DeePCG：通过深度神经网络构建粗粒度模型
DeePCG: constructing coarse-grained models via deep neural networks
摘要：
我们引入了一个一般框架，用于构建粗粒度的潜在模型，而无需特别的近似，如限制潜在的两个和/或三体的贡献。该方案被称为Deep Coarse-Grained Potential（简称DeePCG），利用精心制作的神经网络来构建多体粗粒度势。该网络使用完整的原子数据进行训练，以保持系统的自然对称性。由此产生的模型是非常准确的，可以用来采样的粗粒度变量的配置在一个更快的方式比原来的原子模型。作为一个应用程序，我们认为液态水和使用的氧坐标作为粗粒度的变量，从从头算分子动力学水平上的一个完整的原子模拟这个系统。我们发现，两体，三体和更高阶的氧关联函数产生的粗粒度和全原子模型非常吻合彼此，说明DeePCG模型的有效性，一个相当具有挑战性的任务。
英文摘要：
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 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 found 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.
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