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.

中文翻译：

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将原子模拟的极限推向超高温：ZrB2 的机器学习力场

在广泛的温度范围内确定热和物理量，尤其是在超高温区域内，是一项艰巨的理论和实验挑战。同时对于了解超高温陶瓷 (UHTC) 材料的性能至关重要。在这里，我们介绍了 ZrB2 的机器学习力场的开发，ZrB2 是具有复杂键结构的 UHTC 家族的主要成员之一。力场表现出能量和力的化学准确性，并且可以再现结构、弹性和声子特性，包括热膨胀和热传输。与可用经验潜力的彻底比较表明，我们的力场优于竞争对手。最重要的是，它的有效性从室温扩展到超高温区域（高达 ~ 2,500 K），在那里测量非常困难、成本高昂且有时无法进行。我们的工作表明，机器学习力场可用于在没有实验工具的恶劣环境中模拟材料，但对许多工程应用至关重要，例如航天、航空和核能。