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RESEARCH PAPER · Journal of Applied Physics · 2023

Tutorial: Deep learning prediction of thermophysical properties for liquid multicomponent alloys

Ruilin Xiao, Kelun Liu, Ying Ruan, Liang Hu, Bingbo Wei

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Tutorial: Deep learning prediction of thermophysical properties for liquid multicomponent alloysOriginalDeep alloys: Metal materials empowered by deep learning24Thermophysical properties of undercooled Zr–Fe–Nb alloys investigated by electrostatic levitation and molecular dynamics calculation1Active learning prediction and electromagnetic levitation measurement of metastable liquid properties of Ti-Al-Zr alloy1Identification of the thermal conductivity evolution of polypropylene during crystallization2Comparison of machine-learning models in predicting the liquidus temperature of aluminum cryolitic bath0Structural evolution and mechanical behavior of NbTiVZr high-entropy alloy fabricated by electrostatic levitation0Surface-informed active learning prediction of thermophysical properties for liquid refractory multicomponent alloy0Machine learning in conductivity development of specific alloys1Machine Learning Prediction on Liquidus Temperature for Stainless Steel by Thermodynamics and Measurement Dual-Database-Driven Model1Electrostatic levitation processing and deep learning investigation of liquid W-Zr refractory alloys0

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  1. Deep alloys: Metal materials empowered by deep learning

    Materials Science in Semiconductor Processing · 2024-05-18

    Kaiyuan Zheng, Zhongping He, Lun Che, Hong Cheng, Meiling Ge, Tianyu Si, Xiaotian Xu

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  2. Thermophysical properties of undercooled Zr–Fe–Nb alloys investigated by electrostatic levitation and molecular dynamics calculation

    Journal of Applied Physics · 2024-12-02

    D. D. Zuo, Jian Chang, Dingnan Liu, H. Liao, Haipeng Wang

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  3. Active learning prediction and electromagnetic levitation measurement of metastable liquid properties of Ti-Al-Zr alloy

    Review of Materials Research · 2025-01-01

    Ruilin Xiao, Kelun Liu, H.R. Li, Ying Ruan

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  4. Identification of the thermal conductivity evolution of polypropylene during crystallization

    Applied Thermal Engineering · 2025-02-25

    Rita Moussallem, Elissa El Rassy, Nicolas Lefevre, Jean‐Luc Bailleul

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  5. Comparison of machine-learning models in predicting the liquidus temperature of aluminum cryolitic bath

    Materials Chemistry and Physics · 2025-04-09

    Konstantinos Betsis, Nikolaos E. Karkalos, Anthimos Xenidis

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  6. Structural evolution and mechanical behavior of NbTiVZr high-entropy alloy fabricated by electrostatic levitation

    Journal of Materials Research and Technology · 2025-07-01

    Yongxia Fan, Yan Lin, Qingbo Ao, Jian Wang, Chun Li, Chong Wei, J Wang

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  7. Surface-informed active learning prediction of thermophysical properties for liquid refractory multicomponent alloy

    Computational Materials Science · 2026-02-01

    Kelun Liu, Yingchao Hai, Ying Ruan, Bingbo Wei

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  8. Machine learning in conductivity development of specific alloys

    Canadian Metallurgical Quarterly · 2026-02-27

    Omid Karimi Sadaghiani, Sahba Tabarraei, İlknur Tunç, Wei Peng, Raha Shabani

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  9. Machine Learning Prediction on Liquidus Temperature for Stainless Steel by Thermodynamics and Measurement Dual-Database-Driven Model

    Metallurgical and Materials Transactions B · 2026-03-30

    Chenguang Su, Huashun Chen, Peng Lan, Liang Zhang, cheng Wang

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  10. Electrostatic levitation processing and deep learning investigation of liquid W-Zr refractory alloys

    Acta Materialia · 2026-07-28

    Kelun Liu, Ruilin Xiao, Yingchao Hai, Ying Ruan, Bingbo Wei

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