Machine learning (ML) potentials and force fields have revolutionized atomistic modeling by facilitating larger and longer simulations crucial for modeling dynamic and kinetic properties. General-purpose ML potentials (e.g., ANI-2x , OrbNet Denali, 7 AIQM1 8 ) model chemical (local) interactions and can be useful for subsets of chemical space. These approaches assist molecular screening but require enormous data sets of hundreds of thousands of structures. Alternatively, ML potentials can be tailored to specific systems to improve desired simulation reliability. This requires that models be retrained for each system, so training must involve minimal human involvement and computations to be practical.