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This work brings the leading accuracy, sample efficiency, and robustness ofdeep equivariant neural networks to the extreme computational scale. This isachieved through a combination of innovative model architecture, massiveparallelization, and models and implementations optimized for efficient GPUutilization. The resulting Allegro architecture bridges the accuracy-speedtradeoff of atomistic simulations and enables description of dynamics instructures of unprecedented complexity at quantum fidelity. To illustrate thescalability of Allegro, we perform nanoseconds-long stable simulations ofprotein dynamics and scale up to a 44-million atom structure of a complete,all-atom, explicitly solvated HIV capsid on the Perlmutter supercomputer. Wedemonstrate excellent strong scaling up to 100 million atoms and 70% weakscaling to 5120 A100 GPUs.

Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size