几何图神经网络中的无描述符集合变量

Jintu Zhang, Luigi Bonati, Enrico Trizio, Odin Zhang, Yu Kang, TingJun Hou, Michele Parrinello
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引用次数: 0

摘要

增强采样模拟使罕见事件的计算研究成为可能。这类方法中的一大类关键取决于一些集体变量(CV)的定义,这些集体变量可以对过程的相关物理特性进行低维描述。最近,人们提出了许多方法,通过使用机器学习工具直接从模拟数据中学习变量,实现 CV 设计的半自动化。然而,大多数方法都基于前馈神经网络,需要输入一些用户定义的物理描述符。在此,我们建议使用图神经网络绕过这一步骤,直接使用原子坐标作为 CV 模型的输入。这样,我们就能实现全自动的 CV 确定方法,提供在相关对称性(尤其是突变对称性)下不变的变量。此外,我们还提供了不同的分析工具,以支持最终 CV 的物理解释。我们使用文献中不同的方法来优化 CV,证明了我们方法的稳健性,并在几个系统上证明了其有效性,包括一个小肽、一个显式溶剂中的离子解离以及一个简单的化学反应。
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Descriptors-free Collective Variables From Geometric Graph Neural Networks
Enhanced sampling simulations make the computational study of rare events feasible. A large family of such methods crucially depends on the definition of some collective variables (CVs) that could provide a low-dimensional representation of the relevant physics of the process. Recently, many methods have been proposed to semi-automatize the CV design by using machine learning tools to learn the variables directly from the simulation data. However, most methods are based on feed-forward neural networks and require as input some user-defined physical descriptors. Here, we propose to bypass this step using a graph neural network to directly use the atomic coordinates as input for the CV model. This way, we achieve a fully automatic approach to CV determination that provides variables invariant under the relevant symmetries, especially the permutational one. Furthermore, we provide different analysis tools to favor the physical interpretation of the final CV. We prove the robustness of our approach using different methods from the literature for the optimization of the CV, and we prove its efficacy on several systems, including a small peptide, an ion dissociation in explicit solvent, and a simple chemical reaction.
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