发表机构
National Key Laboratory of Shock Wave and Detonation Physics, Institute of Fluid Physics, China Academy of Engineering Physics（冲击波与爆轰物理国家重点实验室，流体物理研究所，中国工程物理研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MyTm是一个自动化计算熔化温度的工具包，通过模块化分子动力学模拟和机器学习原子分类，实现高通量熔点计算，提高精度与效率。
AI 中文摘要
熔化温度计算是计算材料科学中的重要课题之一。在高通量计算机筛选和人工智能辅助材料设计中，通常需要对目标的熔化温度进行快速且自主的评估。然而，分子动力学（MD）模拟熔点需要许多繁琐的手动操作，使得大规模计算熔点具有挑战性。在这项工作中，我们介绍了MyTm，一个利用MD自动确定熔点的工具包。该方法完全模块化，通过组合这些模块，程序能够使用常用的方法实现全自动熔化计算，包括直接加热法、空洞法、改进空洞法、固液共存法和Z方法。此外，提出并采用了一种机器学习（ML）方法来识别和分类类固体和类液体原子，有效解决了传统分类方法精度低的问题，从而使自动化的高通量熔点计算流程成为可能。MyTm的稳健性和有效性已通过几个研究充分的系统得到证明。
英文摘要
Melting temperature calculation is one of the important topics in computational materials science. In high-throughput in silico screening and artificial intelligence assisted design of materials, it usually requires a rapid and autonomous assessment of the melting temperature of the target. Unfortunately, molecular dynamics (MD) simulations of the melting point require many cumbersome and manual operations, making large-scale calculation of the melting point challenging. In this work, we introduce MyTm, a toolkit that employs MD to automatically determine the melting point. The method is fully modularized, and by combining these modules, the program enables fully automated melting calculations by using commonly adopted approaches, including the direct-heating method, the void method, the modified void method, the solid-liquid coexistence method, and the Z method. Moreover, a machine learning (ML) method is proposed and employed to recognize and classify the solid like and liquid like atoms, which effectively resolve the low accuracy issue in conventional classification approaches, thus making the automated high throughput pipeline of melting-point calculation possible. The robustness and efficacy of MyTm have been demonstrated by several well studied systems.
Comments32 pages, 7 figures
Journal refComputer Physics Communications 330, 110401 (2027)
