders. This study indicates that machine-learned potentials trained on the fly offer a routine solution for accurate and efficient simulations of the thermodynamic properties of a vast class of anharmonic materials.