Molecular dynamics and machine learning assisted design of conjugated polymers for improved ionic conductivity

Ionic transport in conjugated polymers is an area of increasing interest for applications involving such devices as sensors, batteries, and electronic ion pumps. In a previous work we used computational models to investigate the effect on ionic conductivity of specific chemistries of side chains attached to a polythiophene (PT) backbone, and made predictions that have been confirmed by experiments. Here we extend our materials design approach such that a chemical group “library” is considered from which polymers can be constructed. We employ two differing machine learning approaches to converge on a general set of high performing chemistries: a genetic algorithm and a neural network. We only consider PT-like molecules and take advantage of the fact that the crystalline arrangements simplify the task of microstructure modeling/prediction. We find some general trends for designing materials with improved ionic conductivity; namely, biasing the ion solvating groups towards the chain ends while still retaining good percolation of the solvation sites. Our ongoing efforts are focused on extending this scheme for optimization ionic conductivity to molecules with more complex morphologies such as Bolaamphiphiles.