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DOE OSTI · 2452779

Machine learning-guided discovery of polymer membranes for CO 2 separation with genetic algorithm

Basdogan, Yasemin·Pollard, Dylan R.·Shastry, Tejus·Carbone, Matthew R.·Kumar, Sanat K.·Wang, Zhen-Gang
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Abstract

Designing polymer membranes with high gas permeability and selectivity is a difficult multi-task constrained problem due to the trade-off between these two properties. In this work, we present a machine learning (ML) driven genetic algorithm to tackle the design problem of polymer membranes for CO 2 separation from N 2 and O 2 . Using literature data of permeability for three gases, we constructed multiple ML models with different fingerprinting featurization schemes to predict gas permeabilities. Then, we employed a genetic algorithm to design new polymers and evaluated their performance using our ML models. We were able to identify new polymer membranes that are promising for both CO 2 /N 2 and CO 2 /O 2 separations. Further, the top discovered polymers are predicted to have high glass transition temperatures. Similarly, the pyridine functionality was found in ≈20% of the predicted polymers. This framework can be used to design polymers for any application involving constrained optimization. Finally, we outlined the challenges and opportunities with using ML guided data-driven inverse design of polymers.

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRYGas separationGenetic algorithmInverse designMachine learningMembranesPolymers

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Basdogan, Yasemin, Pollard, Dylan R., Shastry, Tejus, Carbone, Matthew R., Kumar, Sanat K., Wang, Zhen-Gang. 2024-08-07. Machine learning-guided discovery of polymer membranes for CO 2 separation with genetic algorithm. https://doi.org/10.1016/j.memsci.2024.123169

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