MOFs to Enhance Green\nNH<sub>3</sub> Synthesis in\nPlasma Reactors: Hierarchical Computational Screening Enhanced by\nIterative Machine Learning

Plasma reactors are promising to decarbonize the production\nof\nNH<sub>3</sub>, but their NH<sub>3</sub> energy yields need to improve\nto facilitate their broad adoption. Two emerging strategies to reduce\nenergy inefficiencies aim to protect the freshly formed NH<sub>3</sub> from destruction by the plasma by leveraging NH<sub>3</sub> adsorption\nproperties of porous materials as either catalyst supports or as membranes.\nAs metal–organic frameworks (MOFs) are promising porous materials\nfor adsorption-based applications, we performed large-scale computational\nscreening of 13,460 MOFs to study their potential for the above-mentioned\nuses. To reduce computational cost by ∼10-fold, we developed\na generalizable hierarchical MOF screening strategy that starts with\nthe selection of a 200-MOF set based on NH<sub>3</sub> adsorption\nHenry’s constants, for which the relevant performance metrics\nare calculated via molecular simulation. This set is used to “initialize”\na machine learning (ML) model that predicts the relevant metrics in\nthe whole MOF database, in turn guiding the selection of additional\npromising MOFs to be evaluated via molecular simulation. The ML model\nis then iteratively refined leveraging the emerging molecular simulation\ndata from the MOFs selected at each iteration from the ML predictions\nthemselves. From evaluation of only ∼10% of the database, for\neach use (catalyst support or membrane), 20 extant MOFs were holistically\nassessed and proposed for experimental testing based on desirable\nadsorption properties as well as complementary properties (e.g., high\nthermal decomposition temperature, constituted by earth abundant metals,\netc.). Data-driven material design guidelines also emerged from the\nscreening. For instance, a pore diameter of ∼10 Å and\na heat of adsorption of ∼90 kJ/mol were found beneficial for\nthe catalyst support use. On the other hand, for the membrane-based\nstrategy, a pore diameter of ∼2.75 Å and a heat of adsorption\nof ∼80 kJ/mol were found beneficial. The presence of V was\nfound beneficial for both uses.

Publication Details

Journal
Figshare
Published
2024-11-27
DOI
https://doi.org/10.1021/acsami.4c11396.s001
Primary Topic
Mycorrhizal Fungi and Plant Interactions
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article
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article

MOFs to Enhance Green\nNH3 Synthesis in\nPlasma Reactors: Hierarchical Computational Screening Enhanced by\nIterative Machine Learning

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Mycorrhizal Fungi and Plant Interactions
article

MOFs to Enhance Green\nNH3 Synthesis in\nPlasma Reactors: Hierarchical Computational Screening Enhanced by\nIterative Machine Learning

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Abstract

Plasma reactors are promising to decarbonize the production\nof\nNH<sub>3</sub>, but their NH<sub>3</sub> energy yields need to improve\nto facilitate their broad adoption. Two emerging strategies to reduce\nenergy inefficiencies aim to protect the freshly formed NH<sub>3</sub> from destruction by the plasma by leveraging NH<sub>3</sub> adsorption\nproperties of porous materials as either catalyst supports or as membranes.\nAs metal–organic frameworks (MOFs) are promising porous materials\nfor adsorption-based applications, we performed large-scale computational\nscreening of 13,460 MOFs to study their potential for the above-mentioned\nuses. To reduce computational cost by ∼10-fold, we developed\na generalizable hierarchical MOF screening strategy that starts with\nthe selection of a 200-MOF set based on NH<sub>3</sub> adsorption\nHenry’s constants, for which the relevant performance metrics\nare calculated via molecular simulation. This set is used to “initialize”\na machine learning (ML) model that predicts the relevant metrics in\nthe whole MOF database, in turn guiding the selection of additional\npromising MOFs to be evaluated via molecular simulation. The ML model\nis then iteratively refined leveraging the emerging molecular simulation\ndata from the MOFs selected at each iteration from the ML predictions\nthemselves. From evaluation of only ∼10% of the database, for\neach use (catalyst support or membrane), 20 extant MOFs were holistically\nassessed and proposed for experimental testing based on desirable\nadsorption properties as well as complementary properties (e.g., high\nthermal decomposition temperature, constituted by earth abundant metals,\netc.). Data-driven material design guidelines also emerged from the\nscreening. For instance, a pore diameter of ∼10 Å and\na heat of adsorption of ∼90 kJ/mol were found beneficial for\nthe catalyst support use. On the other hand, for the membrane-based\nstrategy, a pore diameter of ∼2.75 Å and a heat of adsorption\nof ∼80 kJ/mol were found beneficial. The presence of V was\nfound beneficial for both uses.

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MOFs to Enhance Green\nNH<sub>3</sub> Synthesis in\nPlasma Reactors: Hierarchical Computational Screening Enhanced by\nIterative Machine Learning · Figshare (2024) | TGRS Research Map | TGRS