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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Human interpretable structure-property relationships in chemistry using explainable machine learning and large

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Explainable Artificial Intelligence (XAI) now offers accessible chemical insights. The XpertAI framework uses large language models (LLMs) to translate complex data into understandable natural language explanations for structure-property relationships.

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Area of Science:

  • Artificial Intelligence
  • Chemistry
  • Data Science

Background:

  • Explainable Artificial Intelligence (XAI) methods address the opacity of machine learning models.
  • XAI is valuable for elucidating structure-property relationships in chemistry.
  • Current XAI tools often require technical expertise, limiting broader accessibility.

Purpose of the Study:

  • To develop a framework that makes XAI more accessible to chemists.
  • To integrate XAI with large language models (LLMs) for automated data interpretation.
  • To generate natural language explanations of chemical data.

Main Methods:

  • The XpertAI framework was developed, integrating XAI techniques with LLMs.
  • The framework accesses scientific literature to inform explanations.
  • Five case studies were conducted to evaluate XpertAI's performance.

Main Results:

  • XpertAI successfully generated accessible, natural language explanations of raw chemical data.
  • The framework demonstrated the ability to extract input-output relationships.
  • Case studies confirmed the generation of specific, scientific, and interpretable explanations.

Conclusions:

  • XpertAI bridges the gap between complex XAI methods and chemical data interpretation.
  • The framework leverages LLMs and XAI to enhance understanding of structure-property relationships.
  • XpertAI offers a user-friendly approach to chemical data analysis using AI.