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 simulation

SUBMITTER: Park H 

PROVIDER: S-EPMC11341761 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature