通过神经网络力场探索H-BN和石墨烯的断裂
1Applied Mechanics Laboratory and Department of Engineering Mechanics, Tsinghua University, Beijing 100084, People's Republic of China.
概括
一个新的神经网络力场,NN-F3,通过考虑原子级键变化和应变,准确地建模材料断裂. 这一进步改善了对复杂材料的预测,例如六角化和双层扭曲石墨烯.
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
- 材料机械学 材料机械学
背景情况:
- 材料断裂是一种由极端机械过程 (如键断裂) 驱动的关键故障机制.
- 由于从原子到结构层面的多层次现象,理解裂核和生长是复杂的.
- 现有的实证和机器学习力场难以准确预测断裂行为,特别是非线性压力-应变关系和键性能量.
研究的目的:
- 开发一种高保真力场,能够准确模拟断裂过程.
- 在原子模拟中纳入应变和键断裂/转换能量的张量性质.
- 研究六角化 (h-BN) 和双层扭曲石墨烯的断裂机制.
主要方法:
- 使用深潜力-平滑版 (DeepPot-SE) 框架开发了一个基于神经网络的断裂力场 (NN-F3).
- 使用密度函数理论 (DFT) 计算来生成训练数据.
- 采用应变状态的预抽样和积极学习来探索断裂期间的关键过渡状态.
主要成果:
- NN-F3准确地捕捉了非线性,异型的应力-应变行为和断裂的能量.
- 模拟显示了h-BN中断裂的粗物理,与实验观测相一致.
- 预测了由于扭曲双层石墨烯的跨层裂纹相互作用导致的硬化效应.
结论:
- 开发的NN-F3力场在模拟原子级别的材料裂变方面取得了重大进展.
- 这种方法为破裂机制和材料故障提供了关键的见解.
- 这些发现对设计更坚固的材料和预测其故障模式有影响.
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