pyRMG: A framework for high-throughput, large-cell DFT calculations on supercomputers.

Area of Science:

• Computational Materials Science
• Quantum Chemistry
• High-Performance Computing

Background:

• Exascale computing offers unprecedented power for chemical simulations.
• Real-space multigrid (RMG) density functional theory (DFT) codes exhibit excellent scalability on thousands of processors (GPUs).
• Limited workflow infrastructure has hindered the adoption of RMG-DFT codes.

Purpose of the Study:

• To develop a user-friendly Python package, pyRMG, for streamlining RMG-DFT calculations.
• To enhance the accessibility and automation of large-scale materials simulations.
• To facilitate high-throughput studies using RMG-DFT on leadership-class computing platforms.

Main Methods:

• Developed pyRMG, a Python package integrating pymatgen and ASE.
• Automated input generation and convergence checking for RMG-DFT.
• Integrated pyRMG with modern job schedulers (e.g., Flux) for exascale platforms (Frontier, Perlmutter).

Main Results:

• Demonstrated pyRMG's capability for high-throughput DFT studies.
• Successfully analyzed strain effects in 2D 2L-Bi2Se3/2L-NbSe2 heterostructures.
• Showcased RMG-based workflows converging with limited user intervention.

Conclusions:

• pyRMG significantly lowers the barrier to entry for using scalable RMG-DFT methods.
• The package enables efficient, automated materials discovery and analysis on advanced computing architectures.
• pyRMG empowers researchers to conduct complex simulations with greater ease and speed.