让机器学习势能模型自动处理长程静电，提升精度与通用性。
A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials
- 通过隐式埃瓦尔德求和，从能量力数据中推断静电相互作用
- 在水、二肽、金团簇等系统上显著提升预测准确率
- 兼容多种模型，适合分子与生物体系的高精度模拟
当前大多数机器学习势能模型依赖短程近似，未显式处理长程静电。为此，我们此前提出潜变量埃瓦尔德求和（LES）方法，仅通过能量与力的训练数据即可学习电荷极化及有效电荷（BECs）。本文将LES作为独立库发布，可与任意短程MLIP集成，并成功应用于MACE、NequIP、CACE和CHGNet。我们在体相水、极性二肽及缺陷基底上的金团簇吸附等体系上进行测试，结果表明LES不仅能正确捕捉静电效应，还显著提高模型精度。进一步地，我们基于SPICE数据集训练了MACELES-OFF模型，构建了面向有机体系（包括生物分子）的通用电静电机器学习势能模型。相较同数据集训练的短程模型MACE-OFF，MACELES-OFF精度更高，能可靠预测偶极矩与有效电荷，对体相液体描述更优。该方法无需直接训练电学性质，即可高效实现长程静电建模，为发展静电基础型机器学习势能铺平道路。
原文摘要 · Abstract (English)
Most current machine learning interatomic potentials (MLIPs) rely on short-range approximations, without explicit treatment of long-range electrostatics. To address this, we recently developed the Latent Ewald Summation (LES) method, which infers electrostatic interactions, polarization, and Born effective charges (BECs), just by learning from energy and force training data. Here, we present LES as a standalone library, compatible with any short-range MLIP, and demonstrate its integration with methods such as MACE, NequIP, CACE, and CHGNet. We benchmark LES-enhanced models on distinct systems, including bulk water, polar dipeptides, and gold dimer adsorption on defective substrates, and show that LES not only captures correct electrostatics but also improves accuracy. Additionally, we scale LES to large and chemically diverse data by training MACELES-OFF on the SPICE set containing molecules and clusters, making a universal MLIP with electrostatics for organic systems including biomolecules. MACELES-OFF is more accurate than its short-range counterpart (MACE-OFF) trained on the same dataset, predicts dipoles and BECs reliably, and has better descriptions of bulk liquids. By enabling efficient long-range electrostatics without directly training on electrical properties, LES paves the way for electrostatic foundation MLIPs.
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