Exascale computing delivers the raw power to simulate ever larger and more chemically realistic systems, but realizing this potential requires codes that can efficiently use thousands of processors. Our real-space multigrid (RMG) density functional theory (DFT) code's grid-decomposition approach scales nearly linearly with the number of GPUs, even for simulations exceeding thousands of atoms. This scalability makes RMG a compelling tool for high-throughput DFT studies of materials that would otherwise be bottlenecked in other codes (for example, by global Fast Fourier Transforms in plane-wave DFT). However, the limited workflow infrastructure for RMG has thus far constrained its adoption to a small user community. In this work, we present pyRMG, a Python package designed to streamline the setup and execution of RMG DFT calculations. Built on the pymatgen and ASE computational materials science Python packages, pyRMG automates input generation and convergence checking, and integrates with modern job schedulers (e.g., Flux) on leadership-class platforms such as Frontier and Perlmutter. We demonstrate pyRMG for a high-throughput study of interfacial strain and twist-angle effects in lattice-matched, 2D Bi2Se3/NbSe2 heterostructures, which offers chemical insights into this system and shows that RMG-based workflows can converge with limited user intervention.
@article{arxiv.2509.16775,
title = {pyRMG: A Framework for High-Throughput, Large-Cell DFT Calculations on Supercomputers},
author = {R. J. Morelock and S. Bagchi and E. L. Briggs and W. Lu and J. Bernholc and P. Ganesh},
journal= {arXiv preprint arXiv:2509.16775},
year = {2026}
}