Long-Range Machine Learning Interatomic Potentials for Defect Energetics in SrTiO$_3$
Machine learning interatomic potentials (MLIPs) have advanced rapidly in recent years, yet the majority of models remain semilocal in nature and neglect long-range electrostatic interactions. A growing number of long-range models have emerged to address this limitation, but systematic benchmarks comparing their performance on physically realistic systems remain scarce. In this work, we assess three long-range MLIPs -- MACE-POLAR, MACE-LES, and LOREM, against a short-range baseline MACE model, on their ability to capture long-range Coulombic interactions, progressing from idealised synthetic point-charge systems to first-principles data on realistic crystal structures. This progression allows us to isolate model's capability to learn electrostatic interactions from the many competing energy contributions, such as dielectric screening and exchange-correlation, that are present in DFT total energies. As a physically motivated benchmark system, we focus on strontium titanate SrTiO$_3$ with Sr--O Schottky vacancy pairs, a material in which long-range charge interactions play a central role and which serves as a natural stepping stone towards the study of charged polarons and other electronic and optical properties. Building on this benchmark, we further examine oxygen vacancy migration in the presence of the Sr--O Schottky pair, using climbing-image nudged elastic band calculations and molecular dynamics to obtain activation barriers as a function of the vacancy pair separation, providing further extrapolation test of each model's ability to capture the electrostatic effects that govern defect transport.