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- W4382520131 abstract "Abstract We introduce GHP-MOFassemble, a Generative artificial intelligence (AI), High Performance framework to accelerate the rational discovery of metal-organic frameworks (MOFs) with high CO2 capacity and synthesizable linkers. We combine a diffusion model, a class of generative AI, to generate novel linkers that are assembled with one of three pre-selected nodes into MOFs in a primitive cubic (pcu) topology. The CO2 capacities of these AI-generated MOFs are predicted using a modified version of the crystal graph convolutional neural network model. We then use the LAMMPS code to perform molecular dynamics simulations to identify AI-generated MOF structures that converge to stable structures, and maintain their porous properties throughout the simulations. Among 120,000 pcu MOF candidates generated by GHP-MOFassemble, with three distinct metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer), a total of 102 structures completed molecular dynamics simulations at 1bar with predicted CO2 capacity higher than 2mmol/g at 0.1 bar, which corresponds to the top 5% of hMOF in the hypothetical MOF (hMOF) dataset in the MOFX-DB database. Among these candidates, 18 have change in density lower than 1% during molecular dynamics simulations, indicating their stability. The top five GHP-MOFassemble's MOFs have CO2 capacities higher than 96.9% of hMOF structures." @default.
- W4382520131 created "2023-06-30" @default.
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- W4382520131 date "2023-06-30" @default.
- W4382520131 modified "2023-09-27" @default.
- W4382520131 title "GHP-MOFassemble: Diffusion modeling, high throughput screening, and molecular dynamics for rational discovery of novel metal-organic frameworks for carbon capture at scale" @default.
- W4382520131 doi "https://doi.org/10.21203/rs.3.rs-3084157/v1" @default.
- W4382520131 hasPublicationYear "2023" @default.
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