Imputation of missing gas permeability data for polymer membranes using machine learning

Qi Yuan, Mariagiulia Longo, Aaron W. Thornton, Neil B. McKeown, Bibiana Comesaña-Gándara, Johannes C. Jansen, Kim E. Jelfs*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Polymer-based membranes have the potential for use in energy efficient gas separations. The successful exploitation of new materials requires accurate knowledge of the transport properties of all gases of interest. Open-source databases of gas permeabilities are of significant potential 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 imputed (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.

Original languageEnglish
Article number119207
Number of pages10
JournalJournal of Membrane Science
Volume627
Early online date3 Mar 2021
DOIs
Publication statusPublished - 1 Jun 2021

Keywords / Materials (for Non-textual outputs)

  • Database imputation
  • Gas separation membranes
  • Machine learning
  • Polyimides
  • Polymers of intrinsic microporosity (PIMs)

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