Polymer membranes perform innumerable separations with far-reaching environmentalimplications. Despite decades of research on membrane technologies, design of new membranematerials remains a largely Edisonian process. To address this shortcoming, we demonstrate ageneralizable, accurate machine-learning (ML) implementation for the discovery of innovativepolymers with ideal separation performance. Specifically, multitask ML models are trained onavailable experimental data to link polymer chemistry to gas permeabilities of He, H2, O2, N2, CO2,and CH4. We interpret the ML models and extract chemical heuristics for membrane design,through Shapley Additive exPlanations (SHAP) analysis. We then screen over nine millionhypothetical polymers through our models and identify thousands of candidates that lie well abovecurrent performance upper bounds. Notably, we discover hundreds of never-before-seenultrapermeable polymer membranes with O2 and CO2 permeability greater than 104 and 105 Barrer,respectively, orders of magnitude higher than currently available polymeric membranes. Thesehypothetical polymers are capable of overcoming undesirable trade-off relationship betweenpermeability and selectivity, thus significantly expanding the currently limited library of polymermembranes for highly efficient gas separations. High-fidelity molecular dynamics simulationsconfirm the ML-predicted gas permeabilities of the promising candidates, which suggests thatmany can be translated to reality. Formula of 878167-55-6 Price of Sodium methanesulfinate PMID:24463635
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