MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow
Journal:
arXiv
Published Date:
Jan 18, 2025
Abstract
We present MOFA, an open-source generative AI (GenAI) plus simulation
workflow for high-throughput generation of metal-organic frameworks (MOFs) on
large-scale high-performance computing (HPC) systems. MOFA addresses key
challenges in integrating GPU-accelerated computing for GPU-intensive GenAI
tasks, including distributed training and inference, alongside CPU- and
GPU-optimized tasks for screening and filtering AI-generated MOFs using
molecular dynamics, density functional theory, and Monte Carlo simulations.
These heterogeneous tasks are unified within an online learning framework that
optimizes the utilization of available CPU and GPU resources across HPC
systems. Performance metrics from a 450-node (14,400 AMD Zen 3 CPUs + 1800
NVIDIA A100 GPUs) supercomputer run demonstrate that MOFA achieves
high-throughput generation of novel MOF structures, with CO$_2$ adsorption
capacities ranking among the top 10 in the hypothetical MOF (hMOF) dataset.
Furthermore, the production of high-quality MOFs exhibits a linear relationship
with the number of nodes utilized. The modular architecture of MOFA will
facilitate its integration into other scientific applications that dynamically
combine GenAI with large-scale simulations.
