标题： 机器学习原子间势能函数用于长程系统 显示英文标题

标题： Machine-Learning Interatomic Potentials for Long-Range Systems

摘要： 机器学习原子间势能函数已成为分子模拟中革命性的力场模型类别，在计算成本仅为几分之一的情况下实现了量子力学精度，并使大规模系统在长时间尺度上的模拟成为可能。 然而，它们通常专注于建模局部环境，忽略了重要的长程相互作用。 我们提出了一种高斯和神经网络（SOG-Net），这是一种轻量且通用的框架，用于将长程相互作用整合到机器学习力场中。 SOG-Net 采用了一个潜在变量学习网络，能够无缝连接短程和长程组件，并结合了一个高效的傅里叶卷积层，以包含长程效应。 通过在不同卷积层上学习高斯和乘数，SOG-Net 能够自适应地捕捉多种长程衰减行为，同时通过非均匀快速傅里叶变换在训练和模拟过程中保持接近线性的计算复杂度。 该方法已被证明对广泛范围的长程系统有效。 显示英文摘要

摘要： Machine-learning interatomic potentials have emerged as a revolutionary class of force-field models in molecular simulations, delivering quantum-mechanical accuracy at a fraction of the computational cost and enabling the simulation of large-scale systems over extended timescales. However, they often focus on modeling local environments, neglecting crucial long-range interactions. We propose a Sum-of-Gaussians Neural Network (SOG-Net), a lightweight and versatile framework for integrating long-range interactions into machine learning force field. The SOG-Net employs a latent-variable learning network that seamlessly bridges short-range and long-range components, coupled with an efficient Fourier convolution layer that incorporates long-range effects. By learning sum-of-Gaussian multipliers across different convolution layers, the SOG-Net adaptively captures diverse long-range decay behaviors while maintaining close-to-linear computational complexity during training and simulation via non-uniform fast Fourier transforms. The method is demonstrated effective for a broad range of long-range systems.