Accelerated Prediction of Surface Stability and Particle Morphology in Ionic Crystals via Electrostatic Screening
This work presents a fast and scalable approach for predicting surface stability and equilibrium crystal morphology in ionic materials using electrostatic analysis. The method constructs stoichiometric slab terminations and evaluates their electrostatic energies, enabling high-throughput screening of surface configurations at a fraction of the cost of conventional approaches. Polar surfaces are identified through surface dipole moment calculations and stabilized via electrostatics-based reconstruction using replica-exchange Monte Carlo simulations. The surface dipole moment further emerges as an effective descriptor to distinguish the behavior of different classes of materials. By bypassing expensive Density Functional Theory (DFT) calculations, the approach extends naturally to large systems and high-index surfaces that are typically inaccessible to DFT. Electrostatic interactions are shown to capture the dominant trends in relative surface stability across diverse material systems. The method is validated on simple and complex 3D materials as well as 2D layered oxides, where the predicted dominant facets are consistent with reported density functional theory and experimental observations. Importantly, the framework also reveals cases where high-index surfaces play a non-negligible role in the equilibrium morphology. These results establish electrostatics as a fast and reliable route for high-throughput prediction of surface stability and particle morphology, opening a pathway for accelerated materials discovery and providing a robust starting point for more detailed calculations in complex energy materials.