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.
Highlights
The atomistic modelling of thermodynamic properties of materials at finite temperature and realistic conditions is of fundamental importance for a multitude of technological applications, from photovoltaics to optoelectronic devices, as well as for advancing our understanding of the physical properties of matter
In this work we address this gap by focusing on one paradigmatic example of anharmonic systems, zirconia (ZrO2), and we present a procedure to accurately study its thermodynamic properties using Machine-learned force fields (MLFFs) trained on the fly
In order to machine learn an interatomic potential for zirconia we used the kernel-based machine-learning model introduced in refs. 16,37
Summary
The atomistic modelling of thermodynamic properties of materials at finite temperature and realistic conditions is of fundamental importance for a multitude of technological applications, from photovoltaics to optoelectronic devices, as well as for advancing our understanding of the physical properties of matter. Two of the most challenging, yet central, properties are temperature-induced structural phase transitions in solids and the transport of heat, which in semiconductors and insulators mainly stems from the lattice vibrations. Both can be extracted directly from MD simulations[2]. In the case of heat transport, this can be obtained from equilibrium MD calculations on the basis of the Green–Kubo (GK) theory[3] This method, based on the fluctuation-dissipation theorem, is exact at sufficiently high temperatures where nuclear quantum effects are negligible and is superior to lattice dynamics approaches based on the Boltzmann transport equation (BTE) in crystalline systems[4]. The GK approach carries the advantage that it accounts exactly for anharmonicity to all orders, while intrinsically lending itself to a unified description of ordered and disordered solids, as well as liquids
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