Machine Learning of Coarse-Grained Molecular Dynamics Force Fields
Machine Learning of Coarse-Grained Molecular Dynamics Force Fields
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DOI：
10.1021/acscentsci.8b00913
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发表时间：
2019-05-22
影响因子：
18.2
通讯作者：
Clementi, Cecilia
中科院分区：
文献类型：
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作者：
Wang, Jiang;Olsson, Simon;Clementi, Cecilia
原子或从头算分子动力学模拟被广泛用于预测热力学和动力学，并将它们与分子结构联系起来。要超越这种计算代价高昂的模拟所能达到的时间和长度尺度，一种常见的方法是定义粗粒度分子模型。现有的粗粒度方法定义了一种有效的相互作用潜力，以匹配高分辨率模型或实验数据的定义属性。在本文中，我们将粗粒度问题重新表述为一个监督机器学习问题。我们利用统计学习理论对粗粒度误差进行分解，并通过交叉验证选择和比较不同模型的性能。我们引入了一种深度学习方法CGnets，它可以学习粗粒度的自由能函数，并且可以通过力匹配方案进行训练。CGnets保持所有物理相关的不变性，并允许人们结合先前的物理知识来避免非物理结构的采样。我们发现，CGnets可以用仅使用少量粗粒珠和无溶剂的模型捕获全原子显式溶剂自由能表面，而经典的粗粒方法无法捕获自由能表面的关键特征。因此，CGnets能够捕获从降维中出现的多体项。
Atomistic or ab initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible with such computationally expensive simulations is the definition of coarse-grained molecular models. Existing coarse-graining approaches define an effective interaction potential to match defined properties of high-resolution models or experimental data. In this paper, we reformulate coarse-graining as a supervised machine learning problem. We use statistical learning theory to decompose the coarse-graining error and cross-validation to select and compare the performance of different models. We introduce CGnets, a deep learning approach, that learns coarse-grained free energy functions and can be trained by a force-matching scheme. CGnets maintain all physically relevant invariances and allow one to incorporate prior physics knowledge to avoid sampling of unphysical structures. We show that CGnets can capture all-atom explicit-solvent free energy surfaces with models using only a few coarse-grained beads and no solvent, while classical coarse-graining methods fail to capture crucial features of the free energy surface. Thus, CGnets are able to capture multibody terms that emerge from the dimensionality reduction.