Extracting human interpretable structure-property relationships in
  chemistry using XAI and large language models
Geemi P. Wellawatte Philippe Schwaller
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.