An adaptive modeling method (AMM) that couples a deep neural network potential and a classical force field is introduced to address the accuracy-efficiency dilemma faced by the molecular simulation community. The AMM simulated system is decomposed into three types of regions. The first type captures the important phenomena in the system and requires high accuracy, for which we use the Deep Potential Molecular Dynamics (DeePMD) model in this work. The DeePMD model is trained to accurately reproduce the statistical properties of the ab initio molecular dynamics. The second type does not require high accuracy, and a classical force field is used to describe it in an efficient way. The third type is used for a smooth transition between the first and the second types of regions. By using a force interpolation scheme and imposing a thermodynamics force in the transition region, we make the DeePMD region embedded in the AMM simulated system as if it were embedded in a system that is fully described by the accurate potential. A representative example of the liquid water system is used to show the feasibility and promise of this method.

中文翻译：

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深层神经网络电位与经典力场的自适应耦合

引入了一种将深层神经网络潜力与经典力场相结合的自适应建模方法（AMM），以解决分子模拟界所面临的精度-效率难题。AMM模拟系统被分解为三种类型的区域。第一种类型捕获了系统中的重要现象，并要求很高的精度，为此，我们在这项工作中使用了深潜分子动力学（DeePMD）模型。DeePMD模型经过训练可以准确地重现从头算起的统计属性。分子动力学。第二种类型不需要很高的精度，并且经典的力场用于有效地描述它。第三类用于在第一类和第二类区域之间进行平滑过渡。通过使用力插值方案并在过渡区域中施加热力学力，我们将DeePMD区域嵌入到AMM仿真系统中，就好像它嵌入了一个由准确电势充分描述的系统中一样。以液态水系统为代表的例子说明了该方法的可行性和前景。