Utilization of Explainable AI for Accelerating Functional Polymer Materials Development Cycle

Abstract

Functional polymers are essential materials supporting modern society and are being actively researched in an experiment-centric manner. Use of artificial intelligence (AI) or machine learning (ML) are expected to further accelerate research efficiency, but low transparency and interpretability of AI deter researchers from trusting it. This study built an explainable AI (XAI) to predict property of anion exchange membrane, a kind of functional polymer. This study is conducted in four steps: 1. Construction of an in-house database (DB); 2. Digitization of polymer structure using existing descriptors; 3. Construction of an ML model; 4. Calculate and analyze the Shapley (SHAP) values for each explanatory variable for evaluating explainability and transparency. Open DB is not available for the target material in this study, hence an in-house DB consisting structural property data of around 300 polymers was built. From 2. and 3., we built an ML model with test data prediction accuracy of 0.7983. Carrying out 4., we found that AMID_N, a descriptor-origin explanatory variable highly correlating polymer substructure and its property, is important. The findings of such important features strongly support chemical interpretation, thereby successfully obtaining a XAI that can be used in experiment cycle.