MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow
This work addresses the problem of efficiently discovering materials for carbon capture using a scalable AI-simulation workflow, which is incremental as it combines existing methods into a unified framework.
The authors tackled the challenge of integrating GPU-accelerated generative AI with CPU- and GPU-optimized simulations for high-throughput discovery of metal-organic frameworks (MOFs) for carbon capture, achieving CO2 adsorption capacities in the top 10 of the hypothetical MOF dataset and demonstrating linear scaling with up to 450 nodes.
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.