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Prediction of O2/N2Selectivity in Metal-Organic Frameworks via High-Throughput Computational Screening and Machine Learning

journal contribution
posted on 2024-11-02, 19:03 authored by Ibrahim Orhan, Hilal Daglar, Seda Keskin, Tu LeTu Le, Ravichandar BabaraoRavichandar Babarao
Machine learning (ML), which is becoming an increasingly popular tool in various scientific fields, also shows the potential to aid in the screening of materials for diverse applications. In this study, the computation-ready experimental (CoRE) metal-organic framework (MOF) data set for which the O2 and N2 uptakes, self-diffusivities, and Henry's constants were calculated was used to fit the ML models. The obtained models were subsequently employed to predict such properties for a hypothetical MOF (hMOF) data set and to identify structures having a high O2/N2 selectivity at room temperature. The performance of the model on known entries indicated that it would serve as a useful tool for the prediction of MOF characteristics with r2 correlations between the true and predicted values typically falling between 0.7 and 0.8. The use of different descriptor groups (geometric, atom type, and chemical) was studied; the inclusion of all descriptor groups yielded the best overall results. Only a small number of entries surpassed the performance of those in the CoRE MOF set; however, the use of ML was able to present the structure-property relationship and to identity the top performing hMOFs for O2/N2 separation based on the adsorption and diffusion selectivity.

History

Journal

ACS Applied Materials and Interfaces

Volume

14

Issue

1

Start page

736

End page

749

Total pages

14

Publisher

American Chemical Society

Place published

United States

Language

English

Copyright

© 2021 American Chemical Society

Former Identifier

2006112672

Esploro creation date

2022-03-24