Explainable Artificial Intelligence (XAI) for Material Design and Engineering Applications: A Quantitative Computational Framework

ABSTRACT The advancement of artificial intelligence (AI) in material design and engineering has led to significant improvements in predictive modeling of material properties. However, the lack of interpretability in machine learning (ML)‐based material informatics presents a major barrier to its practical adoption. This study proposes a novel quantitative computational framework that integrates ML models with explainable artificial intelligence (XAI) techniques to enhance both predictive accuracy and interpretability in material property prediction. The framework systematically incorporates a structured pipeline, including data processing, feature selection, model training, performance evaluation,…

• JG
  J. Gustaf Richert Stiftelse
• SF
  Svenska Forskningsrådet Formas

  Awards: 2018-05973, 2022-06725
• V
  Vetenskapsrådet

  Awards: 2018-05973, 05973, 2018-, 2022-06725
• E
  Energimyndigheten
• NF
  Norges Forskningsråd
• KS
  Kungl. Skogs- och Lantbruksakademien