用可解释的电荷均衡机制，让图神经网络高效建模长程分子作用力。
Learning Non-Local Molecular Interactions via Equivariant Local Representations and Charge Equilibration
- 引入电荷均衡层（CELLI），将经典电荷均衡法扩展为通用模块。
- 在基准测试中达到严格局部模型的顶尖性能，且保持高效率。
- 适用于多种数据集与大分子结构，兼具可解释性与稳定性。
基于化学局域性的图神经网络势函数可在显著降低计算成本的同时达到接近量子力学的精度。消息传递型图神经网络通过在邻近粒子间传播局部信息来建模超越邻域的相互作用，同时保持有效局域性。然而，局域性限制了对电荷转移、静电相互作用和色散效应等长程效应的建模能力。本文提出用于长程相互作用的电荷均衡层（CELLI），将经典电荷均衡（Qeq）方法推广为现代等变图神经网络势函数中的模型无关构建块。因此，CELLI在保持高可解释性（显式建模电荷）的同时，扩展了图神经网络对长程相互作用的建模能力。在基准系统上，CELLI实现了严格局部模型的最先进性能，并在多样化数据集和大结构上表现出良好的泛化能力，同时具备高计算效率与鲁棒预测能力。
原文摘要 · Abstract (English)
Graph Neural Network (GNN) potentials relying on chemical locality offer near-quantum mechanical accuracy at significantly reduced computational costs. Message-passing GNNs model interactions beyond their immediate neighborhood by propagating local information between neighboring particles while remaining effectively local. However, locality precludes modeling long-range effects critical to many real-world systems, such as charge transfer, electrostatic interactions, and dispersion effects. In this work, we propose the Charge Equilibration Layer for Long-range Interactions (CELLI) to address the challenge of efficiently modeling non-local interactions. This novel architecture generalizes the classical charge equilibration (Qeq) method to a model-agnostic building block for modern equivariant GNN potentials. Therefore, CELLI extends the capability of GNNs to model long-range interactions while providing high interpretability through explicitly modeled charges. On benchmark systems, CELLI achieves state-of-the-art results for strictly local models. CELLI generalizes to diverse datasets and large structures while providing high computational efficiency and robust predictions.
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