Scaling Up Quantum Fidelity - Argonne Systems Used to Smooth Out Quantum Molecular Dynamics Simulations

Materials science and engineering is getting a boost from quantum methods, as neural-network quantum molecular dynamics (NNQMD) simulations, based on machine learning, are revolutionizing atomistic materials simulations. A state-of-the-art (SOTA) model, called “Allegro” (a musical term meaning “fast and lively”), demonstrates increased accuracy and speed — but it struggles with fidelity scaling on massively parallel supercomputers, a big problem in the era of exascale. A research group of experts from the University of Southern California, Sony Group Corporation, and Argonne were determined to find a way to scale successfully.

To address the fidelity scaling challenge, the researchers used ALCF's powerful computing resources, including the 34-petaflop Polaris supercomputer, to solve the problem by combining the Allegro model with sharpness-aware minimization (SAM) to increase its smoothness and robustness. Polaris is GPU-equipped and its workloads are orchestrated by Altair PBS Professional, a fast, powerful workload manager designed to improve productivity, optimize utilization and efficiency, and simplify resource administration for even the biggest high-performance computing workloads, including demanding materials modeling. The resulting “Allegro-Legato” model — Legato means “smooth” — increases time to failure without compromising speed or accuracy.