Latent Ewald summation for machine learning of long-range interactions

Understand

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.