Machine-Learning-Based phase diagram construction for high-throughput batch experiments
Ryo Tamura, Guillaume Deffrennes, Kwangsik Han, Taichi Abe, Haruhiko Morito, Yasuyuki Nakamura, Masanobu Naito, Ryoji Katsube, Yoshitaro Nose, Kei Terayama
Abstract
To know phase diagrams is a time saving approach for developing novel materials. To efficiently construct phase diagrams, a machine learning technique was developed using uncertainty sampling, which is called as PDC (Phase Diagram Construction) package [K. Terayama et al. Phys. Rev. Mater. 3, 033802 (2019).]. In this method, the most uncertain point in the phase diagram was suggested as the next experimental condition. However, owing to recent progress in lab automation techniques and robotics, high-throughput batch experiments can be performed. To benefit from such a high-throughput nature, multiple conditions must be selected simultaneously to effectively construct a phase diagram using a machine learning technique. In this study, we consider some strategies to do so, and their performances were compared when exploring ternary isothermal sections (two-dimensional) and temperature-dependent ternary phase diagrams (three-dimensional). We show that even if the suggestions are explored several instead of one at a time, the performance did not change drastically. Thus, we conclude that PDC with multiple suggestions is suitable for high-throughput batch experiments and can be expected to play an active role in next-generation automated material development.