Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA.
Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, People's Republic of China and CAEP Software Center for High Performance Numerical Simulation, Huayuan Road 6, Beijing 100088, People's Republic of China.
J Chem Phys. 2018 Jul 21;149(3):034101. doi: 10.1063/1.5027645.
We introduce a general framework for constructing coarse-grained potential models without ad hoc approximations such as limiting the potential to two- and/or three-body contributions. The scheme, called the Deep Coarse-Grained Potential (abbreviated DeePCG), exploits a carefully crafted neural network to construct a many-body coarse-grained potential. The network is trained with full atomistic data in a way that preserves the natural symmetries of the system. The resulting model is very accurate and can be used to sample the configurations of the coarse-grained variables in a much faster way than with the original atomistic model. As an application, we consider liquid water and use the oxygen coordinates as the coarse-grained variables, starting from a full atomistic simulation of this system at the ab initio molecular dynamics level. We find that the two-body, three-body, and higher-order oxygen correlation functions produced by the coarse-grained and full atomistic models agree very well with each other, illustrating the effectiveness of the DeePCG model on a rather challenging task.
我们引入了一种通用框架，用于构建无需特殊近似（例如将势能限制为二体和/或三体贡献）的粗粒势能模型。该方案称为深度粗粒势能（简称 DeePCG），利用精心设计的神经网络构建多体粗粒势能。该网络使用全原子数据进行训练，以保留系统的自然对称性。得到的模型非常准确，可以比原始原子模型更快地采样粗粒变量的配置。作为应用，我们考虑液态水，并使用氧坐标作为粗粒变量，从该系统的从头算分子动力学水平的全原子模拟开始。我们发现，由粗粒和全原子模型产生的二体、三体和更高阶氧相关函数彼此非常吻合，说明了 DeePCG 模型在相当具有挑战性的任务上的有效性。
