材料研究中的可解释机器学习

With the rapid advancement of artificial intelligence (AI), machine learning is playing an increasingly important role in materials research, development, and design. Traditional machine learning models are often“black box”models that limit researchers’ understanding of a model’s decision-making and undermines their confidence in the process. Explainable machine learning (XML) can reveal the internal mechanisms of these models and provide insights into their decision-making processes. This study begins with the fundamentals of XML, outlines the development history and notable milestones of XML methods, and discusses the role of XML in AI, emphasizing the Fairness, Accountability, Simplicity, and Transparency (F.A.S.T.) principles that should be followed. Furthermore, this study introduces two major categories of XML methods—those that use model-intrinsic interpretability and those that use external model interpretability—along with their applications in materials science. Specifically, the symbolic regression of XML and visualized XML methods developed by our team offer new tools for materials research and design. Finally, potential directions for XML in the field of materials science are discussed.