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Li et al. J Mater Inf 2024;4:27 Journal of
DOI: 10.20517/jmi.2024.44
Materials Informatics

Research Article Open Access

An integrated design of novel RAFM steels with
targeted microstructures and tensile properties

using machine learning and CALPHAD

1,2
5
Xiaochen Li , Mingjie Zheng 1,3,* , Hao Pan 1,3,4 , Chunliang Mao , Wenyi Ding 1
1
Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, Anhui, China.
2
School of Physics and Electronic Engineering, Jining University, Qufu 273155, Shandong, China.
3
University of Science and Technology of China, Hefei 230026, Anhui, China.
4
Department of Mechanical Engineering, City University of Hong Kong, Hong Kong 999077, China.
5
College of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, Hebei, China.
* Correspondence to: Prof. Mingjie Zheng, Hefei Institutes of Physical Science, Chinese Academy of Sciences, 350 Shushanhu
Road, Hefei 230031, Anhui, China. E-mail: mingjie.zheng@inest.cas.cn
How to cite this article: Li X, Zheng M, Pan H, Mao C, Ding W. An integrated design of novel RAFM steels with targeted
microstructures and tensile properties using machine learning and CALPHAD. J Mater Inf 2024;4:27. https://dx.doi.org/10.
20517/jmi.2024.44

Received: 6 Sep 2024 First Decision: 15 Oct 2024 Revised: 20 Nov 2024 Accepted: 22 Nov 2024 Published: 30 Nov 2024

Academic Editor: Ming Hu Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang

Abstract
The design optimization of structure and performance of reduced activation ferritic-martensitic (RAFM) steels is
crucial for the development of future fusion reactors, which has always been a significant challenge. In this study,
we proposed a new strategy to integrate the microstructure and performance design of RAFM steels using machine
learning (ML) and calculation of phase diagrams (CALPHAD). Since the microstructures (MX, M C , δ-ferrite,
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coarsening phases, etc.) play important roles in mechanical properties of RAFM steels, a microstructural model was
built by ML to predict their volume fraction or presence based on the CALPHAD data. By integrating this
microstructural model with the forward and reverse models, we developed two RAFM steels with high volume
fraction of MX (0.49% and 0.42%) and excellent tensile properties. At 600 °C, the ultimate tensile strength
(UTS) of the two RAFM steels is about 100 MPa higher than that of the conventional RAFM steels. These
experimental results meet the specific design criteria, confirming the effectiveness of our design strategy. Our
research will provide a valuable guideline for the design of other advanced alloys.

Keywords: Machine learning, integrated design, RAFM steels, tensile properties, MX precipitates

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
indicate if changes were made.

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