由神经网络预测相互作用加速的各向异性粒子分子动力学模拟
Molecular Dynamics Simulations of Anisotropic Particles Accelerated by Neural-Net Predicted Interactions
AI总结：
研究针对分子动力学中各向异性粒子复合刚体距离计算成本高的问题，训练神经网络直接预测质心距离和取向与相互作用能的映射，输出能量、力与扭矩，在保持模拟精度的同时实现最高23倍加速。
摘要：
由较小的复合珠子组成的刚体，通常用于通过分子动力学或Monte Carlo方法模拟各向异性粒子。为了准确表示粒子形状，并在两个刚体之间获得平滑且真实的有效对相互作用，每个刚体可能需要包含数百个球形珠子。给定一对相互作用的粒子，传统MD方法会计算一定距离内刚体珠子之间的体间距离。对于包含许多各向异性粒子的系统，距离计算的计算成本很高，并限制了可达到的系统规模和模拟时间。然而，两个刚性粒子之间的有效相互作用仅取决于它们的质心之间的距离及其相对取向。因此，一个将质心距离和取向直接映射到两个刚体之间相互作用能的函数，将完全绕过珠子间距离的计算。对于大多数非球形刚体，解析地推导出这样一个通用函数具有挑战性。我们训练了神经网络——一种将非线性函数拟合到复杂数据集的强大工具——来完成这项任务。该模型以对构型作为输入，直接预测两个刚性粒子之间的能量、力和扭矩。我们表明，使用从能量神经网络梯度获得的力和扭矩执行的立方体和圆柱体的MD模拟，与使用复合刚体的传统模拟在定量上相匹配。结构量和动态度量均保持一致，同时根据硬件和系统规模的不同，比传统分子动力学实现了高达23倍的加速。原则上，只要能获得足够的训练数据，该方法便可应用于具有任意对相互作用的任何不规则形状。
英文摘要：
Rigid bodies, made of smaller composite beads, are commonly used to simulate anisotropic particles with molecular dynamics or Monte Carlo methods. To accurately represent the particle shape and to obtain smooth and realistic effective pair interactions between two rigid bodies, each body may need to contain hundreds of spherical beads. Given an interacting pair of particles, traditional MD methods calculate the inter-body distances between the beads of the rigid bodies within a certain distance. For a system containing many anisotropic particles, distance calculations are computationally costly and limit the attainable system size and simulation time. However, the effective interaction between two rigid particles only depends on the distance between their center of masses and their relative orientation. Therefore, a function directly mapping the center of mass distance and orientation to the interaction energy between the two rigid bodies, would completely bypass inter-bead distance calculations. It is challenging to derive such a general function analytically for most non-spherical rigid bodies. We have trained neural nets, powerful tools to fit nonlinear functions to complex datasets, to achieve this task. The pair configuration is taken as input and the energy, forces and torques between two rigid particles are predicted directly. We show that MD simulations of cubes and cylinders performed with forces and torques obtained from the gradients of the energy neural-nets quantitatively match traditional simulations that uses composite rigid bodies. Both structural quantities and dynamic measures are in agreement, while achieving up to 23 times speed up over traditional molecular dynamics, depending on hardware and system size. The method can, in principle, be applied to any irregular shape with any pair interaction, provided that sufficient training data can be obtained.
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