标题： 基于机器学习的局域密度矩阵原子力和能量的线性标度计算 显示英文标题

标题： Linear Scaling Calculation of Atomic Forces and Energies with Machine Learning Local Density Matrix

摘要： 准确计算能量和原子力的线性标度方法是加速和改进分子动力学模拟的关键途径。 本文介绍了一种名为HamGNN-DM的机器学习模型，该模型旨在使用分子动力学模拟中的局域密度矩阵来预测原子力和能量。 这种方法具有O(n)的时间复杂度，可以实现高效预测，非常适合大规模系统。 不同系统的实验表明，HamGNN-DM在预测不同大小系统的原子力时达到了DFT级别的精度，这对分子动力学至关重要。 此外，该方法在整个动力学过程中提供了有价值的电子结构信息，并表现出稳健的性能。 显示英文摘要

摘要： Accurately calculating energies and atomic forces with linear-scaling methods is a crucial approach to accelerating and improving molecular dynamics simulations. In this paper, we introduce HamGNN-DM, a machine learning model designed to predict atomic forces and energies using local density matrices in molecular dynamics simulations. This approach achieves efficient predictions with a time complexity of O(n), making it highly suitable for large-scale systems. Experiments in different systems demonstrate that HamGNN-DM achieves DFT-level precision in predicting the atomic forces in different system sizes, which is vital for the molecular dynamics. Furthermore, this method provides valuable electronic structure information throughout the dynamics and exhibits robust performance.