2022
DOI: 10.26434/chemrxiv-2021-gs0wj-v3
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Data-driven matching of experimental crystal structures and gas adsorption isotherms of metal-organic frameworks

Abstract: Porous metal-organic frameworks are a class of materials with great promise in gas separation and gas storage applications. Due to the large material space, computational screening techniques have long been an important part of the scientific toolbox. However, a broad validation of molecular simulations in these materials is hampered by the lack of a connection between databases of gas adsorption experiments and databases of the atomic crystal structure of corresponding materials. This work aims to connect the… Show more

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“…63,64 The number of MOF structures generated via reinforcement learning was much less than the number used for pre-training, and these were optimized using the Universal Forceeld (UFF) 61 as implemented in the Forcite Module of Materials Studio 2019. 65 The EQEq (extended charge equilibration) method 66,67 was used to generate the partial charges of the framework atoms of the MOFs. The lowest common oxidation states of the elements were chosen as their charge centers.…”
Section: Computational Details For Molecular Simulationsmentioning
confidence: 99%
“…63,64 The number of MOF structures generated via reinforcement learning was much less than the number used for pre-training, and these were optimized using the Universal Forceeld (UFF) 61 as implemented in the Forcite Module of Materials Studio 2019. 65 The EQEq (extended charge equilibration) method 66,67 was used to generate the partial charges of the framework atoms of the MOFs. The lowest common oxidation states of the elements were chosen as their charge centers.…”
Section: Computational Details For Molecular Simulationsmentioning
confidence: 99%