jsunn-y/PolymerGasMembraneML
A machine-learning implementation that learns generalizable, interpretable models connecting polymer chemistry to membrane gas permeability.
This project helps polymer scientists and chemical engineers discover new polymer materials for gas separation membranes. It takes in chemical structures (SMILES strings) and, optionally, known gas permeabilities of polymers. It then predicts the gas permeability of new polymers and identifies promising candidates that surpass current performance limits, aiding in the development of more efficient gas separation technologies.
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Use this if you are a materials scientist or chemical engineer looking to computationally screen a large number of hypothetical polymers to identify candidates with high gas permeability for membrane applications.
Not ideal if you are primarily interested in the experimental synthesis and characterization of polymers without a focus on large-scale computational screening or interpretable machine learning models.
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Language
Python
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Last pushed
Jun 01, 2025
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