E2Former-V3: Atom-Role-Aware Equivariant Interatomic Potentials

Machine-learned interatomic potentials enable high-level accuracy for large-scale biological simulation, yet remain costly. In aqueous systems, hydrogens and the water solvent dominate the atom count, while uniform equivariant architectures give every atom the same representation capacity and spend most of their computation on them. We introduce E2Former-V3, an atom-role-aware equivariant potential architecture that represents every atom explicitly and keeps forces conservative. A static chemical role, assigned before the backbone runs, sets each atom’s latent width and how often it is updated. Two configurations follow. E2Former-V3-Standard routes hydrogens through half as many full-width block updates, reaching a force MAE of 8.146 meV/ ˚A, slightly better than E2Former-V2 (Huang et al., 2026), at 1.75–1.84× its throughput. E2Former-V3-Flash routes hydrogens and water oxygens through a narrow stream with fewer updates, trading 3.126 meV/ ˚A higher force error for 4.00–5.85× the throughput. We run molecular dynamics over water, a salt solution and a solvated protein for 20 ps, across three ensembles and three seeds. The runs hold their target temperature, keep chemical structure intact, and reproduce the measured liquid structure to within a few hundredths of an ˚Angstrom. On the solvated protein, both production checkpoints complete the full 1 ns without water fusion, nonbonded clashes or solute collapse.