Polymer design with enhanced crystallization tendency aided by machine learning

Designing the materials with desirable properties is very difficult task. Experimental approaches are expensive and time consuming. Machine learning (ML) guided screening is better option. In present study, different machine learning models are tried for the prediction of crystallization tendency of polymers. Hist gradient booting model is the best one. A large database of polymers is mined and easily synthesizable polymers are selected. Their crystallization tendency is predicted using fast ML model. A selected portion is analyzed using t-distributed stochastic neighbor embedding (t-SNE) method. Change in crystallization tendency on structural change is studied using structure Activity Landscape Index (SALI) analysis. On the selected polymers, clustering analysis is also performed to explore the structurally closely associated polymers with higher crystallization tendency. The synthetic accessibility assessment is also done for selected polymers. Our proposed approach is very useful for virtual screening of efficient materials.