基于DFT的机器学习潜力研究的单层MoS2中硫空缺的结构和动态
Adam Hložný1,2, Ján Brndiar1, Michele Casula3
1Institute of Informatics, Slovak Academy of Sciences, 845 07 Bratislava, Slovakia.
The Journal of chemical physics
|December 5, 2025
概括
我们开发了一种新的机器学习潜力 (MLP) 培训策略,用于在二维材料中准确预测,在标准和具有挑战性的缺陷动态域外场景中表现出色.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 机器学习 机器学习
背景情况:
- 精确建模激活过程,如2D材料中的缺陷迁移,至关重要.
- 现有的机器学习潜力 (MLP) 经常与域外推断 (OOD) 斗争,限制了它们的可靠性.
研究的目的:
- 制定一个强大的多步骤策略,培养稳定和精确的MLP.
- 为了确保MLPs在域内插值和OD外推两种方案中表现良好.
- 在二维材料中应用和验证这项关于空缺动态的战略.
主要方法:
- 设计了一种采样技术,使用推推弹性带和受约束的分子动力学来创建数据集.
- 开发定制的指标,专注于缺陷迁移的关键原子.
- 在单层MoS2中对石灰质空缺动力学进行了MACE MLP模型的基准测试,评估了OOD性能.
主要成果:
- 一个经过适当训练的MACE MLP可靠地复制能量和力量,即使在极端的OOD配置中.
- 在MoS2.2中计算的放松,最小能量路径和空缺过渡的自由能量障碍.
- 证明了针对缺陷过程开发的培训策略的实用性.
结论:
- 开发的多步骤策略可以为激活过程培训稳定和精确的MLP.
- 该方法在2D材料的缺陷动态方面表现出色,包括OOD泛化.
- 这些发现适用于其他等价信息传递神经网络潜力,并建议量子蒙特卡洛精度的途径.
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