Enabling Domain-Specific Atomistic Models: A Machine Learning Potential for the Solid Acid Family

Machine-learned interatomic potentials trained across the periodic table have made atomistic simulation broadly accessible, and specializing them to a single compound class is widely expected to improve accuracy. Yet examples remain scarce, and fewer still surpass universal models in speed or reach