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arXiv · 1911.03307

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

Yanhui Zhang·Alessandro Lunghi·Stefano Sanvito
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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.

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Yanhui Zhang, Alessandro Lunghi, Stefano Sanvito. 2019-11-08. Pushing the limits of atomistic simulations towards ultra-high temperature: a machine-learning force field for ZrB2. https://arxiv.org/abs/1911.03307

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