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RESEARCH ARTICLE · Materials & Design · 2022

Machine learning and Python assisted design and verification of Fe–based amorphous/nanocrystalline alloy

Yichuan Tang, Yuan Wan, Zhongqi Wang, Cong Zhang, Jiani Han, Chaohao Hu, Chengying Tang

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Machine learning and Python assisted design and verification of Fe–based amorphous/nanocrystalline alloyOriginalPrediction of Vickers hardness of amorphous alloys based on interpretable machine learning49Machine Learning-Assisted Design and Synthesis with Excellent Performance of Fe-Based Amorphous/Nanocrystalline Soft Magnetic Materials0Prediction of external corrosion rate in Oil and Gas platforms using ensemble learning8Prediction of magnetocaloric properties of Fe-based amorphous alloys based on interpretable machine learning24Customized design of amorphous solids by generative deep learning6Optimizing Glass Formation Ability and Magnetic Flux Density of High-Fe-Content Fe-Based Amorphous Alloys Using Interpretable Machine Learning0Interpretable machine learning-assisted design of Fe-based nanocrystalline alloys with high saturation magnetic induction and low coercivity39XGBoost algorithm-assisted prediction of amorphous formability of Fe-based nanocrystalline precursor5Machine learning assisted optimization of soft magnetic properties in ternary Fe–Si–Al alloys16Alloys innovation through machine learning: a statistical literature review14Experimentally validated inverse design of multi-property Fe-Co-Ni alloys30Highly effective design of high GFA alloys with different metal-based and various components by machine learning14Accurate prediction of magnetocaloric effect in NiMn‐based Heusler alloys by prioritizing phase transitions through explainable machine learning25Machine learning assisted design and preparation of Fe85Si2B8.5P3.5C1 amorphous/nanocrystalline alloy with high B and low H12Machine Learning Algorithm for Prediction of Iron Loss of Electrical Steel under Stress1Machine Learning Application for Functional Properties Prediction in Magnetic Materials7Integrating ultra-high saturation magnetization intensity and extreme-low coercivity in FeCoBSiCu alloy assisted by machine learning33Generative model-based inverse design of Fe-based metallic glasses with high saturation magnetic flux density8Machine Learning Tools and Web Services for Materials Science Modeling1Predicting the magnetic properties of Fe-based bulk metallic glasses by ensemble machine learning and interpretable information5A Review of Artificial Intelligence‐Driven Innovations in Soft Magnetic Materials Optimization: Current Trends and Future Horizons13Extremely wide annealing processing window in Fe-based amorphous/nanocrystalline alloy guided by machine learning6Machine learning approach for predicting the fracture toughness of bulk metallic glasses2Investigation on the thermal stability, crystallization kinetics and magnetic properties of Fe-Si-B-P-C-Nb amorphous alloys4Application of Machine Learning in Amorphous Alloys4Pushing the Frontiers: Artificial Intelligence (AI)‐Guided Programmable Concepts in Binary Self‐Assembly of Colloidal Nanoparticles15Active learning-based generative design of halogen-free flame-retardant polymeric composites6Iron-Based High-Temperature Alloys: Alloying Strategies and New Opportunities19Tailoring glass forming ability and coercivity in iron-based nanocrystalline alloys through machine learning-driven feature interpretation1Review: Recent progress in titanium alloys development via machine learning and high-throughput preparation4Design of Fe–Co-based amorphous/nanocrystalline alloy with Bs of 2.03T by physics guided active learning0Design of Nanocrystalline Alloy for Reducing Motor Hysteresis Loss by Machine Learning0Multi-task deep-learning optimization of trade-off properties for superior-performance Fe-based soft magnetic alloys0Investigation of the Composition–Phase-Transformation Temperature Relationship in the NiTi-Based Alloys Using Machine Learning0Several issues on the development of soft magnetic composites for high-power magnetic devices0Effect of annealing on atomic structure and mechanical response of fe 78 si 13 b 9 amorphous alloy: a molecular dynamics investigation0Closed-loop active-inverse learning for constrained discovery of high-resistivity Fe-based amorphous alloys0Quantitative descriptor-dependent design of high-Bs Fe-based amorphous alloys using an interpretable machine learning framework0

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

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  3. Prediction of external corrosion rate in Oil and Gas platforms using ensemble learning

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  4. Prediction of magnetocaloric properties of Fe-based amorphous alloys based on interpretable machine learning

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  5. Customized design of amorphous solids by generative deep learning

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

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    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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  7. Interpretable machine learning-assisted design of Fe-based nanocrystalline alloys with high saturation magnetic induction and low coercivity

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  8. XGBoost algorithm-assisted prediction of amorphous formability of Fe-based nanocrystalline precursor

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  9. Machine learning assisted optimization of soft magnetic properties in ternary Fe–Si–Al alloys

    Journal of Materials Research and Technology · 2024

    V. A. Milyutin, Radovan Búreš, Mária Fáberová, Zuzana Birčáková, Zuzana Molčanová, Branislav Kunca, Л. А. Сташкова, Peter Kollár, Jan Füzer

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  10. Alloys innovation through machine learning: a statistical literature review

    Science and Technology of Advanced Materials Methods · 2024

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  11. Experimentally validated inverse design of multi-property Fe-Co-Ni alloys

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    Shakti P. Padhy, Varun Chaudhary, Yee-Fun Lim, Ruiming Zhu, Muang Thway, Kedar Hippalgaonkar, Raju V. Ramanujan

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  12. Highly effective design of high GFA alloys with different metal-based and various components by machine learning

    Science China Technological Sciences · 2024

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  13. Accurate prediction of magnetocaloric effect in NiMn‐based Heusler alloys by prioritizing phase transitions through explainable machine learning

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  14. Machine learning assisted design and preparation of Fe85Si2B8.5P3.5C1 amorphous/nanocrystalline alloy with high B and low H

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    Shengdong Tang, Rui Sun, Yifan He, Guichang Liu, Ruixuan Wang, Yuqin Liu, Chengying Tang

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  15. Machine Learning Algorithm for Prediction of Iron Loss of Electrical Steel under Stress

    Journal of the Japan Institute of Metals and Materials · 2024

    Kyouhei Hayakawa, Isao Matsui, Takumi Hamaguchi, Takaharu Maeguchi

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  16. Machine Learning Application for Functional Properties Prediction in Magnetic Materials

    The Physics of Metals and Metallography · 2024

    V. A. Milyutin, Н. Н. Никульченков

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  17. Integrating ultra-high saturation magnetization intensity and extreme-low coercivity in FeCoBSiCu alloy assisted by machine learning

    Acta Materialia · 2024

    Wenhui Guo, You Wu, Lingxiang Shi, Jili Jia, Ranbin Wang, Hengtong Bu, Zongfan Zhu, Yang Shao, Kefu Yao

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

    Journal of Alloys and Compounds · 2024

    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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  19. Machine Learning Tools and Web Services for Materials Science Modeling

    Challenges and advances in computational chemistry and physics · 2025

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  20. Predicting the magnetic properties of Fe-based bulk metallic glasses by ensemble machine learning and interpretable information

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    Qiulin Bi, Jialiang Liu, Junting Li, Hanmei Chen, Shunwei Chen, Xiujun Han

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  21. A Review of Artificial Intelligence‐Driven Innovations in Soft Magnetic Materials Optimization: Current Trends and Future Horizons

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  22. Extremely wide annealing processing window in Fe-based amorphous/nanocrystalline alloy guided by machine learning

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    Rui Sun, Lichen Liu, Yifan He, Rongce Sun, Zilong Xu, Zhuoqun Fan, Yinghui Zhou, Yinghui Zhou, Chaohao Hu, Jing Zhou, Haibo Ke, Chengying Tang

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  23. Machine learning approach for predicting the fracture toughness of bulk metallic glasses

    The Philosophical Magazine A Journal of Theoretical Experimental and Applied Physics · 2025

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  24. Investigation on the thermal stability, crystallization kinetics and magnetic properties of Fe-Si-B-P-C-Nb amorphous alloys

    Progress in Natural Science Materials International · 2025

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  25. Application of Machine Learning in Amorphous Alloys

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  26. Pushing the Frontiers: Artificial Intelligence (AI)‐Guided Programmable Concepts in Binary Self‐Assembly of Colloidal Nanoparticles

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  27. Active learning-based generative design of halogen-free flame-retardant polymeric composites

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  28. Iron-Based High-Temperature Alloys: Alloying Strategies and New Opportunities

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  29. Tailoring glass forming ability and coercivity in iron-based nanocrystalline alloys through machine learning-driven feature interpretation

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  30. Review: Recent progress in titanium alloys development via machine learning and high-throughput preparation

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  31. Design of Fe–Co-based amorphous/nanocrystalline alloy with Bs of 2.03T by physics guided active learning

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  32. Design of Nanocrystalline Alloy for Reducing Motor Hysteresis Loss by Machine Learning

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  33. Multi-task deep-learning optimization of trade-off properties for superior-performance Fe-based soft magnetic alloys

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  34. Investigation of the Composition–Phase-Transformation Temperature Relationship in the NiTi-Based Alloys Using Machine Learning

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  35. Several issues on the development of soft magnetic composites for high-power magnetic devices

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  36. Effect of annealing on atomic structure and mechanical response of fe 78 si 13 b 9 amorphous alloy: a molecular dynamics investigation

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  37. Closed-loop active-inverse learning for constrained discovery of high-resistivity Fe-based amorphous alloys

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  38. Quantitative descriptor-dependent design of high-Bs Fe-based amorphous alloys using an interpretable machine learning framework

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