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RESEARCH PAPER · SSRN Electronic Journal · 2022

Machine Learning and Python Assisted Design and Verification of Fe–Based Amorphous/Nanocrystalline Alloy

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Machine Learning and Python Assisted Design and Verification of Fe–Based Amorphous/Nanocrystalline AlloyOriginalMachine Learning-Assisted Design and Synthesis with Excellent Performance of Fe-Based Amorphous/Nanocrystalline Soft Magnetic Materials0Accelerated discovery of Fe-based amorphous/nanocrystalline alloy through explicit expression and interpretable information based on machine learning21Investigation on close-coupled gas atomization for Fe-based amorphous powder production via simulation and industrial trials: Part II. Particle flight and cooling during secondary atomization15Optimizing Glass Formation Ability and Magnetic Flux Density of High-Fe-Content Fe-Based Amorphous Alloys Using Interpretable Machine Learning0Generative model-based inverse design of Fe-based metallic glasses with high saturation magnetic flux density8Design and preparation of novel Fe-Si-B-P-C-Nb amorphous /nanocrystalline alloys guided by machine learning5Quantitatively predicting and evaluating the hardness of amorphous ribbons via a deep learning-symbolic regression approach0

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  1. Machine Learning-Assisted Design and Synthesis with Excellent Performance of Fe-Based Amorphous/Nanocrystalline Soft Magnetic Materials

    SSRN Electronic Journal · 2023-01-01

    jingfqian huang, Chengying Tang

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  2. Accelerated discovery of Fe-based amorphous/nanocrystalline alloy through explicit expression and interpretable information based on machine learning

    Materials & Design · 2023-06-02

    Bo Pang, Zhilin Long, Tao Long, Rong He, Xiaowei Liu, Mingwang Pan

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  3. Investigation on close-coupled gas atomization for Fe-based amorphous powder production via simulation and industrial trials: Part II. Particle flight and cooling during secondary atomization

    Journal of Materials Research and Technology · 2023-09-01

    Pu Wang, Jiaqi Liu, Yannan Dong, Huan Zhao, Jing Pang, Jiaquan Zhang

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  4. Optimizing Glass Formation Ability and Magnetic Flux Density of High-Fe-Content Fe-Based Amorphous Alloys Using Interpretable Machine Learning

    SSRN Electronic Journal · 2024-01-01

    Yichuan Tang, Yin Zhang, Ruonan Ma, Shaopeng Liu, Silong Li, Kun Wang, Minxia Fang, Kaiyan Cao, Chao Zhou, Chuanhui Cheng, Sen Yang

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  5. Generative model-based inverse design of Fe-based metallic glasses with high saturation magnetic flux density

    Journal of Alloys and Compounds · 2024-12-26

    K.Y. Li, L.C. Liu, L.C. Liu, Lin Shao, Jing Zhou, H.B. Ke, M.Z. Li, Weihua Wang

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  6. Design and preparation of novel Fe-Si-B-P-C-Nb amorphous /nanocrystalline alloys guided by machine learning

    Progress in Natural Science Materials International · 2025-01-18

    Yifan He, Rui Sun, Shengdong Tang, Zilong Xu, Long Hou, Chaohao Hu, Chengying Tang, Yuqin Liu

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  7. Quantitatively predicting and evaluating the hardness of amorphous ribbons via a deep learning-symbolic regression approach

    Mechanics of Materials · 2025-03-31

    Bo Pang, Zhilin Long, Tao Long, Zhonghuan Su

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