Orchestrating explainable AI, ChatGPT, and human expertise: a framework for extracting polymer design guidelines

Abstract

To accelerate the rational design of high-performance functional polymers, such as anion exchange membranes (AEMs), the establishment of chemically meaningful and actionable design guidelines is essential. Machine learning (ML) models, particularly artificial neural networks (ANNs), offer high predictive accuracy for such materials but suffer from limited interpretability due to their black-box nature. Although explainable artificial intelligence (XAI) methods such as SHapley Additive exPlanations (SHAP) provide a unified framework for model explanation, their application to ANN models is hindered by the expensive computation cost associated with the high dimensionality of molecular descriptors commonly used to represent polymer structures. In this study, a framework that combines statistical (minimum redundancy maximum relevance) and explainable ANN-based (permutation importance via ELI5) feature selection was developed, reducing the input space to 67 key descriptors. This dimensionality reduction enabled computationally feasible SHAP analysis while enhancing the predictive accuracy of the ANN by 40.87%. However, the resulting key descriptors were often difficult to interpret in physicochemical terms. To address this, large language models (LLMs) such as ChatGPT were employed to analyze descriptor source code and assist human experts in deriving chemically intuitive insights. By orchestrating XAI, LLM assistance, and expert knowledge, the framework successfully extracted design guidelines for AEMs. Based on these insights, two candidate AEM polymers with predicted anion conductivities ≥0.1 S cm⁻¹ at 80 °C were proposed, exceeding typical commercialization thresholds. This study illustrates a generalizable, explainable, and efficient pathway for integrating ML, XAI, and LLMs in polymer informatics, with broad applicability across descriptor-based materials research.