Designing exceptional gas-separation polymer membranes using machine learning
Designing advanced membrane materials with machine-assisted learning. The field of polymer membrane design is primarily based on empirical observation, which limits discovery of new materials optimized for separating a given gas pair. Instead of relying on exhaustive experimental investigations, we trained a machine learning (ML) algorithm, using a topological, path-based hash of the polymer repeating unit. We used a limited set of experimental gas permeability data for six different gases in ~700 polymeric constructs that have been measured to date to predict the gas-separation behavior of over 11,000 homopolymers not previously tested for these properties. To test the algorithm’s accuracy, we synthesized two of the most promising polymer membranes predicted by this approach and found that they exceeded the upper bound for CO2/CH4 separation performance. This ML technique, which is trained using a relatively small body of experimental data (and no simulation data), evidently represents an innovative means of exploring the vast phase space available for polymer membrane design.
This work trained a machine learning (ML) algorithm, using a topological, path-based hash of the polymer repeating unit, to predict the gas-separation behavior of over 11,000 homopolymers not previously tested for these properties.
@article{Barnett_2020,
doi = {10.1126/sciadv.aaz4301},
url = {https://doi.org/10.1126%2Fsciadv.aaz4301},
year = 2020,
month = {may},
publisher = {American Association for the Advancement of Science ({AAAS})},
volume = {6},
number = {20},
author = {J. Wesley Barnett and Connor R. Bilchak and Yiwen Wang and Brian C. Benicewicz and Laura A. Murdock and Tristan Bereau and Sanat K. Kumar},
title = {Designing exceptional gas-separation polymer membranes using machine learning},
journal = {Science Advances}
}