改进机器学习力场用于分子动力学模拟,使用细粒度力度指标
Zun Wang1, Hongfei Wu1,2, Lixin Sun3
1Microsoft Research AI4Science, Beijing 100084, China.
The Journal of chemical physics
|July 17, 2023
概括
机器学习力场 (MLFF) 提供成本效益高的分子动力学 (MD) 模拟,但难以实现稳定性. 新的力量指标系统地测量MLFF,提高模拟稳定性和性能.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 生物物理学的生物物理.
背景情况:
- 机器学习力场 (MLFF) 越来越多地被用作分子动力学 (MD) 模拟的初始方法的计算效率高的替代方案.
- 尽管有优势,但MLFF通常在一般化和稳定性方面存在局限性,导致长期MD模拟过程中的不稳定性.
研究的目的:
- 开发和验证新的指标,以系统地评估MLFF在各种分子系统和构造中的准确性和可靠性.
- 调查拟议的力度指标与MLFF产生的MD模拟的稳定性之间的相关性.
- 为改善MLFF性能和MD模拟可靠性提供一个框架.
主要方法:
- 引入了全球和细粒度力指标,考虑元素和构造方面的因素,以在原子和构造层面评估MLFF.
- 对阿司匹林,Ac-Ala3-NHMe和Chignolin MD数据集 (21166个原子) 的三个最先进的MLFF (ET,NequIP,ViSNet) 进行了评估.
- 使用各种初始形状的训练有素的MLFF进行MD模拟,分析力度,轨迹稳定性和模拟失败.
主要成果:
- 建立了拟议的力度指标与MD模拟轨迹的稳定性之间的关系.
- 确定了导致MLFF驱动的MD模拟崩的关键因素.
- 证明通过将这些力度指标纳入训练过程,可以提高MLFF性能和MD模拟稳定性.
结论:
- 开发的部队指标为系统评估和改进MLFF提供了强大的工具.
- 将这些指标集成到MLFF培训中,通过损失函数,重权或持续培训等方法,可以显著提高模拟的准确性和稳定性.
- 这项工作为更可靠,更准确的MLFF引导分子模拟提供了途径.
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