Coarse-Grained Molecular Dynamics with Normalizing Flows,Journal of Chemical Theory and Computation

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Coarse-Grained Molecular Dynamics with Normalizing Flows
Journal of Chemical Theory and Computation ( IF 5.7 ) Pub Date : 2024-09-02 , DOI: 10.1021/acs.jctc.4c00700
Samuel Tamagnone ₁ , Alessandro Laio _{1,

2} , Marylou Gabrié ₃

Affiliation

We propose a sampling algorithm relying on a collective variable (CV) of midsize dimension modeled by a normalizing flow and using nonequilibrium dynamics to propose full configurational moves from the proposition of a refreshed value of the CV made by the flow. The algorithm takes the form of a Markov chain with nonlocal updates, allowing jumps through energy barriers across metastable states. The flow is trained throughout the algorithm to reproduce the free energy landscape of the CV. The output of the algorithm is a sample of thermalized configurations and the trained network that can be used to efficiently produce more configurations. We show the functioning of the algorithm first in a test case with a mixture of Gaussians. Then, we successfully tested it on a higher-dimensional system consisting of a polymer in solution with a compact state and an extended stable state separated by a high free energy barrier.

中文翻译：

具有归一化流动的粗粒度分子动力学

我们提出了一种采样算法，该算法依赖于由归一化流建模的中等尺寸的集体变量（CV），并使用非平衡动力学从流所产生的 CV 刷新值的命题中提出完整的配置移动。该算法采用具有非局部更新的马尔可夫链的形式，允许跨越亚稳态的能量障碍。在整个算法中对流程进行训练，以重现 CV 的自由能景观。该算法的输出是热化配置的样本和经过训练的网络，可用于有效地生成更多配置。我们首先在混合高斯的测试用例中展示算法的功能。然后，我们成功地在一个高维系统上进行了测试，该系统由溶液中的聚合物组成，具有致密态和由高自由能势垒分隔的扩展稳定态。

更新日期：2024-09-02

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