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机器学习分子动力学揭示了化中离子束损伤的原子化机制

Zixuan Chen¹, Jixiang Ding¹, Shayu Song¹

¹State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China. zhangjg@hust.edu.cn.

Physical chemistry chemical physics : PCCP

|July 2, 2025

概括

研究人员开发了一种机器学习潜力,以了解高能离子束如何影响化 (CaF) 晶体. 这揭示了优化光学材料离子束处理的关键机制.

科学领域:

材料科学材料科学材料科学
计算物理计算物理
表面科学是一门学科.

背景情况:

化 (CaF) 对于光学元件至关重要,因为它具有紫外线传导性和光学异构性.
在离子束处理过程中了解原子尺度的稳定性对于高精度的CaF制造至关重要.
目前对CaF₂表面和离子轰炸下的界面行为知识有限.

研究的目的:

开发一种机器学习潜力,以模拟与CaF₂的高能离子束相互作用.
在离子辐射下阐明控制微观结构和CaF₂属性变化的物理机制.
为优化对CaF₂的离子束处理技术提供理论指导.

主要方法:

使用DeePMD方法开发机器学习潜力 (DP).
在化晶体上模拟高能离子束辐射.
对辐射损伤,微观结构演变和粒子动态的分析.

主要成果:

开发的DP模型在复杂条件下预测CaF₂属性的高准确性和稳定性.
模拟显示了高能和低能离子束的不同的处理机制.
该研究确定了辐射损伤,微观结构进化和粒子行为模式.

结论:

机器学习潜力使高能离子束照射系统的稳定运行成为可能.
这项研究阐明了对CaF₂的高能离子束处理的微观机制.
这些发现为改进离子束处理和材料表面修饰技术提供了理论指导.

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