Advanced ab initio materials simulations face growing challenges as increasing systems and phenomena complexity requires higher accuracy, driving up computational demands. Quantum many-body GW methods are state-of-the-art for treating electronic excited states and couplings but often hindered due to the costly numerical complexity. Here, we present innovative implementations of advanced GW methods within the BerkeleyGW package, enabling large-scale simulations on Frontier and Aurora exascale platforms. Our approach demonstrates exceptional versatility for complex heterogeneous systems with up to 17,574 atoms, along with achieving true performance portability across GPU architectures. We demonstrate excellent strong and weak scaling to thousands of nodes, reaching double-precision core-kernel performance of 1.069 ExaFLOP/s on Frontier (9,408 nodes) and 707.52 PetaFLOP/s on Aurora (9,600 nodes), corresponding to 59.45% and 48.79% of peak, respectively. Our work demonstrates a breakthrough in utilizing exascale computing for quantum materials simulations, delivering unprecedented predictive capabilities for rational designs of future quantum technologies.

Large-scale GW calculations are the state-of-the-art approach to accurately describe many-body excited-state phenomena in complex materials. This is critical for novel device design but due to their extremely high computational cost, these calculations often run at a limited scale. In this paper, we present algorithm and implementation advancements made in the materials science code BerkeleyGW to scale calculations to the order of over 10,000 electrons utilizing the entire Summit at OLCF. Excellent strong and weak scaling is observed, and a 105.9 PFLOP/s double-precision performance is achieved on 27,648 V100 GPUs, reaching 52.7% of the peak. This work for the first time demonstrates the possibility to perform GW calculations at such scale within minutes on current HPC systems, and leads the way for future efficient HPC software development in materials, physical, chemical, and engineering sciences.