ATOM: A Pretrained Neural Operator for Multitask Molecular Dynamics

Key innovation: relaxes strict equivariance, replaces sequential rollout with parallel temporal attention, and enables multi-task pretraining across compounds and timescales - a significant step toward transferable MD foundation models.

Industrial relevance: directly applicable to computational drug discovery, materials science, and biochemistry MD pipelines that currently rely on expensive QM force calculations.