Shinkle Emily, Pachalieva Aleksandra, Bahl Riti, Matin Sakib, Gifford Brendan, Craven Galen T, Lubbers Nicholas
Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.
Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.
J Chem Theory Comput. 2024 Dec 10;20(23):10524-10539. doi: 10.1021/acs.jctc.4c00788. Epub 2024 Nov 23.
Coarse-graining is a molecular modeling technique in which an atomistic system is represented in a simplified fashion that retains the most significant system features that contribute to a target output while removing the degrees of freedom that are less relevant. This reduction in model complexity allows coarse-grained molecular simulations to reach increased spatial and temporal scales compared with corresponding all-atom models. A core challenge in coarse-graining is to construct a force field that represents the interactions in the new representation in a way that preserves the atomistic-level properties. Many approaches to building coarse-grained force fields have limited transferability between different thermodynamic conditions as a result of averaging over internal fluctuations at a specific thermodynamic state point. Here, we use a graph-convolutional neural network architecture, the Hierarchically Interacting Particle Neural Network with Tensor Sensitivity (HIP-NN-TS), to develop a highly automated training pipeline for coarse-grained force fields, which allows for studying the transferability of coarse-grained models based on the force-matching approach. We show that this approach yields not only highly accurate force fields but also that these force fields are more transferable through a variety of thermodynamic conditions. These results illustrate the potential of machine learning techniques, such as graph neural networks, to improve the construction of transferable coarse-grained force fields.
粗粒化是一种分子建模技术，其中原子系统以简化的方式表示，保留了有助于目标输出的最重要系统特征，同时去除了不太相关的自由度。与相应的全原子模型相比，模型复杂性的这种降低使得粗粒化分子模拟能够达到更大的空间和时间尺度。粗粒化中的一个核心挑战是构建一个力场，该力场以保留原子级属性的方式表示新表示中的相互作用。由于在特定热力学状态点对内部涨落进行平均，构建粗粒化力场的许多方法在不同热力学条件之间的可转移性有限。在这里，我们使用一种图卷积神经网络架构，即具有张量敏感性的分层相互作用粒子神经网络（HIP-NN-TS），来开发一种用于粗粒化力场的高度自动化训练管道，这使得能够基于力匹配方法研究粗粒化模型的可转移性。我们表明，这种方法不仅产生高度准确的力场，而且这些力场在各种热力学条件下更具可转移性。这些结果说明了机器学习技术，如图神经网络，在改进可转移粗粒化力场构建方面的潜力。
