Pushing the limits of atomistic simulations towards ultra-high temperature: a machine-learning force field for ZrB2

TL;DR

This paper introduces a machine-learning force field for ZrB2 that accurately models its properties across a broad temperature range, including ultra-high temperatures up to 2,500 K, enabling simulations in extreme environments.

Contribution

The study develops and validates a machine-learning force field for ZrB2 that outperforms existing potentials, extending reliable simulations to ultra-high temperatures.

Findings

Accurately reproduces structural, elastic, and phonon properties.

Outperforms existing empirical potentials.

Effective up to ~2,500 K, enabling high-temperature simulations.

Abstract

Determining thermal and physical quantities across a broad temperature domain, especially up to the ultra-high temperature region, is a formidable theoretical and experimental challenge. At the same time it is essential for understanding the performance of ultra-high temperature ceramic (UHTC) materials. Here we present the development of a machine-learning force field for ZrB2, one of the primary members of the UHTC family with a complex bonding structure. The force field exhibits chemistry accuracy for both energies and forces and can reproduce structural, elastic and phonon properties, including thermal expansion and thermal transport. A thorough comparison with available empirical potentials shows that our force field outperforms the competitors. Most importantly, its effectiveness is extended from room temperature to the ultra-high temperature region (up to ~ 2,500 K), where measurements are very difficult, costly and some time impossible. Our work demonstrates that machine-learning force fields can be used for simulations of materials in a harsh environment, where no experimental tools are available, but crucial for a number of engineering applications, such as in aerospace, aviation and nuclear.