Millions of polymers were screened using machine learning (ML) models
- Gaussian process regression (GPR)-based ML models were developed to predict permeability and selectivity using an in-house dataset containing experimental values
- A novel approach was developed to construct large polymer datasets
- ~15 million polymers were screened using ML models
- About 3,500 polymers of interest were identified for CO2/CH4 and CO2/N2 gas separation
- ML models helped identify high-performance polymers for gas separation with the potential of transforming the field
