标题： MGNN：用于通用分子势能的矩图神经网络 显示英文标题

标题： MGNN: Moment Graph Neural Network for Universal Molecular Potentials

摘要： 在科学探索中，对于分子系统表示的高效且稳健的深度学习模型的需求日益关键。 消息传递神经网络的出现标志着基于图的学习的变革时代，尤其是在预测化学性质和加速分子动力学研究领域。 我们提出了矩图神经网络（MGNN），这是一种旋转不变的消息传递神经网络架构，利用了三维分子图的矩表示学习，能够有效地捕捉三维分子结构中固有的细微空间关系。 MGNN在QM9和修订后的MD17等基准数据集上表现出优于现有方法的新最先进性能。 MGNN的能力还扩展到动态模拟，能够准确预测如非晶电解质等复杂系统的结构和动力学特性，其结果与从头算模拟的结果高度一致。 MGNN在分子光谱模拟中的应用展示了其显著提升计算工作流程的潜力，为传统电子结构方法提供了一个有前景的替代方案。 显示英文摘要

摘要： The quest for efficient and robust deep learning models for molecular systems representation is increasingly critical in scientific exploration. The advent of message passing neural networks has marked a transformative era in graph-based learning, particularly in the realm of predicting chemical properties and expediting molecular dynamics studies. We present the Moment Graph Neural Network (MGNN), a rotation-invariant message passing neural network architecture that capitalizes on the moment representation learning of 3D molecular graphs, is adept at capturing the nuanced spatial relationships inherent in three-dimensional molecular structures. MGNN demonstrates new state-of-the-art performance over contemporary methods on benchmark datasets such as QM9 and the revised MD17. The prowess of MGNN also extends to dynamic simulations, accurately predicting the structural and kinetic properties of complex systems such as amorphous electrolytes, with results that closely align with those from ab-initio simulations. The application of MGNN to the simulation of molecular spectra exemplifies its potential to significantly enhance the computational workflow, offering a promising alternative to traditional electronic structure methods