Bingqing Cheng

Standard machine learning interatomic potentials (MLIPs) often rely on short-range approximations, limiting their applicability to systems with significant electrostatics. We recently introduced the Latent Ewald Summation (LES) method, which learns long-range electrostatics from *just energy and force data*. We show that LES can effectively infer physical partial charges, polarization and Born effective charge (BEC) tensors, as well as achieve better accuracy compared to methods that explicitly learn charges. As demonstrations, we predict the infrared spectra of bulk water under zero or finite external electric fields, ionic conductivities of high-pressure superionic ice, and the phase transition and hysteresis in ferroelectric PbTiO3 perovskite.