新模型通过全局电荷重分布提升材料模拟精度。
Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution
- 基于原子电负性预测构建电荷平衡机制，显式建模长程库仑作用。
- 在周期与非周期体系上，能量和力预测误差低于现有方法。
- 适合研究电荷转移、长程相互作用的复杂材料系统。
机器学习势函数（MLIPs）为材料性质预测提供了比量子力学模拟更高效的计算替代方案。当前主流的消息传递图神经网络依赖局部描述符对称函数建模原子相互作用，但在存在长程相互作用、电荷转移和组分异质性的体系中表现受限。本文提出一种新的等变MLIP，通过显式处理电子自由度，特别是系统的全局电荷分布，引入长程库仑相互作用。该方法基于预测的原子电负性实现电荷平衡。我们在一系列周期与非周期基准数据集上系统评估了模型性能，结果表明其在能量和力预测上优于仅考虑短程的等变模型及包含长程但不考虑电荷动态的不变模型。该方法显著提升了长程相互作用与电荷异质性体系的模拟精度与效率，拓展了MLIP在计算材料科学中的应用边界。
原文摘要 · Abstract (English)
Machine learning interatomic potentials (MLIPs) provide a computationally efficient alternative to quantum mechanical simulations for predicting material properties. Message-passing graph neural networks, commonly used in these MLIPs, rely on local descriptor-based symmetry functions to model atomic interactions. However, such local descriptor-based approaches struggle with systems exhibiting long-range interactions, charge transfer, and compositional heterogeneity. In this work, we develop a new equivariant MLIP incorporating long-range Coulomb interactions through explicit treatment of electronic degrees of freedom, specifically global charge distribution within the system. This is achieved using a charge equilibration scheme based on predicted atomic electronegativities. We systematically evaluate our model across a range of benchmark periodic and non-periodic datasets, demonstrating that it outperforms both short-range equivariant and long-range invariant MLIPs in energy and force predictions. Our approach enables more accurate and efficient simulations of systems with long-range interactions and charge heterogeneity, expanding the applicability of MLIPs in computational materials science.
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