Abstract
Predicting the phase precipitation of multicomponent alloys, especially the Ni-base superalloys, is a difficult task. In this work, we introduced a dependable and efficient way to establish the relationship between composition and detrimental phases in Ni-base superalloys, by integrating high throughput experiments and machine learning algorithms. 8371 sets of data about composition and phase information were obtained rapidly, and analyzed by machine learning to establish a high-confidence phase prediction model. Compared with the traditional methods, the proposed approach has remarkable advantage in acquiring and analyzing the experimental data, which can also be applied to other multicomponent alloys.
IMPACT STATEMENT
By integrating the high throughput experiments and machine learning algorithms, it is hopeful to facilitate the design of new Ni-base superalloys, and even other multicomponent alloys.
GRAPHICAL ABSTRACT
Acknowledgments
The authors acknowledge Prof. Ji-Cheng Zhao from University of Maryland and Prof. Zhanpeng Jin from Central South University for insightful discussions.
Data and materials availability
The data supporting the findings of this work is available in the main text. Raw data are available from the corresponding authors on reasonable request.
Disclosure statement
No potential conflict of interest was reported by the author(s).