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RESEARCH PAPER · npj Computational Materials · 2024

Transfer learning enables the rapid design of single crystal superalloys with superior creep resistances at ultrahigh temperature

Fan Yang, Wenyue Zhao, Yi Ru, Siyuan Lin, Jiapeng Huang, Boxuan Du, Yanling Pei, Shusuo Li, Shengkai Gong, Huibin Xu

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Transfer learning enables the rapid design of single crystal superalloys with superior creep resistances at ultrahigh temperatureOriginalTransfer Learning Across Materials Classes: Conceptual Boundaries of Reusability0Transfer Learning in Computational Materials Engineering: Techniques and Case Studies0A Review on the Application of Superalloys Composition, Microstructure, Processing, and Performance via Machine Learning18A Multi-Fidelity Learning Framework for High-Entropy Alloy Design with Sparse DFT and Abundant CALPHAD Data0Improving oxidation resistance property of single-crystal Ni-based superalloy via utilizing high temperature aging-induced dislocations11Machine Learning-Based Computational Design Methods for High-Entropy Alloys49High-throughput design strategy for creep-resistance steel using machine learning and optimization algorithm0Multi-attribute-physical-law-informed transfer learning to design high-entropy Elinvar alloys4Atomic-scale insights into the frictional wear behaviour of nickel-based single-crystal high-temperature alloys in the γ/Laves phase1Negative mixing enthalpy and mixing enthalpy alloying leads to interface and size effects towards superb creep resistance of nickel-based single crystalline superalloys9Multi-mode fatigue life prediction using machine learning inspired by damage physics63Nature-to-microstructure network: A cross-domain knowledge transfer framework for steel microstructure classification4Graph neural networks enable design of high-performance aluminum alloys for laser powder bed fusion2The strengthening effect of Ta-W/Mo mediated by their partitioning behaviors in Ni-based single-crystal superalloys: Insights from first-principles calculations2Creep life prediction of 2.25Cr1Mo0.25V steel using cross-material transfer learning, automatic hyperparameter optimization, and Bagging ensemble5Study on Deep Learning Model for Creep Life Evaluation of T91 Steel at High Temperature0Transfer learning-enabled discovery of MXenes as efficient anchoring materials for Li–S batteries2A comprehensive review of machine learning for superalloys: from data-driven prediction to intelligent design2Data-augmented machine learning design and performance-enhancing quaternary synergistic mechanism of novel Cu-Be alloy7Knowledge-enabled data-driven smart design of ultra-strong ductile near-α titanium alloys under extreme conditions0A novel transfer learning framework for predicting the elevated-temperature fatigue properties of aero-bearing steels0Data-driven design: Interpretive optimization of strength-ductility synergy in as-cast Mg-Gd-Y-Zn-Zr alloys through correlation-guided approaches and data augmentation2Microstructural stability enhancement in Pt-modified 718Plus superalloy through coarsening and morphology control of γ′ precipitates2An oxidation-microstructure-mediated model for predicting thin-walled creep behavior in Ni-based single-crystal superalloys0Interpretable machine learning-assisted physical modeling of oxidation kinetics in non-oxide ceramics0A Transfer-Learning and Continuous Optimization-Based Framework for Predicting Heat Treatment-Dependent Mechanical Properties of DED-Processed Low-Alloy Steels0Domain-aware joint learning strategy for melting point prediction of high-entropy carbides under extreme data scarcity0

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  1. Transfer Learning Across Materials Classes: Conceptual Boundaries of Reusability

    Journal of Computational and Data-Driven Materials Engineering · 2024-01-01

    Lucas Meyer, Stefan Braun, Anna Schmid

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  2. Transfer Learning in Computational Materials Engineering: Techniques and Case Studies

    Journal of Computational and Data-Driven Materials Engineering · 2024-01-01

    Daniel Fischer, Laura Meier, Thomas Braun, Stefan Koch

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  3. A Review on the Application of Superalloys Composition, Microstructure, Processing, and Performance via Machine Learning

    JOM · 2024-10-10

    Junhui Zhang, Haiyan Gao, Yahui Liu, Jun Wang

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  4. A Multi-Fidelity Learning Framework for High-Entropy Alloy Design with Sparse DFT and Abundant CALPHAD Data

    Journal of Computational and Data-Driven Materials Engineering · 2025-01-01

    Elena Petrova, Ivan Georgiev, Nikolay Stoyanov, Petar Kolev

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  5. Improving oxidation resistance property of single-crystal Ni-based superalloy via utilizing high temperature aging-induced dislocations

    Journal of Materials Research and Technology · 2025-01-10

    Boxuan Du, Yi Ru, Jiao Wen, Xuetao Li, Yang Cao, Yunpeng Hu, Fan Yang, Yanling Pei, Shusuo Li, Shengkai Gong

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  6. Machine Learning-Based Computational Design Methods for High-Entropy Alloys

    High Entropy Alloys & Materials · 2025-03-01

    Yan Ming Zhao, J. Y. Zhang, Peter Kaehuei Liaw, Tao Yang

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  7. High-throughput design strategy for creep-resistance steel using machine learning and optimization algorithm

    Materials Today Communications · 2025-04-09

    Chengbo Pan, Chenchong Wang, Yuqi Zhang, Xiaolu Wei, Wei Xu

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  8. Multi-attribute-physical-law-informed transfer learning to design high-entropy Elinvar alloys

    Materials Science and Engineering A · 2025-04-29

    Lei Nie, Baobin Xie, Haiyu Wan, Bin Liu, Qihong Fang, Peter Kaehuei Liaw, Jia Li

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  9. Atomic-scale insights into the frictional wear behaviour of nickel-based single-crystal high-temperature alloys in the γ/Laves phase

    Molecular Simulation · 2025-06-04

    Zongxiao Zhu, Jing Li, Min Zheng, Qiang Lu, Weihua Chen, Xingchun Wei, Bo Song

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  10. Negative mixing enthalpy and mixing enthalpy alloying leads to interface and size effects towards superb creep resistance of nickel-based single crystalline superalloys

    National Science Review · 2025-06-06

    Junbo Zhao, Xiaoyi Yuan, Yunsong Zhao, Zhanxin Wang, Haibo Long, Shengcheng Mao, Lihua Wang, Xiaodong Han, Ze Zhang

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  11. Multi-mode fatigue life prediction using machine learning inspired by damage physics

    International Journal of Mechanical Sciences · 2025-08-19

    W.Q. Hao, Duoqi Shi, Xiaoguang Yang, N.N. Li, S. Su, Yongquan Fan

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  12. Nature-to-microstructure network: A cross-domain knowledge transfer framework for steel microstructure classification

    Scripta Materialia · 2025-08-25

    Kang Xu, Huihui Yang, ZhengMing Sun, Wenwang Wu

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  13. Graph neural networks enable design of high-performance aluminum alloys for laser powder bed fusion

    Virtual and Physical Prototyping · 2025-08-28

    Jiabo Fu, Xingming Yang, Hongyuan Zhang, Xinni Tian, Hao Yu, Chenchong Wang, Wei Xu

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  14. The strengthening effect of Ta-W/Mo mediated by their partitioning behaviors in Ni-based single-crystal superalloys: Insights from first-principles calculations

    Journal of Alloys and Compounds · 2025-11-26

    Shichao Du, Siyuan Lin, Yifeng Xing, Wenyue Zhao, Yi Ru, Bin Gan, Yanling Pei, Shusuo Li, Shengkai Gong, Huibin Xu

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  15. Creep life prediction of 2.25Cr1Mo0.25V steel using cross-material transfer learning, automatic hyperparameter optimization, and Bagging ensemble

    International Journal of Pressure Vessels and Piping · 2025-12-26

    You Li, Bieerlan Jianayihan, Zhenyu Wang, Peng Jiao, Chaoxu Guan, Hao Huang

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  16. Study on Deep Learning Model for Creep Life Evaluation of T91 Steel at High Temperature

    Lecture notes in networks and systems · 2026-01-01

    Daochen Luan, J. Zhu, Decai Zhang, Weidong Li, Rongrong Wei

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  17. Transfer learning-enabled discovery of MXenes as efficient anchoring materials for Li–S batteries

    Nanoscale · 2026-01-01

    Jianqiang Wang, Haiyuan Chen, Yin Hu, Xiaobin Niu

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  18. A comprehensive review of machine learning for superalloys: from data-driven prediction to intelligent design

    Progress in Natural Science Materials International · 2026-02-01

    Linlin Sun, Jie Xiong, Qingshuang Ma, Chenghao Pei, Huijun Li, Qiuzhi Gao

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  19. Data-augmented machine learning design and performance-enhancing quaternary synergistic mechanism of novel Cu-Be alloy

    npj Computational Materials · 2026-02-18

    Wei Chen, Haoyuan Zheng, Yanbin Jiang, Fei Tan, Meng Wang, Qian Lei, Xiaoyu Jiang, Zixuan Zhao, RONGJIA YU, Yiwei Qin, Zhou Li

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  20. Knowledge-enabled data-driven smart design of ultra-strong ductile near-α titanium alloys under extreme conditions

    Journal of Materials Informatics · 2026-04-07

    Shuo Liu, Xiaoqian Fan, Hongjian Ye, Ibragim Makhambet, Haifeng Song, Xingyu Gao, Gulmira Sharipovna Yar-Mukhamedova, Daniel M. Zellele, Peixuan Li, William Yi Wang, Jinshan Li

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  21. A novel transfer learning framework for predicting the elevated-temperature fatigue properties of aero-bearing steels

    Materials & Design · 2026-04-25

    Guolei Liu, Kai Chu, Zhenzhen Chen, Hongwei Liu, Yanfei Cao, Dianzhong Li

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  22. Data-driven design: Interpretive optimization of strength-ductility synergy in as-cast Mg-Gd-Y-Zn-Zr alloys through correlation-guided approaches and data augmentation

    Materials Science and Engineering A · 2026-04-27

    Shijie Wang, X D Zhang, Jiaqi Li, Zhi Wu, Yuchao Zhou, Lin Xue

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  23. Microstructural stability enhancement in Pt-modified 718Plus superalloy through coarsening and morphology control of γ′ precipitates

    Journal of Materials Science · 2026-04-29

    Hongliang Liu, Guowei Wang, Lan Liu, Yu Shen, MinQing Wang, Boning Zhang, Lei Zheng

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  24. An oxidation-microstructure-mediated model for predicting thin-walled creep behavior in Ni-based single-crystal superalloys

    Materials Science and Engineering A · 2026-07-07

    Fan Yang, Wenyue Zhao, Jianxin Hou, Shuangqi Zhang, Haibo Wang, Boxuan Du, Pengkun Zhang, Lilun Geng, Huadong Fu, Yi Ru, Yanling Pei, Shusuo Li, Shengkai Gong, HJ Xu

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  25. Interpretable machine learning-assisted physical modeling of oxidation kinetics in non-oxide ceramics

    Ceramics International · 2026-07-28

    Yilei He, Yuan Gao, Chunyu Guo, Lu Mao, Xinran Zhou, Enhui Wang, Xinmei Hou

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  26. A Transfer-Learning and Continuous Optimization-Based Framework for Predicting Heat Treatment-Dependent Mechanical Properties of DED-Processed Low-Alloy Steels

    Metals · 2026-08-10

    Atiqur Rahman, Sung-Heng Wu, Ranjit Joy, Frank Liou

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  27. Domain-aware joint learning strategy for melting point prediction of high-entropy carbides under extreme data scarcity

    Computational and Theoretical Chemistry · 2026-09-01

    Bin Pan, Yunhui Zhang, Kang Xu, Xiaorong Wang

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