High-temperature thermal stability, elastic moduli and anisotropy are among the key properties, which are used in selecting materials for cutting and machining applications. The high computational demand of ab initio molecular dynamics (AIMD) simulations in calculating elastic constants of alloys promotes the development of alternative approaches. Machine learning concept grasped as hybride classical molecular dynamics and static first principles calculations have several orders less computational costs. Here we prove the applicability of the concept considering the recently developed moment tensor potentials (MTP), where moment tensors are used as material’s descriptors which can be trained to predict the elastic constants of the prototypical hard coating alloy, Ti${}_{0.5}$Al${}_{0.5}$N at 900 K. We demonstrate excellent agreement between classical molecular dynamics simulations with MTPs and AIMD simulations. Moreover, we show that using MTPs one overcomes the inaccuracy issues present in approximate AIMD simulations of elastic constants of alloys.

高温热稳定性、弹性模量和各向异性是关键特性，用于选择用于切削和加工应用的材料。从头算分子动力学 (AIMD) 模拟计算合金弹性常数的高计算需求促进了替代方法的发展。机器学习概念被理解为混合经典分子动力学和静态第一原理计算，计算成本低几个数量级。在这里，我们考虑了最近开发的力矩张量势 (MTP)，证明了该概念的适用性，其中力矩张量用作材料的描述符，可以通过训练来预测原型硬涂层合金 Ti 的弹性常数${}_{0.5}$铝${}_{0.5}$N 为 900 K。我们证明了经典分子动力学模拟与 MTP 和 AIMD 模拟之间的极好一致性。此外，我们表明使用 MTP 克服了合金弹性常数近似 AIMD 模拟中存在的不准确问题。