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RESEARCH PAPER · Scientia Sinica Technologica · 2021

Multi-objective machine learning of four mechanical properties of steels

Qinghua Wei, Jie Xiong, Sheng Sun, Tong‐Yi Zhang

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14 direct citing papers in this saved network. Explore the next layer with second-order citations.

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Multi-objective machine learning of four mechanical properties of steelsOriginalModelling of phase stability: integrating computational materials science and materials informatics3Domain knowledge-guided interpretive machine learning: formula discovery for the oxidation behavior of ferritic-martensitic steels in supercritical water46Machine learning of the mechanical properties and data-driven design of lead-free solder alloys1Discovering a formula for the high temperature oxidation behavior of FeCrAlCoNi based high entropy alloys by domain knowledge-guided machine learning41FDPBoost: Federated differential privacy gradient boosting decision trees14An effective multitask neural networks for predicting mechanical properties of steel11Multi‐objective optimization and its application in materials science46Predictive Modeling of Tensile Strength in Aluminum Alloys via Machine Learning28Evading the strength-ductility trade-off dilemma in high-entropy alloys by multi-objective machine learning1Linear Regression1Machine learning-driven multi-objective alloy selection framework for mechanical property criteria1Multi-objective optimization in machine learning assisted materials design and discovery25Creep rupture life prediction of Ni-based superalloys via machine learning with insights into metallurgical mechanisms2Utilizing hybrid BIGRU-LIGHTGBM model for mechanical characteristics prediction in aluminium alloys0

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  1. Modelling of phase stability: integrating computational materials science and materials informatics

    Journal of Materials Informatics · 2021-01-01

    Xiaoyan Song, Kai Guo, Hao Lu, Dong Liu, Fawei Tang

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  2. Domain knowledge-guided interpretive machine learning: formula discovery for the oxidation behavior of ferritic-martensitic steels in supercritical water

    Journal of Materials Informatics · 2022-01-01

    Bin Cao, Shuang Yang, Ankang Sun, Ziqiang Dong, Tong‐Yi Zhang

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  3. Machine learning of the mechanical properties and data-driven design of lead-free solder alloys

    Scientia Sinica Technologica · 2022-08-15

    Hao Yuan, Bin Cao, Kangdong You, Ziqiang Dong, Tong‐Yi Zhang, Jubo Peng, Shanshan Cai, Xiaobin Luo, Chen LIU, JiaJun WANG

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  4. Discovering a formula for the high temperature oxidation behavior of FeCrAlCoNi based high entropy alloys by domain knowledge-guided machine learning

    Journal of Material Science and Technology · 2023-02-05

    Qinghua Wei, Bin Cao, Lucheng Deng, Ankang Sun, Ziqiang Dong, Tong‐Yi Zhang

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  5. FDPBoost: Federated differential privacy gradient boosting decision trees

    Journal of Information Security and Applications · 2023-03-22

    Yingjie Li, Yan Feng, Quan Qian

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  6. An effective multitask neural networks for predicting mechanical properties of steel

    Materials Letters · 2023-09-17

    Yunqi Ban, Jianxin Hou, Xianpeng Wang, Guodong Zhao

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  7. Multi‐objective optimization and its application in materials science

    Materials Genome Engineering Advances · 2023-11-13

    Bofeng Shi, Turab Lookman, Dezhen Xue

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  8. Predictive Modeling of Tensile Strength in Aluminum Alloys via Machine Learning

    Materials · 2023-11-20

    Keya Fu, Dexin Zhu, Yuqi Zhang, Cheng Zhang, Cheng Zhang, Xiaodong Wang, Changji Wang, Tao Jiang, Feng Mao, Cheng Zhang, Cheng Zhang, Xiaobo Meng, Hua Yu

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  9. Evading the strength-ductility trade-off dilemma in high-entropy alloys by multi-objective machine learning

    Big Data and Information Analytics · 2025-01-01

    Guanying Wei, Jesper Byggmästar, Junzhi Cui, Jingli Ren

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  10. Linear Regression

    Springer eBooks · 2025-01-01

    Tong‐Yi Zhang

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  11. Machine learning-driven multi-objective alloy selection framework for mechanical property criteria

    Procedia CIRP · 2025-01-01

    Erkan Tur, Joseph Betts, Laurent Perge, Alborz Shokrani

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  12. Multi-objective optimization in machine learning assisted materials design and discovery

    Journal of Materials Informatics · 2025-03-24

    Pengcheng Xu, Yingying Ma, Wencong Lu, Minjie Li, Wenyue Zhao, Zhilong Dai

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  13. Creep rupture life prediction of Ni-based superalloys via machine learning with insights into metallurgical mechanisms

    Materials Letters · 2025-12-12

    Qinghua Wei

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  14. Utilizing hybrid BIGRU-LIGHTGBM model for mechanical characteristics prediction in aluminium alloys

    International Journal of Material Forming · 2026-08-10

    Lakshmi Rajan P., Sujatha I., Arun M. S.

    Read original paper ↗ ·
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