A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials 1. This novel study introduces the Latent Ewald Summation (LES) method, a universal framework to incorporate long-range electrostatics into machine learning interatomic potentials (MLIPs) without needing explicit charge labels. The LES method infers electrostatic interactions directly from energy and force data, significantly enhancing the accuracy of MLIPs for systems with significant electrostatics. 2. The LES framework is designed as a standalone library compatible with various short-range MLIPs, including MACE, NequIP, CACE, and CHGNet. It demonstrates remarkable improvements in predicting Born effective charges (BECs) and dipole moments, even when trained exclusively on energy and force data. This capability is crucial for accurately simulating systems with significant dielectric response, such as polar materials and charged molecules. 3. The study benchmarks LES-enhanced models on diverse systems, including bulk water, polar dipeptides, and gold dimer adsorption on defective substrates. Across all systems, LES not only reduces energy and force prediction errors but also accurately captures physical observables like BECs and adsorption energies. This highlights the robustness and versatility of the LES method. 4. The LES framework is scalable to large and chemically diverse datasets. The authors demonstrate this by training MACELES-OFF on the SPICE dataset, which includes organic molecules and biomolecules. MACELES-OFF outperforms its short-range counterpart in predicting bulk liquid properties and electrical response properties like IR spectra. 5. The LES method addresses a core limitation of current MLIPs by enabling efficient long-range electrostatics without additional training on electrical properties. This opens the door for developing universal MLIPs with full electrostatic physics, which can be applied across a wide range of chemical and biological systems. 💻Code: github.com/ChengUCB/les 📜Paper: arxiv.org/abs/2507.14302 #MachineLearning #Electrostatics #InteratomicPotentials #MaterialsScience #ComputationalChemistry #LongRangeInteractions