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RESEARCH PAPER · Materials & Design · 2025

Target-driven design of high strength yet corrosion resistant medium Mn steel via interpretable machine-learning

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

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Target-driven design of high strength yet corrosion resistant medium Mn steel via interpretable machine-learningOriginalPrediction of Corrosion Potential for Stainless Steel and Investigation of Rare Earth Element Influence Mechanism via Machine Learning0A review of radiation effects and tribological behavior in multi-principal element alloys: towards extreme environment applications5Machine learning applications in metallic materials: Recent advances and future perspectives8Accelerated alloy design through a fully integrated High-Throughput computational Framework: Case study on medium manganese steels0Multi-fidelity physics-guided graph neural networks for predicting structure–property relationships in polycrystalline metals1Critical role of microstructure-driven hydrogen distribution in stress corrosion cracking behavior of medium Mn steels0Machine Learning for Structural Steels: Materials Design, Property Prediction, Durability, and Future Directions0Using machine learning to predict superplasticity in high-entropy alloys0

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  1. Prediction of Corrosion Potential for Stainless Steel and Investigation of Rare Earth Element Influence Mechanism via Machine Learning

    SSRN Electronic Journal · 2026-01-01

    Zhenghao Yan, Bin Wu, Yicong Luo, Shiwei Yu, Endian Fan

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  2. A review of radiation effects and tribological behavior in multi-principal element alloys: towards extreme environment applications

    Review of Materials Research · 2026-03-01

    Jincheng Li, Qing Zhou, Dongpeng Hua, Zhenyuan Liang, Zikun Tang, Junpeng Gao, Haifeng Wang, Jian Wang

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  3. Machine learning applications in metallic materials: Recent advances and future perspectives

    Journal of Alloys and Compounds · 2026-03-24

    Bin Miao, Guan-Qi Lin, Yuan Zhang, Yu-Wei Cheng, Huan Yang

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  4. Accelerated alloy design through a fully integrated High-Throughput computational Framework: Case study on medium manganese steels

    Materials & Design · 2026-05-10

    Mahmoud Elaraby, Mohammed Mustafa Ali , Saeed Sadeghpour, Mamdouh Eissa, L.P. Karjalainen, Jukka I. Kömi, Sakari Pallaspuro, Pasi Pekka Suikkanen, Vahid Javaheri

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  5. Multi-fidelity physics-guided graph neural networks for predicting structure–property relationships in polycrystalline metals

    Materials & Design · 2026-05-28

    Minh Tien Tran, Hoang Cuong Phan, Jaimyun Jung, Se‐Jong Kim, Seong-Hoon Kang, Ho Won Lee

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  6. Critical role of microstructure-driven hydrogen distribution in stress corrosion cracking behavior of medium Mn steels

    Corrosion Science · 2026-07-14

    Jiayu Wang, Biaobiao Wang, Xiaoya Wang, Bingxuan Zhu, Liang Zhen, Li Liu

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  7. Machine Learning for Structural Steels: Materials Design, Property Prediction, Durability, and Future Directions

    Materials · 2026-08-25

    Guomin Wei, Minghe Li, Bo Cui, Wencui Xiu, Asmawan Mohd Sarman

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  8. Using machine learning to predict superplasticity in high-entropy alloys

    Journal of Materials Science · 2026-09-29

    Hamed Shahmir, Sina Kooshamanesh, Mohammad Sajad Mehranpour, Terence G. Langdon

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