AIPHAD, an active learning web application for visual understanding of phase diagrams

Ryo Tamura; Haruhiko Morito; Guillaume Deffrennes; Masanobu Naito; Yoshitaro Nose; Taichi Abe; Kei Terayama

doi:10.1038/s43246-024-00580-7

論文 AIPHAD, an active learning web application for visual understanding of phase diagrams

Ryo Tamura (National Institute for Materials Science) ; Haruhiko Morito ; Guillaume Deffrennes (National Institute for Materials Science) ; Masanobu Naito (National Institute for Materials Science) ; Yoshitaro Nose ; Taichi Abe (National Institute for Materials Science) ; Kei Terayama

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Ryo Tamura, Haruhiko Morito, Guillaume Deffrennes, Masanobu Naito, Yoshitaro Nose, Taichi Abe, Kei Terayama. AIPHAD, an active learning web application for visual understanding of phase diagrams. Communications Materials. 2024, 5 (1), 139. https://doi.org/10.1038/s43246-024-00580-7

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(abstract)

Phase diagrams provide considerable information that is vital for materials exploration. However, the determination of multidimensional phase diagrams typically requires a significant investment of time, cost, and human resources owing to the necessity of numerous experiments or simulations. Machine learning and artificial intelligence techniques present a viable solution for expediting phase diagrams investigations. Additionally, effective visualization is critical for understanding phase diagrams. This study reports the development of AIPHAD (Artificial Intelligence technique for PHAse Diagram), an open-source web application to assist in the investigation and visual understanding of phase diagrams using active learning. AIPHAD employs PDC (Phase Diagram Construction) algorithm, which operates on the principle of uncertainty sampling in active learning. The AIPHAD application facilitates the examination of five diagram types: two-variable diagrams, three-variable diagrams, ternary sections, ternary phase diagrams, and quaternary sections. The efficacy of the application is demonstrated in the study of the Fe-Ti-Sn ternary system, where it efficiently identified the presence of the Heusler phase. The integration of machine learning tools with traditional materials science approaches showcased in this study has the potential to drive groundbreaking advancements in materials exploration and discovery.

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