Theoretical and Computational Chemistry

Imputation of Missing Gas Permeability Data for Polymer Membranes using Machine Learning

Abstract

Polymer-based membranes can be used for energy efficient gas separations. Successful exploitation of new materials requires accurate knowledge of the transport properties of all gases of interest. An open source database of such data is of significant benefit to the research community. The Membrane Society of Australasia (https://membrane-australasia.org/) hosts a database for experimentally measured and reported polymer gas permeabilities. However, the database is incomplete, limiting its potential use as a research tool. Here, missing values in the database were filled using machine learning (ML). The ML model was validated against gas permeability measurements that were not recorded in the database. Through imputing the missing data, it is possible to re-analyse historical polymers and look for potential “missed” candidates with promising gas selectivity. In addition, for systems with limited experimental data, ML using sparse features was performed, and we suggest that once the permeability of CO2 and/or O2 for a polymer has been measured, most other gas permeabilities and selectivities, including those for CO2/CH4 and CO2/N2, can be quantitatively estimated. This early insight into the gas permeability of a new system can be used at an initial stage of experimental measurements to rapidly identify polymer membranes worth further investigation.

Content

Thumbnail image of Imputing_PolymerMembranes_MAIN.pdf

Supplementary material

Thumbnail image of Imputing_PolymerMembranes_SI.pdf
Imputing PolymerMembranes SI
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TOC
Thumbnail image of Imputed_database_BLR_standard_deviation.txt
Imputed database BLR standard deviation
Thumbnail image of Imputed_database_BLR_ERT.txt
Imputed database BLR ERT