MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow

TL;DR

MOFA is an open-source workflow combining generative AI and simulations to rapidly discover new metal-organic frameworks for carbon capture, efficiently utilizing HPC resources for high-throughput screening and filtering.

Contribution

This work introduces MOFA, a novel integrated platform that combines GPU-accelerated GenAI with large-scale simulations for accelerated MOF discovery.

Findings

High-throughput generation of MOFs on a supercomputer

Top 10 CO2 adsorption capacities in the hMOF dataset

Linear scaling of MOF quality with computational resources

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.