新模型无需显式学习电荷，就能精准模拟长程静电作用。
Learning charges and long-range interactions from energies and forces
- 通过隐式求和方法捕捉长程电场，不依赖传统电荷参数化
- 在带电分子、电解质等复杂体系中精度优于显式电荷模型
- 能自动推断物理部分电荷与偶极矩，适合材料与化学模拟
精确建模原子体系中的长程力对材料与化学系统的性质预测至关重要。然而，标准机器学习势函数（MLIPs）通常依赖短程近似，难以处理显著的静电和色散作用。我们此前提出的潜伏艾尔德曼求和（LES）方法可在不显式学习原子电荷或电荷平衡的情况下捕捉长程静电相互作用。本文进一步扩展了该方法，使其能够学习物理意义上的部分电荷、编码电荷状态，并可施加电荷中性约束。我们在多种挑战性系统上进行了基准测试，包括带电分子、离子液体、电解质溶液、极性二肽、表面吸附、电解质/固态界面及固-固界面。结果表明，该方法能有效推断出物理部分电荷、偶极与四极矩，且相比显式学习电荷的方法具有更高精度。因此，LES提供了一种高效、可解释且泛化能力强的机器学习势框架，适用于复杂体系中的电荷转移与长程作用模拟。
原文摘要 · Abstract (English)
Accurate modeling of long-range forces is critical in atomistic simulations, as they play a central role in determining the properties of materials and chemical systems. However, standard machine learning interatomic potentials (MLIPs) often rely on short-range approximations, limiting their applicability to systems with significant electrostatics and dispersion forces. We recently introduced the Latent Ewald Summation (LES) method, which captures long-range electrostatics without explicitly learning atomic charges or charge equilibration. Extending LES, we incorporate the ability to learn physical partial charges, encode charge states, and the option to impose charge neutrality constraints. We benchmark LES on diverse and challenging systems, including charged molecules, ionic liquid, electrolyte solution, polar dipeptides, surface adsorption, electrolyte/solid interfaces, and solid-solid interfaces. Our results show that LES can effectively infer physical partial charges, dipole and quadrupole moments, as well as achieve better accuracy compared to methods that explicitly learn charges. LES thus provides an efficient, interpretable, and generalizable MLIP framework for simulating complex systems with intricate charge transfer and long-range
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