Insights and Heuristics for Predicting Diffusion Rates of Chemical Warfare Agents in Zirconium Metal-Organic Frameworks

Brandon C. Bukowski; Randall Q. Snurr

doi:10.1021/acsami.2c17313

Insights and Heuristics for Predicting Diffusion Rates of Chemical Warfare Agents in Zirconium Metal-Organic Frameworks

Brandon C. Bukowski, Randall Q. Snurr^*

^*Corresponding author for this work

Chemical and Biological Engineering

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

Designing nanoporous catalysts to destroy chemical warfare agents (CWAs) and environmental contaminants requires consideration of both intrinsic catalytic activity and the mass transfer of molecules in and out of the pores. Polar adsorbates such as CWAs experience a heterogeneous environment in many metal-organic frameworks (MOFs) due to the arrangement of the metal nodes and organic linkers of the MOF. However, quantitative relationships between the pore architecture and the resulting diffusion properties of polar molecules have not been established. We used molecular dynamics simulations to calculate the diffusion coefficients of the CWA simulant dimethyl methyl phosphonate (DMMP) in a diverse set of 776 MOFs with Zr6 nodes. We developed a 4-parameter machine learning model to predict DMMP diffusivities in Zr6 MOFs and found the model to be transferable to the CWA sarin. We then developed a simplified heuristic based on the machine learning model that the node-node distance and accessible surface area should be maximized to find MOFs with rapid CWA diffusion.

Original language	English (US)
Pages (from-to)	55608-55615
Number of pages	8
Journal	ACS Applied Materials and Interfaces
Volume	14
Issue number	50
DOIs	https://doi.org/10.1021/acsami.2c17313
State	Published - Dec 21 2022

Funding

B.C.B. and R.Q.S. acknowledge support from the Defense Threat Reduction Agency (HDTRA1-19-1-0007). This research used resources of the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility supported by the Office of Science of the US Department of Energy (DE AC02-05CH11231). We thank Andrew Rosen for assistance in generating the 10-connected Zr node and Arun Gopalan for his assistance with using Scikit-image to find local maxima. This research was supported in part through the computational resources and staff contributions provided for the Quest high-performance computing facility at Northwestern University, which is jointly supported by the Office of the Provost, the Office for Research, and Northwestern University Information Technology.

Keywords

MOF
diffusion
machine learning
molecular dynamics
porous material

ASJC Scopus subject areas

General Materials Science

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10.1021/acsami.2c17313

Cite this

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title = "Insights and Heuristics for Predicting Diffusion Rates of Chemical Warfare Agents in Zirconium Metal-Organic Frameworks",

abstract = "Designing nanoporous catalysts to destroy chemical warfare agents (CWAs) and environmental contaminants requires consideration of both intrinsic catalytic activity and the mass transfer of molecules in and out of the pores. Polar adsorbates such as CWAs experience a heterogeneous environment in many metal-organic frameworks (MOFs) due to the arrangement of the metal nodes and organic linkers of the MOF. However, quantitative relationships between the pore architecture and the resulting diffusion properties of polar molecules have not been established. We used molecular dynamics simulations to calculate the diffusion coefficients of the CWA simulant dimethyl methyl phosphonate (DMMP) in a diverse set of 776 MOFs with Zr6 nodes. We developed a 4-parameter machine learning model to predict DMMP diffusivities in Zr6 MOFs and found the model to be transferable to the CWA sarin. We then developed a simplified heuristic based on the machine learning model that the node-node distance and accessible surface area should be maximized to find MOFs with rapid CWA diffusion.",

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AU - Snurr, Randall Q.

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Y1 - 2022/12/21

N2 - Designing nanoporous catalysts to destroy chemical warfare agents (CWAs) and environmental contaminants requires consideration of both intrinsic catalytic activity and the mass transfer of molecules in and out of the pores. Polar adsorbates such as CWAs experience a heterogeneous environment in many metal-organic frameworks (MOFs) due to the arrangement of the metal nodes and organic linkers of the MOF. However, quantitative relationships between the pore architecture and the resulting diffusion properties of polar molecules have not been established. We used molecular dynamics simulations to calculate the diffusion coefficients of the CWA simulant dimethyl methyl phosphonate (DMMP) in a diverse set of 776 MOFs with Zr6 nodes. We developed a 4-parameter machine learning model to predict DMMP diffusivities in Zr6 MOFs and found the model to be transferable to the CWA sarin. We then developed a simplified heuristic based on the machine learning model that the node-node distance and accessible surface area should be maximized to find MOFs with rapid CWA diffusion.

AB - Designing nanoporous catalysts to destroy chemical warfare agents (CWAs) and environmental contaminants requires consideration of both intrinsic catalytic activity and the mass transfer of molecules in and out of the pores. Polar adsorbates such as CWAs experience a heterogeneous environment in many metal-organic frameworks (MOFs) due to the arrangement of the metal nodes and organic linkers of the MOF. However, quantitative relationships between the pore architecture and the resulting diffusion properties of polar molecules have not been established. We used molecular dynamics simulations to calculate the diffusion coefficients of the CWA simulant dimethyl methyl phosphonate (DMMP) in a diverse set of 776 MOFs with Zr6 nodes. We developed a 4-parameter machine learning model to predict DMMP diffusivities in Zr6 MOFs and found the model to be transferable to the CWA sarin. We then developed a simplified heuristic based on the machine learning model that the node-node distance and accessible surface area should be maximized to find MOFs with rapid CWA diffusion.

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KW - machine learning

KW - molecular dynamics

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Insights and Heuristics for Predicting Diffusion Rates of Chemical Warfare Agents in Zirconium Metal-Organic Frameworks

Abstract

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Keywords

ASJC Scopus subject areas

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