论文标题
多组分系统的机器学习潜力：TI-AL二进制系统
Machine learning potentials for multicomponent systems: The Ti-Al binary system
论文作者
论文摘要
机器学习潜力（MLP）正在成为执行准确的原子模拟和晶体结构优化的强大工具。一种开发MLP的方法采用了一组系统的多项式不变性，包括高阶的方法来表示相邻的原子密度。在这项研究中，多项式不变剂的公式扩展到了多组分系统的情况。扩展配方比元素系统的配方更复杂。这项研究还显示了其在Ti-Al二元系统中的应用。结果，误差最低的MLP和具有较高计算成本性能的MLP是从系统上开发的许多MLP中选择的。研究了开发的MLP对许多特性的预测能力，例如形成能，弹性常数，热力学特性和机械性能。 MLP在各种有序结构中具有高预测能力。本方案应系统地适用于其他多组分系统。
Machine learning potentials (MLPs) are becoming powerful tools for performing accurate atomistic simulations and crystal structure optimizations. An approach to developing MLPs employs a systematic set of polynomial invariants including high-order ones to represent the neighboring atomic density. In this study, a formulation of the polynomial invariants is extended to the case of multicomponent systems. The extended formulation is more complex than the formulation for elemental systems. This study also shows its application to Ti-Al binary system. As a result, an MLP with the lowest error and MLPs with high computational cost performance are selected from the many MLPs developed systematically. The predictive powers of the developed MLPs for many properties, such as the formation energy, elastic constants, thermodynamic properties, and mechanical properties, are examined. The MLPs exhibit high predictive power for the properties in a wide variety of ordered structures. The present scheme should be systematically applicable to other multicomponent systems.
