论文标题
开发用于粗粒分子模拟的机器学习电位：挑战和陷阱
Developing Machine-Learned Potentials for Coarse-Grained Molecular Simulations: Challenges and Pitfalls
论文作者
论文摘要
与原子分辨率上可实现的相比，粗砂（CG）可以研究较大系统和更长的时间尺度的分子特性。最近已经提出了机器学习技术来学习CG粒子相互作用，即开发CG力场。分子的图表和图形卷积神经网络结构的监督训练用于通过力匹配方案来学习平均力的潜力。在这项工作中，作用在每个CG粒子上的力与以Schnet的名义相关的其本地环境的表示，该代表是通过连续过滤器卷积构建的。我们探索了Schnet模型在获得液体苯的CG潜力的应用，研究模型结构和超参数对模拟CG系统的热力学，动力学和结构特性的影响，并报告并讨论所设想的挑战以及未来的方向。
Coarse graining (CG) enables the investigation of molecular properties for larger systems and at longer timescales than the ones attainable at the atomistic resolution. Machine learning techniques have been recently proposed to learn CG particle interactions, i.e. develop CG force fields. Graph representations of molecules and supervised training of a graph convolutional neural network architecture are used to learn the potential of mean force through a force matching scheme. In this work, the force acting on each CG particle is correlated to a learned representation of its local environment that goes under the name of SchNet, constructed via continuous filter convolutions. We explore the application of SchNet models to obtain a CG potential for liquid benzene, investigating the effect of model architecture and hyperparameters on the thermodynamic, dynamical, and structural properties of the simulated CG systems, reporting and discussing challenges encountered and future directions envisioned.
