Phase diagram construction and prediction method based on machine learning algorithms

被引:0
|
作者
Xi, Shengkun [1 ,2 ,3 ]
Li, Jiahui [1 ,2 ,4 ]
Bao, Longke [1 ,2 ]
Shi, Rongpei [1 ,2 ]
Zhang, Haijun [3 ]
Chong, Xiaoyu [5 ]
Li, Zhou [6 ]
Wang, Cuiping [7 ,8 ]
Liu, Xingjun [1 ,2 ,7 ,8 ,9 ]
机构
[1] Harbin Inst Technol, Sch Mat Sci & Engn, Shenzhen 518055, Peoples R China
[2] Harbin Inst Technol, Inst Mat Genome & Big Data, Shenzhen 518055, Peoples R China
[3] Harbin Inst Technol, Dept Comp Sci, Shenzhen 518055, Peoples R China
[4] Harbin Inst Technol, Sch Mech Engn & Automat, Shenzhen 518055, Peoples R China
[5] Kunming Univ Sci & Technol, Fac Mat Sci & Engn, Kunming 650093, Peoples R China
[6] Shandong First Med Univ & Shandong Acad Med Sci, Coll Med Informat & Artificial Intelligence, Jinan 250117, Peoples R China
[7] Xiamen Univ, Coll Mat, Xiamen 361005, Peoples R China
[8] Xiamen Univ, Fujian Prov Key Lab Mat Genome, Xiamen 361005, Peoples R China
[9] Harbin Inst Technol, State Key Lab Adv Welding & Joining, Shenzhen 518055, Peoples R China
来源
JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T | 2025年 / 36卷
基金
中国国家自然科学基金; 国家重点研发计划;
关键词
CALPHAD; Machine learning; Digitalized phase diagram; Phase region; Solvus temperature; ENERGY;
D O I
10.1016/j.jmrt.2025.03.065
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Phase diagram, which is known as the "compass" and "map" of materials research, plays a guiding role in the material design and development. Conventional CALPHAD method could provide the detailed information on the phase equilibria via the assessment of thermodynamics model parameters. However, CALPHAD assessment for the multi-component systems can be particularly challenging due to the significant time costs involved and lack of experimental data, especially when attempting to predict the phase diagrams of multi-component systems. The fast-growing machine learning technique opens a new pathway to deal with tons of data and parameters. Meanwhile, the CALPHAD method has accumulated abundant high-quality phase diagram data who would be the perfect training data for the machine learning algorithms. In the present work, a phase diagram prediction method which integrates machine learning algorithms with CALPHAD descriptors is proposed. The present study establishes and train machine learning models to predict phase-type and solvus temperature of the materials. Using training datasets obtained from the CALPHAD method, we combine the total Gibbs energy and magnetic descriptor with training set to predict the isothermal sections of Cu-Co-Ni and Fe-Cu-Co ternary systems. The results indicate that the elevated temperatures not only enhance the solubility of Co, Ni, and Cu in intermetallic compounds but also facilitate the formation of eutectic precipitates (gamma Fe+alpha Co). This methodology can efficiently predict the phase diagram of material system with higher number of components by training the phase diagram data of lower ones, thereby providing a new strategy to complement the CALPHAD with machine learning technique and extend the application of CALPHAD method to the advanced materials including high entropy alloys and functional materials.
引用
收藏
页码:1917 / 1929
页数:13
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