Thermodynamic properties by on-the-fly machine-learned interatomic potentials: thermal transport and phase transitions of zirconia

Machine-learned interatomic potentials enable realistic finite temperature calculations of complex materials properties with first-principles accuracy. In particular, detailed predictions of the lattice thermal conductivity and phase transitions in solids are crucial for many technological applications. Here we employ a recently developed on-the-fly learning technique based on molecular dynamics and Bayesian regression [1] in order to generate an interatomic potential capable to describe the thermodynamic properties of the prototypical transition metal oxide ZrO₂. We showcase the predictive power of the machine learning potential by calculating the heat transport using the Green-Kubo method, which allows to account for anharmonic effects to all orders. The entropy-driven phase transitions below the melting point are also accurately described. This study demonstrates that machine-learned interatomic potentials offer a routine solution for accurate and efficient simulations of the thermodynamic properties of solid-state systems.

[1] R. Jinnouchi, F. Karsai, and G. Kresse, Phys. Rev. B 100, 014105 (2019).