A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture

Abstract

Metal-organic frameworks (MOFs) exhibit great promise for CO₂ capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential building blocks. Here, we introduce GHP-MOFassemble, a generative artificial intelligence (AI), high performance framework for the rational and accelerated design of MOFs with high CO₂ adsorption capacity and synthesizable linkers. GHP-MOFassemble generates novel linkers, assembled with one of three pre-selected metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer) into MOFs in a primitive cubic topology. GHP-MOFassemble screens and validates AI-generated MOFs for uniqueness, synthesizability, structural validity, uses molecular dynamics simulations to study their stability and chemical consistency, and crystal graph neural networks and Grand Canonical Monte Carlo simulations to quantify their CO₂ adsorption capacities. We present the top six AI-generated MOFs with CO₂ capacities greater than 2m mol g⁻¹, i.e., higher than 96.9% of structures in the hypothetical MOF dataset.

Metal-organic frameworks have demonstrated great promise for application in CO₂ capture, but the enormous breadth of potential building blocks available makes searching the chemical space for the best-performing materials challenging via traditional methods. Here, the authors present a high-throughput computational framework based on a molecular generative diffusion model to accelerate the discovery of MOF structures with high CO₂ capacities and synthesizable linkers.