Database of Nonaqueous Proton-Conducting Materials

This work presents the assembly of 48 papers, representing 74 different compounds and blends, into a machine-readable database of nonaqueous proton-conducting materials. SMILES was used to encode the chemical structures of the molecules, and we tabulated the reported proton conductivity, proton diffusion coefficient, and material composition for a total of 3152 data points. The data spans a broad range of temperatures ranging from -70 to 260 °C. To explore this landscape of nonaqueous proton conductors, DFT was used to calculate the proton affinity of 18 unique proton carriers. The results were then compared to the activation energy derived from fitting experimental data to the Arrhenius equation. It was found that while the widely recognized positive correlation between the activation energy and proton affinity may hold among closely related molecules, this correlation does not necessarily apply across a broader range of molecules. This work serves as an example of the potential analyses that can be conducted using literature data combined with emerging research tools in computation and data science to address specific materials design problems.

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Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2025-03-09
DOI
https://doi.org/10.1021/acsami.4c22618
Citations
1
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.52

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article

Database of Nonaqueous Proton-Conducting Materials

Harrison J. Cassady, Debjyoti Bhattacharya, Wesley F. Reinhart, E. H. Martin et al.
1 citations
ACS Applied Materials & Interfaces
Machine Learning in Materials Science
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article

Database of Nonaqueous Proton-Conducting Materials

Harrison J. Cassady, Debjyoti Bhattacharya, Wesley F. Reinhart, E. H. Martin, Valentino R. Cooper, Maria F. Rochow, Michael A. Hickner, Yifan Liu, Brock A. Dyer
article en
1 citations

Abstract

This work presents the assembly of 48 papers, representing 74 different compounds and blends, into a machine-readable database of nonaqueous proton-conducting materials. SMILES was used to encode the chemical structures of the molecules, and we tabulated the reported proton conductivity, proton diffusion coefficient, and material composition for a total of 3152 data points. The data spans a broad range of temperatures ranging from -70 to 260 °C. To explore this landscape of nonaqueous proton conductors, DFT was used to calculate the proton affinity of 18 unique proton carriers. The results were then compared to the activation energy derived from fitting experimental data to the Arrhenius equation. It was found that while the widely recognized positive correlation between the activation energy and proton affinity may hold among closely related molecules, this correlation does not necessarily apply across a broader range of molecules. This work serves as an example of the potential analyses that can be conducted using literature data combined with emerging research tools in computation and data science to address specific materials design problems.

ACS Applied Materials & InterfacesVol. 17(11)
Oak Ridge National Laboratory (US), Pennsylvania State University (US), Lawrence Berkeley National Laboratory (US), Ursinus College (US), University of Michigan (US), Energy Technologies Institute (GB), Michigan State University (US)
National Science Foundation, U.S. Department of Energy, National Energy Research Scientific Computing Center, Office of Science, Basic Energy Sciences, Energy Frontier Research Centers
Openalex Percentile: Top 41%
Machine Learning in Materials Science
0.52
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