<ul><li>Accurate modeling of long-range forces is crucial in atomistic simulations for understanding material properties.</li><li>Standard machine learning interatomic potentials often rely on short-range approximations, limiting their applicability in systems with significant electrostatics and dispersion forces.</li><li>The Latent Ewald Summation (LES) method was introduced to capture long-range electrostatics without explicitly learning atomic charges or charge equilibration.</li><li>LES has been successfully applied in benchmarking various challenging systems, showing its effectiveness in inferring physical partial charges, dipole and quadrupole moments, and achieving higher accuracy compared to methods that explicitly learn charges.</li></ul>

Learning charges and long-range interactions from energies and forces

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