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增强采样 (PINES) 的 permutationally Invariant 网络:发现多分子和含溶剂的集体变量

Nicholas S M Herringer¹, Siva Dasetty², Diya Gandhi²

¹Department of Chemistry, University of Chicago, Chicago, Illinois 60637, United States.

Journal of chemical theory and computation

|December 27, 2023

概括

这项研究引入了分子模拟的新方法,使复杂的能源景观能够有效地探索. 该方法克服了发现关键分子描述物的挑战,这对于理解化学过程至关重要.

科学领域:

计算化学和分子动力学
在物理科学中的机器学习

背景情况:

分子自由能量景观通常具有高的障碍,阻碍了高效的相位空间采样.
改进的采样技术加快了使用集体变量 (CV) 的勘探速度.
发现旋转和顺序不变的CV仍然是一个重大挑战.

研究的目的:

开发一种数据驱动的方法来发现对分子对称性不变的非线性集体变量 (CV).
将这些不变的CV与增强的抽样进行整合,以改善相位空间探索.
为复杂的分子系统代地改进CV和自由能量景观.

主要方法:

与自编码神经网络集成的顺序不变向量 (PIV) 特征化.
增强采样 (PINES) 方法的变量不变网络的开发.
交叉轮的CV发现和增强采样,以实现代阶段空间扩张.

主要成果:

成功学习了对翻译,旋转和排列不变的非线性CV.
在各种系统上展示了PINES方法:子集群自组装,水中的NaCl离子对和聚合物疏水崩.
通过代采样和发现,通过代采样和发现实现了融合的CV和自由能源景观.

结论:

在分子模拟中,PINES方法有效地解决了在分子模拟中发现 permutationally invariant CV 的挑战.
这种方法提高了增强的采样技术的效率和准确性.
现在可以在PLUMED2库中使用PINES模块,以促进更广泛的应用.

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