Machine learning interatomic potentials (MLIPs) often neglect long-range interactions, such as electrostatic and dispersion forces. In this work, we introduce a straightforward and efficient method to account for long-range interactions by learning a latent variable from local atomic descriptors and applying an Ewald summation to this variable. We demonstrate that in systems including charged and polar molecular dimers, bulk water, and water-vapor interface, standard short-ranged MLIPs can lead to unphysical predictions even when employing message passing. The long-range models effectively eliminate these artifacts, with only about twice the computational cost of short-range MLIPs.

翻译：机器学习原子间势（MLIPs）通常忽略长程相互作用，例如静电力和色散力。本研究提出了一种直接高效的方法来处理长程相互作用：通过从局部原子描述符中学习隐变量，并对该变量应用埃瓦尔德求和。我们证明，在包含带电分子二聚体、极性分子二聚体、体相水以及水-汽界面的体系中，即使采用消息传递机制，标准短程MLIPs仍可能导致非物理预测。长程模型能有效消除这些伪影，其计算成本仅为短程MLIPs的两倍左右。