Multiscale Light-Mater Dynamics at a High-Performance Computing Crossroads

[Grigory Bronevetsky]

Modeling Talks

Multiscale Light-Mater Dynamics at a High-Performance Computing Crossroads Aiichiro Nakano, USC Tues, November 11, 2025 | 9am PT Meet | Youtube Stream Hi all, The presentation will be via Meet and all questions will be addressed there. If you cannot attend live, the event will be recorded and can be found afterward at https://sites.google.com/modelingtalks.org/entry/multiscale-light-mater-dynamics-at-a-high-performance-computing-crossroads More information on previous and future talks: https://sites.google.com/modelingtalks.org/entry/home Abstract:  Light-matter dynamics in topological quantum materials could enable ultrafast (petahertz) and ultralow-energy (attojoule) computing and sensing devices toward sustainable AI-embedded future. A challenge is simulating multiple field and particle equations for light, electrons, and atoms over vast spatiotemporal scales. Meanwhile, high-performance computing is at a historic crossroads, where traditional modeling and simulation applications may not survive the increasing heterogeneity and low-precision focuses of hardware. We have developed a divide-conquer-recombine (DCR)/metamodel-space-algebra (MSA) paradigm to solve the multiscale/multiphysics/heterogeneity/low-precision challenge harnessing hardware heterogeneity and hybrid-precision arithmetic. We have thereby developed a MLMD (multiscale light-matter dynamics) software composed of first-principles DC-MESH (divide-and- conquer Maxwell-Ehrenfest-surface hopping) module for nonadiabatic quantum dynamics (NAQMD) and AI-accelerated XS-NNQMD (excited-state neural-network quantum molecular dynamics) module to expand the spatiotemporal scales of NAQMD. Using 60,000 GPUs of the Aurora supercomputer at Argonne National Laboratory, the DC-MESH and XS-NNQMD modules achieved nearly perfect scalability with 1.87 Exaflop/s performance for the former, thus allowing the simulation of light-induced switching of topological superlattices for future ferroelectric ‘topotronics’. This work suggests new algorithm-hardware co-design pathways at the nexus of post-exascale computing, quantum computing, and AI. Bio: Aiichiro Nakano is a Professor of Computer Science with joint appointments in Physics & Astronomy, Quantitative & Computational Biology, and Collaboratory for Advanced Computing and Simulations at the University of Southern California (USC). He has authored 500+ refereed articles in the areas of scalable scientific algorithms, scientific visualization and machine learning, and computational materials science. He is a recipient of the National Science Foundation Career Award, Okawa Foundation Faculty Research Award, U.S. Department of Energy Aurora Early Science Program Award, and Best Paper Awards at IEEE/ACM Supercomputing, IEEE Virtual Reality, IEEE PDSEC, and ACM HPCAsia. He is a Fellow of the American Physical Society.