TY - JOUR
T1 - A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture
AU - Park, Hyun
AU - Yan, Xiaoli
AU - Zhu, Ruijie
AU - Huerta, Eliu A.
AU - Chaudhuri, Santanu
AU - Cooper, Donny
AU - Foster, Ian
AU - Tajkhorshid, Emad
N1 - This work was supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357, and by the Braid project of the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research, under contract number DE-AC02-06CH11357. The work used resources of the Argonne Leadership Computing Facility, a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. EAH and IF acknowledge support from National Science Foundation (NSF) award OAC-2209892. SC and XY acknowledge partial support from NSF Future of Manufacturing Research Grant 2037026. This research also used the Delta advanced computing and data resources, which is supported by the National Science Foundation (award OAC 2005572) and the State of Illinois. Delta is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications.
PY - 2024/12
Y1 - 2024/12
N2 - 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.
AB - 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.
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U2 - 10.1038/s42004-023-01090-2
DO - 10.1038/s42004-023-01090-2
M3 - Article
C2 - 38355806
AN - SCOPUS:85185245417
SN - 2399-3669
VL - 7
JO - Communications Chemistry
JF - Communications Chemistry
IS - 1
M1 - 21
ER -