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基于机器学习的钻石状碳的机械行为的一项粗的研究

Zhipeng Xiong¹, Yifeng Yu², Huan Chen³

¹Key Laboratory of Traffic Safety on Track (Central South University), Ministry of Education, School of Traffic & Transportation Engineering, Central South University, Changsha, 410075, People's Republic of China.

|July 6, 2023

概括

一种新的粗粒度分子动力学 (CGMD) 方法,通过机器学习 (ML) 增强,在更大的尺度上准确模拟类似钻石的碳 (DLC) 薄膜. 这种方法显著降低了计算成本,有助于开发先进的DLC材料.

科学领域:

材料科学材料科学材料科学
计算材料科学科学计算材料科学
纳米技术纳米技术

背景情况:

类似钻石的碳 (DLC) 薄膜具有理想的性能,如高硬度和耐磨性.
目前的模拟方法很难捕捉微米级DLC电影的变形和故障机制.
了解这些机制对于优化DLC电影性能至关重要.

研究的目的:

开发一种可扩展的模拟方法来分析DLC电影的机械行为.
在比传统的分子动力学更大的规模上研究DLC电影的单轴拉伸行为.
为了提高粗粒度分子动力学 (CGMD) 模拟的参数化效率.

主要方法:

经过CGMD模拟的高通量选,开发了一种修改后的Tersoff潜力.
使用机器学习 (ML) 模型,将高通量计算成本加快了86%.
使用ML增强的CGMD方法来模拟DLC片的单轴拉伸行为.

主要成果:

基于ML的CGMD方法成功地在更高的尺度上模拟了DLC电影.
由CGMD生成的拉伸曲线与来自全原子模拟的曲线非常相匹配.
通过ML驱动的参数优化实现了显著的计算资源节约.

结论:

通过ML增强的CGMD方法提供了一种计算效率高,准确的方法,可以在更大的尺度上研究DLC电影.
这种方法有助于研究DLC电影中的变形和故障机制.
这些发现支持研究和生产高性能DLC电影的进步.

关键词:

粗粒度分子动力学的粗粒度分子动力学像钻石一样的碳.高通量分析的分析方法机器学习是机器学习.

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