Calculating the elastic and mechanical characteristics of non-crystalline solids can be challenging due to the high computational cost of ab initio methods and the low accuracy of empirical potentials. This paper proposes a computational technique for efficient calculations of mechanical properties of polycrystals, composites, and multi-phase systems from atomistic simulations with high accuracy and reasonable computational cost. The calculated elastic moduli of polycrystalline diamond and their dependence on grain size are determined using a developed approach based on actively learned machine learning interatomic potentials (MLIPs). These potentials are trained on local fragments of the polycrystalline system, and ab initio calculations are used to compute forces, stresses, and energies. This technique allows researchers to perform extensive calculations of the mechanical properties of complex solids with different compositions and structures, achieving high accuracy and facilitating the transition from ideal (single crystal) systems to more realistic ones.

中文翻译：

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通过机器学习了解单晶和多晶固体的机械特性

由于从头计算方法的计算成本高且经验势的精度低，计算非晶固体的弹性和机械特性可能具有挑战性。本文提出了一种计算技术，用于通过原子模拟有效计算多晶、复合材料和多相系统的机械性能，具有高精度和合理的计算成本。多晶金刚石的计算弹性模量及其对晶粒尺寸的依赖性是使用基于主动学习机器学习原子间势（MLIP）的开发方法确定的。这些势能在多晶系统的局部片段上进行训练，并使用从头计算来计算力、应力和能量。这项技术使研究人员能够对具有不同成分和结构的复杂固体的机械性能进行广泛的计算，实现高精度并促进从理想（单晶）系统到更现实的系统的转变。