Machine Learning-Enabled Prediction and High-Throughput Screening of Polymer Membranes for Pervaporation Separation

Pervaporation
(PV) is considered as a robust membrane-based separation
technology for liquid mixtures. However, the development of PV membranes
is impeded largely by the lack of adequate models capable of reliably
predicting the performance of PV membranes. In this study, we collect
an experimental data set with a total of 681 data samples including
16 polymers and 6 organic solvents for a wide variety of water/organic
mixtures under various operating conditions. Then, two types of machine
learning (ML) models are developed for prediction and high-throughput
screening of polymer membranes for PV separation. Based on the intrinsic
properties of polymer and solvent (water contact angle of polymer
and solubility parameter of solvent) as gross descriptors, the first
type accurately predicts PV separation performance (total flux and
separation factor). The second type is based on the molecular representation
of polymer and solvent, giving accuracy comparable to the first type,
and applied to screen ∼1 million hypothetical polymers for
PV separation of water/ethanol mixtures. With a threshold of 700 for
the PV separation index, 20 polymers are shortlisted, with many surpassing
experimental samples. Among these, 10 are further identified to be
synthesizable in terms of a synthetic complexity score. The ML models
developed in this study would facilitate the optimization of operating
conditions and accelerate the development of new polymer membranes
for high-performance PV separation.