AI-Assisted Exploration of Superionic Glass-Type Li+ Conductors with Aromatic Structures

Abstract

It has long remained challenging to predict the properties of complex chemical systems, such as polymer-based materials and their composites. We constructed currently the largest database of lithium conducting solid polymer electrolytes (104 entries) and employed a transfer learned, graph neural network to accurately predict their conductivity (mean absolute error of less than 1 in a logarithmic scale). The bias-free prediction by the network helped us to find out superionic conductors, composed of charge transfer complexes of aromatic polymers (ionic conductivity of around 10-3 S/cm at room temperature). The glassy design was against the traditional rubbery concept of polymer electrolytes, but found to be appropriate to achieve the fast, decoupled motion of ionic species from polymer chains, and to enhance thermal and mechanical stability. The unbiased suggestions by machine learning models are helpful for researches to discover unexpected chemical phenomena, which would also induce the paradigm shift of energy-related functional materials.