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arXiv 2211.03350cond-mat.mtrl-sci
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用神经网络势高效准确地模拟多组分金属液体的玻璃化过程

Efficient and accurate simulation of vitrification in multi-component metallic liquids with neural-network potentials

Rui Su, Jieyi Yu, Pengfei Guan, Weihua Wang

摘要:

构造精确的原子间相互作用势和克服结构平衡时间的指数增长是对深过冷多组分金属液体玻璃化过程中组分相关结构和动力学的原子学研究的挑战。在这项工作中,我们描述了一个国家的最先进的战略,以同时应对这些挑战。以典型的Zr-Cu-Al体系为例,结合高效、准确地生成多元金属玻璃神经网络势(NNP)的通用算法,提出了一种高效的原子交换混合蒙特卡罗(SHMC)算法,用于加速深度过冷液体的热力学平衡.大量的计算表明,新开发的NNP忠实地再现了从从头计算和实验获得的相稳定性和结构特征。在组合NNP-SHMC算法中,在深度过冷温度下的结构平衡时间被加速至少五个数量级,并且淬火的玻璃样品表现出与实验室中制备的样品相当的稳定性。我们的研究结果为下一代研究玻璃化过程铺平了道路,从而为多组分金属玻璃的成分依赖的玻璃形成能力和物理性能铺平了道路。

英文摘要:

Constructing accurate interatomic potential and overcoming the exponential growth of structural equilibration time are challenges to the atomistic investigations of the composition-dependent structure and dynamics during the vitrification process of deeply supercooled multi-component metallic liquids. In this work, we describe a state-of-the-art strategy to address these challenges simultaneously. In the case of the representative Zr-Cu-Al system, in combination with a general algorithm for generating the neural-network potentials (NNP) of multi-component metallic glasses effectively and accurately, we propose a highly efficient atom-swapping hybrid Monte Carlo (SHMC) algorithm for accelerating the thermodynamic equilibration of deeply supercooled liquids. Extensive calculations demonstrate that the newly developed NNP faithfully reproduces the phase stabilities and structural characteristics obtained from the ab initio calculations and experiments. In the combined NNP-SHMC algorithm, the structure equilibration time in the deeply supercooled temperatures is accelerated by at least five orders of magnitudes, and the quenched glassy samples exhibit comparable stability to those prepared in the laboratory. Our results pave the way for the next-generation studies of the vitrification process and, thereby the composition-dependent glass-forming ability and physical properties of multi-component metallic glasses.

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