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

Chemistry

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

H. Park, X. Yan, et al.

Discover how GHP-MOFassemble, a groundbreaking generative AI framework developed by Hyun Park and colleagues, is revolutionizing CO2 capture through the design of innovative metal-organic frameworks (MOFs) with exceptional adsorption capacities. This research highlights MOFs that outperform most existing structures, paving the way for greener technologies.

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Playback language: English
Abstract
Metal-organic frameworks (MOFs) are promising for CO2 capture, but finding optimal materials is challenging due to the vast chemical space. This paper introduces GHP-MOFassemble, a generative AI framework for designing MOFs with high CO2 adsorption capacity. GHP-MOFassemble generates novel linkers, assembles them with pre-selected metal nodes into MOFs, and screens for uniqueness, synthesizability, and structural validity. Molecular dynamics and Grand Canonical Monte Carlo simulations assess stability and CO2 adsorption capacity. The top six AI-generated MOFs show CO2 capacities exceeding 2 mmol g⁻¹, surpassing 96.9% of structures in a hypothetical MOF dataset.
Publisher
Communications Chemistry
Published On
Feb 14, 2024
Authors
Hyun Park, Xiaoli Yan, Ruijie Zhu, Eliu A. Huerta, Santanu Chaudhuri, Donny Cooper, Ian Foster, Emad Tajkhorshid
Tags
CO2 capture
metal-organic frameworks
generative AI
adsorption capacity
synthesizability
molecular dynamics
Monte Carlo simulations

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