Symbolic Ensemble Learning Enables Discovery of Fast Accurate Physics-Based Interatomic Potentials

Machine learning has transformed materials simulation by delivering force fields with ab initio accuracy, yet bridging the gap between high-dimensional regression and physical interpretability remains a grand challenge. Conventional analytical potentials offer transparency but often fail to capture