The article discusses the development of AIPHAD, a web application designed to aid in the visualization and understanding of phase diagrams through active learning. AIPHAD uses the Phase Diagram Construction (PDC) algorithm, which applies machine learning techniques to efficiently map phase diagrams by identifying the most informative experiments. The application supports various types of phase diagrams, such as two-variable and ternary phase diagrams. The effectiveness of AIPHAD is demonstrated through its application in studying the Fe-Ti-Sn ternary system, showcasing its potential in materials exploration and discovery.
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