Using Active Learning to Rapidly Develop Machine Learned Diffusion Coefficients of CO2 Conversion Reagents in Metal–Organic Frameworks

Leverant, Calen J.; Cooper, John; Sava Gallis, Dorina F.; Harvey, Jacob A.

doi:10.1021/acs.jpcc.4c04660

Using Active Learning to Rapidly Develop Machine Learned Diffusion Coefficients of CO₂ Conversion Reagents in Metal–Organic Frameworks

Journal Article · Tue Sep 17 00:00:00 EDT 2024 · Journal of Physical Chemistry. C

DOI:https://doi.org/10.1021/acs.jpcc.4c04660· OSTI ID:2483451

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Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Here, we used a combined molecular dynamics/active learning (AL) approach to create machine learning models that can predict the diffusion coefficient of epichlorohydrin and chloropropene carbonate, the reactant and product of a common CO₂ cycloaddition reaction, in metal–organic frameworks (MOFs). Nanoporous MOFs are effective catalysts for the cycloaddition of CO₂ to epoxides. The diffusion rates within nanoporous catalysts can control the rate of reaction as the reactants and products must diffuse to the active sites within the MOF and then out of the nanoporous material for reusability. However, the diffusion process is routinely ignored when searching for new materials in catalytic applications. Here we verified improvement during the AL process by consistently tracking metrics on the same groups of MOFs to ensure consistency. Metal identity was found to have little impact on diffusion rates, while structural features like pore limiting diameter act as a threshold where a minimum value is needed for high diffusion rates. We identified the MOFs with the highest epichlorohydrin and chloropropene carbonate diffusion coefficients which can be used for further studies of reaction energetics.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0003525

OSTI ID:: 2483451

Report Number(s):: SAND--2024-16822J

Journal Information:: Journal of Physical Chemistry. C, Journal Name: Journal of Physical Chemistry. C Journal Issue: 38 Vol. 128; ISSN 1932-7447

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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Using Active Learning to Rapidly Develop Machine Learned Diffusion Coefficients of CO2 Conversion Reagents in Metal–Organic Frameworks

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Using Active Learning to Rapidly Develop Machine Learned Diffusion Coefficients of CO₂ Conversion Reagents in Metal–Organic Frameworks