Data from: Machine learning-based discovery of molecular descriptors that control polymer gas permeation

While machine learning has found increasing use in predicting the
properties of polymeric materials with only a knowledge of chain
architecture, determining the molecular factors underpinning properties
("interpretable AI") has remained less well explored. We show
that encoding chain chemistry in commonly employed formats, e.g.,
binary-valued fingerprints, leads to uniqueness issues during the hashing
process to save storage space. This is because the hashing algorithm can
map several chemical moieties into the same bit. These issues carry over
into the ML algorithms, especially for “inverse” design and interpretable
AI, and cannot be avoided by changing the length of the fingerprint. Using
MACCS key featurizations of monomer repeats resolves some of these issues,
and we show that a few substructures consistently appear in top features
for maximizing permeability across several gases and ML models. These are
carbon-carbon double bonds (as in polyacetylenes) especially when they are
associated with methyl groups (found in branching architectures). These
results, derived from the limited data set of ~500 polymers with
experimental gas permeation data, are in agreement with physical insight
and thus provide a robust foundation which could further enable study of
these material classes through detailed experiments and simulations.