Machine Learning Solvation Environments in Conductive Polymers: Application to ProDOT-2Hex with Solvent Swelling

Automated identification and classification of ion-solvation sites in diverse chemical systems will improve the understanding and design of polymer electrolytes for battery applications. We introduce a machine learning approach to classify and characterize ion-solvation environments based on feature vectors extracted from all-atom simulations. This approach is demonstrated in poly(3,4-propylenedioxythiophene), which is a promising candidate polymer binder for Li-ion batteries. In the dry polymer, four distinct Li+ solvation environments are identified close to the backbone of the polymer. Upon swelling of the polymer with propylene carbonate solvent, the nature of Li+ solvation changes dramatically, featuring a rapid diversification of solvation environments. This application of machine learning can be generalized to other polymer condensed-phase systems to elucidate the molecular mechanisms underlying ion solvation.

Automated summary

Automated identification and classification of ion-solvation sites using machine learning approach based on feature vectors extracted from all-atom simulations have been demonstrated in poly(3,4-propylenedioxythiophene), revealing significant changes in solvation environments and paving the way for understanding molecular mechanisms underlying ion solvation in polymer condensed-phase systems.