用新架构提升分子力场预测精度，更好捕捉复杂原子相互作用。
Equivariant Graph Network Approximations of High-Degree Polynomials for Force Field Prediction
- 基于边增强与原子簇展开技术，构建新型等变网络PACE。
- 在多个基准上实现能量与受力预测的最新性能，跨温度泛化强。
- 适合需要高精度力场建模的分子模拟研究者使用。
近期等变深度模型在分子动力学模拟中预测原子势能和力场方面展现出巨大潜力。通过球谐函数（SH）和张量积（TP），这些等变网络增强了对对称性及多体相互作用的物理理解。除了编码物理先验知识，SH与TP也是表示等变多项式函数的关键。本文分析了等变架构中的等变多项式函数，并提出一种新型等变网络PACE。该方法利用边增强机制与原子簇展开（ACE）技术，更高效地逼近更高阶的SE(3) × S_n等变多项式函数。在常用基准测试中，PACE在原子能量与力场预测任务上均达到当前最优表现，且在不同温度条件下的分子动力学模拟中展现出稳健的泛化能力。代码已开源，包含于AIRS库中：https://github.com/divelab/AIRS/。
原文摘要 · Abstract (English)
Recent advancements in equivariant deep models have shown promise in accurately predicting atomic potentials and force fields in molecular dynamics simulations. Using spherical harmonics (SH) and tensor products (TP), these equivariant networks gain enhanced physical understanding, like symmetries and many-body interactions. Beyond encoding physical insights, SH and TP are also crucial to represent equivariant polynomial functions. In this work, we analyze the equivariant polynomial functions for the equivariant architecture, and introduce a novel equivariant network, named PACE. The proposed PACE utilizes edge booster and the Atomic Cluster Expansion (ACE) technique to approximate a greater number of $SE(3) \times S_n$ equivariant polynomial functions with enhanced degrees. As experimented in commonly used benchmarks, PACE demonstrates state-of-the-art performance in predicting atomic energy and force fields, with robust generalization capability across various geometric distributions under molecular dynamics (MD) across different temperature conditions. Our code is publicly available as part of the AIRS library https://github.com/divelab/AIRS/.
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