深度学习路径类集体变量用于增强采样分子动态
Thorben Fröhlking1,2,3, Luigi Bonati4, Valerio Rizzi1,2,3
1School of Pharmaceutical Sciences, University of Geneva, Rue Michel Servet 1, 1206 Genève, Switzerland.
The Journal of chemical physics
|May 15, 2024
概括
我们介绍了DeepLNE,这是一种新的路径类集体变量,用于增强采样. 这种方法有效地模仿反应坐标,并加速分子动力学模拟用于自由能量计算.
科学领域:
- 计算化学计算化学
- 生物物理学的生物物理.
- 机器学习 机器学习
背景情况:
- 改进的采样技术对于探索复杂的自由能源景观至关重要.
- 现有的反应路径集体变量在定义复杂路径方面存在局限性.
研究的目的:
- 引入一种新的路径类型集体变量,DeepLNE (深部局部非线性嵌入).
- 解决目前探索反应途径的方法的局限性.
主要方法:
- DeepLNE受到局部线性嵌入的启发,并接受了反应轨迹的训练.
- 使用可分化通用自编码器,结合神经网络和k-最近邻居选择.
- 自动选择邻居搜索指标,并学习路径,没有手动地标选择.
主要成果:
- 在玩具模型中,DeepLNE非常接近理想的反应坐标 (米勒-布朗潜力,氨二).
- 加快过渡,并估计在RNA四旋翼分子动力学模拟的折叠自由能量.
结论:
- DeepLNE提供了一种有效和自动化的方法来定义反应坐标.
- 在加速分子动力学模拟和对复杂系统的自由能量估计方面表现出实用性.
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