MatSci-ML Studio: an interactive workflow toolkit for automated machine learning in materials science

Machine learning (ML) has become a cornerstone of modern materials science, offering powerful tools for predicting material properties and accelerating experimental workflows. However, its widespread adoption is often hindered by the steep learning curve associated with programming languages like Python, which presents a significant technical barrier for many domain experts. To address this challenge, we introduce MatSci-ML Studio: an interactive and user-friendly software toolkit designed to empower materials scientists with limited coding expertise. In contrast to traditional code-based frameworks, MatSci-ML Studio features an intuitive Graphical User Interface (GUI) that encapsulates a comprehensive, end-to-end ML workflow. This integrated platform seamlessly guides users through data management, advanced preprocessing, multi-strategy feature selection, automated hyperparameter optimization, and model training, democratizing advanced computational analysis for the materials community. Notably, it incorporates advanced capabilities such as a SHAP-based interpretability analysis module for explaining model predictions and a multi-objective optimization engine for exploring complex design spaces. The practicality and effectiveness of MatSci-ML Studio are demonstrated through representative case studies, confirming its capacity to lower the technical barrier for ML application, foster innovation, and significantly enhance the efficiency of data-driven materials science.