Extracting human interpretable structure-property relationships in chemistry using XAI and large language models

TL;DR

This paper introduces XpertAI, a framework combining XAI and large language models to automatically generate accessible, scientific explanations of chemical data, enhancing interpretability in chemistry.

Contribution

The paper presents XpertAI, a novel framework that integrates XAI with LLMs to produce human-interpretable explanations of chemical structure-property relationships.

Findings

XpertAI effectively generates specific scientific explanations.

The framework successfully combines LLMs and XAI tools.

Evaluation through 5 case studies demonstrates its performance.

Abstract

Explainable Artificial Intelligence (XAI) is an emerging field in AI that aims to address the opaque nature of machine learning models. Furthermore, it has been shown that XAI can be used to extract input-output relationships, making them a useful tool in chemistry to understand structure-property relationships. However, one of the main limitations of XAI methods is that they are developed for technically oriented users. We propose the XpertAI framework that integrates XAI methods with large language models (LLMs) accessing scientific literature to generate accessible natural language explanations of raw chemical data automatically. We conducted 5 case studies to evaluate the performance of XpertAI. Our results show that XpertAI combines the strengths of LLMs and XAI tools in generating specific, scientific, and interpretable explanations.