ATOM: A Pretrained Neural Operator for Multitask Molecular Dynamics
Original Research Paper
ICLR 2026 paper proposing ATOM, a quasi-equivariant pretrained transformer neural operator that requires no explicit molecular graph and uses temporal attention for parallel decoding of multiple future states; curates TG80 (80 compounds, 2.5M fs trajectories) and demonstrates zero-shot generalization on MD17/RMD17/MD22.
arXiv preprint (copyright held by authors) Neural OperatorMolecular DynamicsTransformer