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RESEARCH PAPER · Metallurgical and Materials Transactions A · 2019

Predicting Twin Nucleation in a Polycrystalline Mg Alloy Using Machine Learning Methods

Zhounuo Tong, Leyun Wang, Gaoming Zhu, Xiaoqin Zeng

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Predicting Twin Nucleation in a Polycrystalline Mg Alloy Using Machine Learning MethodsOriginalInvestigation of the alloying effect on deformation behavior in Mg by Visco-Plastic Self-Consistent modeling59Predicting Tensile Properties of AZ31 Magnesium Alloys by Machine Learning66Multi-objective machine learning of four mechanical properties of steels14A deep learning based life prediction method for components under creep, fatigue and creep-fatigue conditions247A combined machine learning and EBSD approach for the prediction of {10-12} twin nucleation in an Mg-RE alloy22Grain-size effect on the deformation of Mg–3Al–3Sn alloy: Experiments and elastic-viscoplastic self-consistent modeling50C and N doping in high-entropy alloys: A pathway to achieve desired strength-ductility synergy138Quantitative analysis of mechanical properties associated with aging treatment and microstructure in Mg-Al-Zn alloys through machine learning59Predicting grain boundary damage by machine learning75A Machine Learning Enabled Ultra-Fine Grain Design Strategy of Mg-Mn-Based Alloys23Grain Boundary Slip – Twin Transmission in Titanium59Optimizing mechanical properties of magnesium alloys by philosophy of thermo-kinetic synergy: Review and outlook62Feature selection method reducing correlations among features by embedding domain knowledge101Machine learning of the mechanical properties and data-driven design of lead-free solder alloys1Prediction of electronic work function of the second phase in binary magnesium alloy based on machine learning method8Study on the key features of dynamic compressive fracture strain of Ti-Zr-Nb solid solution alloys through random forest regressor2A machine learning study of grain boundary damage in Mg alloy6Prediction of 316 stainless steel low-cycle fatigue life based on machine learning19An Improved Random Forest Magnesium Alloy Prediction Method Based on Particle Swarm Optimization2Machine learning dislocation density correlations and solute effects in Mg-based alloys9Predicting the Hall-Petch slope of magnesium alloys by machine learning29Divide and conquer: Machine learning accelerated design of lead-free solder alloys with high strength and high ductility37A combined EBSD and machine learning study of predicting deformation twinning in BCC Fe81Ga19 alloy3A brief review of machine learning-assisted Mg alloy design, processing, and property predictions38Twinning aspects and their efficient roles in wrought Mg alloys: A comprehensive review97Integration of artificial intelligence and big data in materials science: New paradigms and scientific discoveries2An improved extreme learning machine model for predicting the mechanical property of AZ80 magnesium alloy1Effect of Strain Rate on Deformation Mechanisms of a Mg Alloy13Leveraging molecular dynamics and machine learning to predict impact performance in polycrystalline magnesium alloys6Deep Learning for Lightweight Materials1AI-Driven Design of AZ61 Magnesium Alloy for Enhanced Performance in Industrial Automation5Effects of composition and natural ageing on microstructure and mechanical properties of Al-Mg-Si heat treatment-free die-cast alloys assisted with machine learning5Machine learning-based phase design of Mg-Re alloy LPSO phase2Review: machine learning approaches for diverse alloy systems46Investigating creep damage initiation at the mesoscale using high-resolution electron microscopy, crystal plasticity modelling, and a classification algorithm5Prediction of mechanical properties of magnesium alloys through machine learning: a review0Tailoring deformation mechanisms via minor Ce to improve the tension–compression yield asymmetry of Mg-Ca-Mn alloy3Performance-Boosted Interpretable ML via Optuna-SHAP: Uncovering Orientation-Driven Twinning in Mg Alloys0Strengthening mechanisms of carbon nanotube-reinforced polycrystalline magnesium composites: A molecular dynamics study0

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  1. Investigation of the alloying effect on deformation behavior in Mg by Visco-Plastic Self-Consistent modeling

    Journal of Magnesium and Alloys · 2020-02-14

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  2. Predicting Tensile Properties of AZ31 Magnesium Alloys by Machine Learning

    JOM · 2020-09-01

    Xuenan Xu, Leyun Wang, Gaoming Zhu, Xiaoqin Zeng

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  3. Multi-objective machine learning of four mechanical properties of steels

    Scientia Sinica Technologica · 2021-03-05

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  4. A deep learning based life prediction method for components under creep, fatigue and creep-fatigue conditions

    International Journal of Fatigue · 2021-03-18

    Xiaocheng Zhang, Jian‐Guo Gong, Fu‐Zhen Xuan

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  5. A combined machine learning and EBSD approach for the prediction of {10-12} twin nucleation in an Mg-RE alloy

    Materials Today Communications · 2021-03-27

    Yunwei Gui, Quanan Li, Kaige Zhu, Yibei Xue

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  6. Grain-size effect on the deformation of Mg–3Al–3Sn alloy: Experiments and elastic-viscoplastic self-consistent modeling

    International Journal of Plasticity · 2021-05-01

    Gaoming Zhu, Leyun Wang, Yingjun Sun, Xiaoqing Shang, Jie Wang, Huamiao Wang, Xiaoqin Zeng

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  7. C and N doping in high-entropy alloys: A pathway to achieve desired strength-ductility synergy

    Applied Materials Today · 2021-09-04

    Mengyuan He, Y.F. Shen, Nan Jia, Peter Kaehuei Liaw

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  8. Quantitative analysis of mechanical properties associated with aging treatment and microstructure in Mg-Al-Zn alloys through machine learning

    Journal of Material Science and Technology · 2021-10-07

    Joung Sik Suh, Byeong‐Chan Suh, Sang Eun Lee, Jun Ho Bae, Byoung-Gi Moon

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  9. Predicting grain boundary damage by machine learning

    International Journal of Plasticity · 2021-12-17

    Sheng Zhang, Leyun Wang, Gaoming Zhu, Martin Diehl, Alireza Maldar, Xiaoqing Shang, Xiaoqin Zeng

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  10. A Machine Learning Enabled Ultra-Fine Grain Design Strategy of Mg-Mn-Based Alloys

    Journal of Materials Research and Technology · 2022-01-01

    Xiaoxi Mi, Xuerui Jing, Hailian Wang, Jianbin Xu, Jia She, Aitao Tang, Bjørn Holmedal, Fusheng Pan

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  11. Grain Boundary Slip – Twin Transmission in Titanium

    Acta Materialia · 2022-01-01

    Behnam Ahmadikia, Leyun Wang, Mariyappan Arul Kumar, Irene J. Beyerlein

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  12. Optimizing mechanical properties of magnesium alloys by philosophy of thermo-kinetic synergy: Review and outlook

    Journal of Magnesium and Alloys · 2022-02-01

    Tianle Wang, Feng Liu

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  13. Feature selection method reducing correlations among features by embedding domain knowledge

    Acta Materialia · 2022-07-22

    Yue Liu, Xinxin Zou, Shuchang Ma, Maxim Avdeev, Siqi Shi

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  14. 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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  15. Prediction of electronic work function of the second phase in binary magnesium alloy based on machine learning method

    Journal of materials research/Pratt's guide to venture capital sources · 2022-09-29

    Xiaoxiu Wei, Jianfeng Wang, Chao Wang, Shijie Zhu, Liguo Wang, Shaokang Guan

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  16. Study on the key features of dynamic compressive fracture strain of Ti-Zr-Nb solid solution alloys through random forest regressor

    Journal of Physics Conference Series · 2022-10-01

    Bojian Fan, Xuemei Liu, Shengping Si, Shuang Liu, Ruyue Xie, Jinxu Liu

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  17. A machine learning study of grain boundary damage in Mg alloy

    Materials Science and Engineering A · 2023-02-01

    Sheng Zhang, Gaoming Zhu, Yunhao Fan, Xiaoqin Zeng, Leyun Wang

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  18. Prediction of 316 stainless steel low-cycle fatigue life based on machine learning

    Scientific Reports · 2023-04-25

    Hongyan Duan, Mengjie Cao, Lin Liu, Shunqiang Yue, Hong He, Yingjian Zhao, Zengwang Zhang, Yang Liu

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  19. An Improved Random Forest Magnesium Alloy Prediction Method Based on Particle Swarm Optimization

    Materials science forum · 2023-05-18

    Yu Zhang, Shi Cheng Wei, Bo Wang, Lin Wei Li

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  20. Machine learning dislocation density correlations and solute effects in Mg-based alloys

    Scientific Reports · 2023-07-10

    Henri Salmenjoki, Stefanos Papanikolaou, Dongfeng Shi, Damien Tourret, Carmen María Cepeda-Jiménez, María Teresa Pérez-Prado, Lasse Laurson, Mikko J. Alava

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  21. Predicting the Hall-Petch slope of magnesium alloys by machine learning

    Journal of Magnesium and Alloys · 2023-08-03

    Bo Guan, Chao Chen, Yunchang Xin, Jing Rui Xu, Bo Feng, Xiaoxu Huang, Qing Liu

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  22. Divide and conquer: Machine learning accelerated design of lead-free solder alloys with high strength and high ductility

    npj Computational Materials · 2023-10-23

    Qinghua Wei, Bin Cao, Hao Yuan, Youyang Chen, Kangdong You, Shuting Yu, Tixin Yang, Ziqiang Dong, Tong‐Yi Zhang

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  23. A combined EBSD and machine learning study of predicting deformation twinning in BCC Fe81Ga19 alloy

    Materials Today Communications · 2024-02-25

    Zongwen Ma, Yuanxiang Zhang, Yukun Xia, Yuchen Wang, R.D.K. Misra, Feng Fang, Yang Wang, Xiaoming Zhang

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  24. A brief review of machine learning-assisted Mg alloy design, processing, and property predictions

    Journal of Materials Research and Technology · 2024-05-01

    Yanhui Cheng, Lifei Wang, Chaoyang Yang, Yunli Bai, Hongxia Wang, Weili Cheng, Hanuma Reddy Tiyyagura, Alexander A. Komissarov, Kwang Seon Shin

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  25. Twinning aspects and their efficient roles in wrought Mg alloys: A comprehensive review

    Journal of Magnesium and Alloys · 2024-06-01

    S.S.A. Shah, Manping Liu, Azim Khan, Farooq Ahmad, M.R. Abdullah, Xingquan Zhang, Shiwei Xu, Zhen Peng

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  26. Integration of artificial intelligence and big data in materials science: New paradigms and scientific discoveries

    Chinese Science Bulletin (Chinese Version) · 2024-07-05

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  27. An improved extreme learning machine model for predicting the mechanical property of AZ80 magnesium alloy

    Applied Physics A · 2024-07-25

    Jiahan Gu, Song Jiang, Wenbo Guo, Leilei Wang, Jianping Zhang

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  28. Effect of Strain Rate on Deformation Mechanisms of a Mg Alloy

    Metallurgical and Materials Transactions A · 2025-03-15

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  29. Leveraging molecular dynamics and machine learning to predict impact performance in polycrystalline magnesium alloys

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  30. Deep Learning for Lightweight Materials

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  31. AI-Driven Design of AZ61 Magnesium Alloy for Enhanced Performance in Industrial Automation

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  32. Effects of composition and natural ageing on microstructure and mechanical properties of Al-Mg-Si heat treatment-free die-cast alloys assisted with machine learning

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  33. Machine learning-based phase design of Mg-Re alloy LPSO phase

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  34. Review: machine learning approaches for diverse alloy systems

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  35. Investigating creep damage initiation at the mesoscale using high-resolution electron microscopy, crystal plasticity modelling, and a classification algorithm

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  36. Prediction of mechanical properties of magnesium alloys through machine learning: a review

    Emerging Materials Research · 2026-02-23

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  37. Tailoring deformation mechanisms via minor Ce to improve the tension–compression yield asymmetry of Mg-Ca-Mn alloy

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  38. Performance-Boosted Interpretable ML via Optuna-SHAP: Uncovering Orientation-Driven Twinning in Mg Alloys

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  39. Strengthening mechanisms of carbon nanotube-reinforced polycrystalline magnesium composites: A molecular dynamics study

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