Interpretable Machine Learning Applications: A Promising Prospect of AI for Materials

Abstract In recent years, data‐driven machine learning has significantly advanced the design of new materials and transformed the research and development landscape. However, its heavy reliance on data and the “black‐box” nature of its model‐mapping mechanisms have hindered its application in materials science research. Integrating material knowledge with machine learning to enhance model generalization and prediction accuracy remains an important objective. Such integration can deepen the understanding of material mechanisms by screening physical and chemical features to uncover explicit intrinsic relationships. Thus, it promotes the advancement of materials science, representing a promising avenue for…

• NN
  National Natural Science Foundation of China

  Awards: 92270001, 52425409, 52090041