2026
A recipe for scalable attention-based ML potentials: unlocking long-range accuracy with all-to-all node attention
ICML 2026poster
Machine-learning interatomic potentials (MLIPs) have advanced rapidly, with many top models relying on strong physics-based inductive bias. However, as models scale to larger systems like biomolecules and electrolytes, they struggle to accurately capture long-range (LR) interactions, leading current…