Phase prediction of Ni-base superalloys via high-throughput experiments and machine learning

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

KEYWORDS:

• Superalloy
• high-throughput experiments
• machine learning
• diffusion multiple
• phase selection

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).

Additional information

Funding

This work was supported by The National Key Research and Development Program of China [grant number 2016YFB0701404]; The Natural Science Foundation of China [grant number 91860105]; The Project founded by China Postdoctoral Science Foundation [grant number 2019M662799].