OMC-bench and AtomBit-OMC: task-aligned benchmarks and robust, interpretable machine-learned interatomic potentials for organic molecular crystals

Organic molecular crystals (OMCs) underpin key applications in pharmaceuticals and functional materials, but accurate atomistic simulation remains difficult because OMC stability depends on subtle noncovalent interactions, conformational flexibility, and polymorphism. Although machine-learned interatomic potentials (MLIPs) have advanced rapidly for inorganic materials, progress for OMCs has been constrained by (1) the lack of large, physically realistic training data and (2) the lack of task-aligned benchmarks that probe deployment-relevant behaviour. Here we introduce OMC-bench, a benchmark spanning four practical tasks—large-cell OOD force/stress prediction, lattice dynamics, cocrystal relaxation, and polymorph energy ranking—and an extensive OMC training dataset sampled from near-equilibrium to strongly perturbed regimes. Benchmarking off-the-shelf MLIPs reveals a clear transfer gap, with improved but still task-dependent performance at foundation scale. We train OMC-specialized models and develop AtomBit-OMC, a new, efficient SE(3)-equivariant Cartesian-tensor model with physics-informed gating and coupled scalar–equivariant updates. AtomBit-OMC variants achieve strong cross-task performance on OMC-bench, including the lowest cocrystal structural-recovery failure rate and the best polymorph-ranking metrics, while providing qualitative gate-weight maps that highlight chemically relevant covalent and directional noncovalent contacts in representative OMCs. Together, OMC-bench, the OMC training dataset, and AtomBit-OMC provide a practical foundation for accelerating MLIP development in OMC modelling and discovery.