BetaCitation Atlas

RESEARCH PAPER · Metallurgical and Materials Transactions A · 2021

Accelerated Development of High-Strength Magnesium Alloys by Machine Learning

Yanwei Liu, Leyun Wang, Huan Zhang, Gaoming Zhu, Jie Wang, Yuhui Zhang, Xiaoqin Zeng

Read original paper ↗

See where this research leads.

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

Open in your workspace →Saved 2026-10-14 · complete coverage
Filter Citing Papers
Press Enter or comma to add · × to remove · searches titles and available abstracts
Journals
All journals
Most represented first · select one or more
Explore Terms In These PapersHide SuggestionsShow Suggestions

Original and current center papers stay visible as anchors.
Numbers show citations received
42 direct citing works

Accelerated Development of High-Strength Magnesium Alloys by Machine LearningOriginalBoosting for concept design of casting aluminum alloys driven by combining computational thermodynamics and machine learning techniques32Effect of cryomilling time on microstructure evolution and hardness of cryomilled AZ31 powders15A reverse design model for high-performance and low-cost magnesium alloys by machine learning48Recent progress in the machine learning-assisted rational design of alloys69Design of super-hard high-entropy ceramics coatings via machine learning62Prediction of mechanical properties of Mg-rare earth alloys by machine learning16Design of Light-Metal Alloys Using Machine Learning Techniques0Machine learning assisted design of aluminum-lithium alloy with high specific modulus and specific strength56Digital Model for Rapid Prediction of Die Forging Force and Forming Control of Aluminum Alloy Aviation Components0Unveiling the Alloying-Processing-Microstructure Correlations in High-Formability Sheet Magnesium Alloys5Accelerated discovery of high-performance Al-Si-Mg-Sc casting alloys by integrating active learning with high-throughput CALPHAD calculations40Machine learning dislocation density correlations and solute effects in Mg-based alloys9Machine learning assisted materials design of high-speed railway wheel with better fatigue performance11Machine Learning Aided Prediction and Design for the Mechanical Properties of Magnesium Alloys60Data Science Approach for EBSD Data Processing and Materials Design for Magnesium Alloy0Development of High-Strength Mg–Gd–Y Alloy Based on Machine Learning Method2Accelerated design of high-performance Mg-Mn-based magnesium alloys based on novel bayesian optimization40Ultrafine-grained Mg alloy: Preparation, properties, design strategy51Alloys innovation through machine learning: a statistical literature review14A brief review of machine learning-assisted Mg alloy design, processing, and property predictions38Predicting grain size-dependent superplastic properties in friction stir processed ZK30 magnesium alloy with machine learning methods17Inversely optimized design of Al-Mg-Si alloys using machine learning methods26A machine learning method based on TPE-XGBoost model for TRIP/TWIP near-β titanium alloy design11Accelerated intelligent prediction and analysis of mechanical properties of magnesium alloys based on scaled super learner machine-learning algorithms9Prediction of phases and mechanical properties of magnesium-based high-entropy alloys using machine learning9Towards high stiffness and ductility—The Mg-Al-Y alloy design through machine learning53Prediction of mechanical properties of magnesium alloys based on machine learning0Machine learning-based study of hardness in polypropylene/carbon nanotube and low-density polyethylene/carbon nanotube composites25Accelerated design of age-hardened Mg-Ca-Zn alloys with enhanced mechanical properties via machine learning9Machine Learning Assisted Design of High Thermal Conductivity and High Strength Mg Alloys8Accelerated material discovery of high-performance Mg alloys via active learning and high throughput multi-objective informed Bayesian optimization7Machine learning-driven prediction of microstructure-mechanical property relationships in Mg-Al alloys10Review: machine learning approaches for diverse alloy systems46Reverse design of Mg-Zn-Mn-Sr-Ca alloys for biodegradable implants by interpretable machine learning and genetic algorithm15Composition design and process optimization of refractory boride coatings based on high-throughput preparation and machine learning2Digital model for rapid prediction and autonomous control of die forging force for aluminum alloy aviation components8Bridge for the thermodynamics and kinetics of electrochemical corrosion: Influence of trace fluctuations in alloying element content on the corrosion resistance of low alloy steels7Prediction of mechanical properties of magnesium alloys through machine learning: a review0Machine learning-driven composition optimization for high strength and ductility in Al-Mg-Si-Zr-Er alloys4Heat treatment design guided by semi-supervised learning for strength-ductility optimization in an Al-Cu-Li alloy0Improving Alloy Mechanical Property Prediction by Addressing Data Homogeneity through Noise Augmentation0A survey of machine learning applications for Mg alloys: A promising prospect for current trends and future directions2A Precipitation Strengthening Parameter Driven Design Strategy for Aging Hardened Nickel-Based Corrosion-Resistant Alloys: Thermodynamics-Genetic Algorithm Integration0Computational strategies for the design of high-strength and high-thermal-conductivity casting Mg/Al alloys1Machine learning for high-performance alloy design: A review0

Arrows point to the paper being cited. Selecting a paper shows its incoming citations and its known path back to the original paper.

Drag to pan · Use + / − to zoom · Escape to close43 nodes / 53 citation links
Browse Matching Papers

Papers that cite this work

45 direct citing papers saved.

  1. Boosting for concept design of casting aluminum alloys driven by combining computational thermodynamics and machine learning techniques

    Journal of Materials Informatics · 2021-01-01

    Yi Wang, Guangchen Liu, Jianbao Gao, Lijun Zhang

    Read original paper ↗ ·

  2. Effect of cryomilling time on microstructure evolution and hardness of cryomilled AZ31 powders

    Materials Characterization · 2021-07-11

    Dikai Guan, Junheng Gao, W.M. Rainforth

    Read original paper ↗ ·

  3. A reverse design model for high-performance and low-cost magnesium alloys by machine learning

    Computational Materials Science · 2021-09-27

    Xiaoxi Mi, Lianjuan Tian, Aitao Tang, Jing X. Kang, Peng Peng, Jia She, Hailian Wang, Xianhua Chen, Fusheng Pan

    Read original paper ↗ ·

  4. Recent progress in the machine learning-assisted rational design of alloys

    International Journal of Minerals Metallurgy and Materials · 2022-04-01

    Huadong Fu, Hong‐Tao Zhang, Changsheng Wang, Wei Yong, Jianxin Xie

    Read original paper ↗ ·

  5. Design of super-hard high-entropy ceramics coatings via machine learning

    Ceramics International · 2022-07-30

    Xiaoqian Xu, Xiaobo Wang, Shaoyu Wu, Luchun Yan, Tao Guo, Kewei Gao, Xiaolu Pang, Alex A. Volinsky

    Read original paper ↗ ·

  6. Prediction of mechanical properties of Mg-rare earth alloys by machine learning

    Materials Research Express · 2022-10-01

    Jie Lu, Yanghua Chen, Meng Xu, YingZhang

    Read original paper ↗ ·

  7. Design of Light-Metal Alloys Using Machine Learning Techniques

    Materials horizons · 2022-11-18

    P. G. Kubendran Amos

    Read original paper ↗ ·

  8. Machine learning assisted design of aluminum-lithium alloy with high specific modulus and specific strength

    Materials & Design · 2022-12-09

    Huiyu Li, Xiwu Li, Yanan Li, Wei Xiao, Kai Wen, Zhihui Li, Yongan Zhang, Baiqing Xiong

    Read original paper ↗ ·

  9. Digital Model for Rapid Prediction of Die Forging Force and Forming Control of Aluminum Alloy Aviation Components

    SSRN Electronic Journal · 2023-01-01

    Hao Hu, Fan Zhao, Daoxiang Wu, Wei Yong, Zhengan Wang, Jianxin Xie

    Read original paper ↗ ·

  10. Unveiling the Alloying-Processing-Microstructure Correlations in High-Formability Sheet Magnesium Alloys

    Metals · 2023-04-03

    Jiyong Yang, Renhai Shi, Alan A. Luo

    Read original paper ↗ ·

  11. Accelerated discovery of high-performance Al-Si-Mg-Sc casting alloys by integrating active learning with high-throughput CALPHAD calculations

    Science and Technology of Advanced Materials · 2023-04-11

    Jianbao Gao, Jing Zhong, Guangchen Liu, Shaoji Zhang, Jiali Zhang, Zuming Liu, Bo Song, Lijun Zhang

    Read original paper ↗ ·

  12. 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

    Read original paper ↗ ·

  13. Machine learning assisted materials design of high-speed railway wheel with better fatigue performance

    Engineering Fracture Mechanics · 2023-09-09

    Xiuyang Fang, Jian-En Gong, Feng Zhang, Haonan Zhang, Jia-Hong Wu

    Read original paper ↗ ·

  14. Machine Learning Aided Prediction and Design for the Mechanical Properties of Magnesium Alloys

    Metals and Materials International · 2023-09-13

    Shuya Dong, Yingying Wang, Jinya Li, Yuanyuan Li, Li Wang, Jinglai Zhang

    Read original paper ↗ ·

  15. Data Science Approach for EBSD Data Processing and Materials Design for Magnesium Alloy

    ˜The œminerals, metals & materials series · 2024-01-01

    Haoran Yi, Xun Zeng, Dikai Guan

    Read original paper ↗ ·

  16. Development of High-Strength Mg–Gd–Y Alloy Based on Machine Learning Method

    ˜The œminerals, metals & materials series · 2024-01-01

    Yunchuan Cheng, Zhihua Dong, Yuan Peng, Zhi-Ying Zheng, Xiaoying Qian, Cuihong Wang, Bin Jiang, Fusheng Pan

    Read original paper ↗ ·

  17. Accelerated design of high-performance Mg-Mn-based magnesium alloys based on novel bayesian optimization

    Journal of Magnesium and Alloys · 2024-01-21

    Xiaoxi Mi, Lili Dai, Xuerui Jing, Jia She, Bjørn Holmedal, Aitao Tang, Fusheng Pan

    Read original paper ↗ ·

  18. Ultrafine-grained Mg alloy: Preparation, properties, design strategy

    Journal of Materials Research and Technology · 2024-02-21

    Peng Peng, Hansong Xue, Jia She, Jianyue Zhang, Aitao Tang, Shuai Long, Cheng Zhang, Qingshan Yang, Fusheng Pan

    Read original paper ↗ ·

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

    Science and Technology of Advanced Materials Methods · 2024-03-04

    Alireza Valizadeh, Ryoji Sahara, Maaouia Souissi

    Read original paper ↗ ·

  20. 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

    Read original paper ↗ ·

  21. Predicting grain size-dependent superplastic properties in friction stir processed ZK30 magnesium alloy with machine learning methods

    Journal of Magnesium and Alloys · 2024-05-01

    Farid Bahari-Sambran, F. Carreño, Carmen María Cepeda-Jiménez, Alberto Orozco‐Caballero

    Read original paper ↗ ·

  22. Inversely optimized design of Al-Mg-Si alloys using machine learning methods

    Computational Materials Science · 2024-05-22

    Qiqi Shen, Qiao Yin, Hongliang Zhao, Shuya Zhang, Yuheng Fan, Xianglei Dong, Chunwen Guo

    Read original paper ↗ ·

  23. A machine learning method based on TPE-XGBoost model for TRIP/TWIP near-β titanium alloy design

    Materials Today Communications · 2024-09-04

    Zheng Wang, Haoyu Zhang, Shengyuan Wang, Jun Jie Cheng, Chuan Wang, Ge Zhou, Siqian Zhang, Lijia Chen

    Read original paper ↗ ·

  24. Accelerated intelligent prediction and analysis of mechanical properties of magnesium alloys based on scaled super learner machine-learning algorithms

    Mechanics of Materials · 2024-09-19

    Atwakyire Moses, Ying Gui, Buzhuo Chen, B.H. Chen, Marembo Micheal, Ding Chen

    Read original paper ↗ ·

  25. Prediction of phases and mechanical properties of magnesium-based high-entropy alloys using machine learning

    Journal of King Saud University - Science · 2024-09-24

    Robert Kennedy Otieno, Edward V. Odhong, Charles M. Ondieki

    Read original paper ↗ ·

  26. Towards high stiffness and ductility—The Mg-Al-Y alloy design through machine learning

    Journal of Material Science and Technology · 2024-10-18

    Zhiyuan Liu, Tianyou Wang, Jin Li, Jian Zeng, Shuai Dong, Fenghua Wang, Fulin Wang, Jie Dong

    Read original paper ↗ ·

  27. Prediction of mechanical properties of magnesium alloys based on machine learning

    SSRN Electronic Journal · 2025-01-01

    Xi He, Shuo Peng, Jinde Liu, Chenlong Qin, Yingying Chen, Yushu Yu, Liang Zhao, Gang Jiang

    Read original paper ↗ ·

  28. Machine learning-based study of hardness in polypropylene/carbon nanotube and low-density polyethylene/carbon nanotube composites

    Discover Materials · 2025-01-04

    Harshit Sharma, Gaurav Arora, Raj Kumar, Suman Debnath, Suchart Siengchin

    Read original paper ↗ ·

  29. Accelerated design of age-hardened Mg-Ca-Zn alloys with enhanced mechanical properties via machine learning

    Computational Materials Science · 2025-01-10

    Chenhui Zhang, Yuhui Zhang, Yuhui Zhang, Benpeng Ren, Yanling Hu, Yanling Hu, Yanfu Chai, Longshan Xu, Qinghang Wang

    Read original paper ↗ ·

  30. Machine Learning Assisted Design of High Thermal Conductivity and High Strength Mg Alloys

    Metallurgical and Materials Transactions A · 2025-03-03

    Huafeng Liu, Taiki Nakata, Chao Xu, Deli Zhao, Lin Zhu, Nan Qu, Haoyang Ding, Kun-kun Deng, Kai-bo Nie, Tao Liu, Guangze Tang, Xiaojun Wang, S. Kamado, Lin Geng

    Read original paper ↗ ·

  31. Accelerated material discovery of high-performance Mg alloys via active learning and high throughput multi-objective informed Bayesian optimization

    Materials Today Communications · 2025-04-09

    Atwakyire Moses, Ying Gui, Ding Chen

    Read original paper ↗ ·

  32. Machine learning-driven prediction of microstructure-mechanical property relationships in Mg-Al alloys

    Journal of Alloys and Compounds · 2025-07-01

    Shi-min Ai, Da-ran Fang, Yao-wei Guo, Jie Ye, Xiaoping Lin

    Read original paper ↗ ·

  33. Review: machine learning approaches for diverse alloy systems

    Journal of Materials Science · 2025-07-17

    Arafat Rahman, Md Sojib Hossain, Abdullah-Bin Siddique

    Read original paper ↗ ·

  34. Reverse design of Mg-Zn-Mn-Sr-Ca alloys for biodegradable implants by interpretable machine learning and genetic algorithm

    Materials & Design · 2025-07-29

    Joung Sik Suh, Jae Hoon Jang, Byeong‐Chan Suh, Jae‐Yeon Kim

    Read original paper ↗ ·

  35. Composition design and process optimization of refractory boride coatings based on high-throughput preparation and machine learning

    Journal of Materials Science · 2025-08-23

    Binyuan Jia, Wenhui Ma, Qiang Wan, Cai Lu, Qiwen Wei, Bing Yang, Yangyi Xiao

    Read original paper ↗ ·

  36. Digital model for rapid prediction and autonomous control of die forging force for aluminum alloy aviation components

    International Journal of Minerals Metallurgy and Materials · 2025-09-01

    Hao Hu, Fan Zhao, Daoxiang Wu, Zhengan Wang, Zhilei Wang, Zhihao Zhang, Zhilei Wang, Zhihao Zhang, Weidong Li, Jianxin Xie

    Read original paper ↗ ·

  37. Bridge for the thermodynamics and kinetics of electrochemical corrosion: Influence of trace fluctuations in alloying element content on the corrosion resistance of low alloy steels

    Corrosion Science · 2026-01-14

    Zinuo Wang, Peng Zhou, Tao Zhang, Fuhui Wang

    Read original paper ↗ ·

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

    Emerging Materials Research · 2026-02-23

    A. Packia Antony Amalan, N Renugadevi

    Read original paper ↗ ·

  39. Machine learning-driven composition optimization for high strength and ductility in Al-Mg-Si-Zr-Er alloys

    Journal of Alloys and Compounds · 2026-03-20

    Tianjie Wang, Shaohong Li, Jie Yuan, Jun Li, Liexing Zhou, Junwan Li, Hengyong Bu, Huarong Qi, Min Li

    Read original paper ↗ ·

  40. Heat treatment design guided by semi-supervised learning for strength-ductility optimization in an Al-Cu-Li alloy

    Materials & Design · 2026-04-20

    Lyu Jing, Yanan Li, Li Xiwu, Xiao Wei, Chen Shuhai, Kai Wen, Qilong Liu, Yan Hongwei, Mingyang Yu, Li Zhihui, Zhang Yongan, Baiqing Xiong

    Read original paper ↗ ·

  41. Improving Alloy Mechanical Property Prediction by Addressing Data Homogeneity through Noise Augmentation

    Journal of Materials Engineering and Performance · 2026-05-03

    Yuhui Zhang, Dongfa Qiao, Yangbo Wu, Yanling Hu, Yurong Wu, Qinghang Wang

    Read original paper ↗ ·

  42. A survey of machine learning applications for Mg alloys: A promising prospect for current trends and future directions

    Solid State Communications · 2026-05-12

    Atwakyire Moses, Wang Xuefeng, Qiao Huang, Juanyun Li, Bin Wang, Yanming Huang, Behangana Albert, Usito Twine Bakesigakyi, Ezkihel T. Tefera, Ding Chen

    Read original paper ↗ ·

  43. A Precipitation Strengthening Parameter Driven Design Strategy for Aging Hardened Nickel-Based Corrosion-Resistant Alloys: Thermodynamics-Genetic Algorithm Integration

    Journal of Materials Engineering and Performance · 2026-05-15

    Yintian Gao, Yong Lian, Yingjie Sun, Rongrong Chen, Jin Zhang, Yinghu Wang, Qubo He, Dadi Zhou, Hengcan Yang

    Read original paper ↗ ·

  44. Computational strategies for the design of high-strength and high-thermal-conductivity casting Mg/Al alloys

    Journal of Materials Informatics · 2026-05-22

    Yunjian Chen, Ling Hu, Hongcan Chen, Kai Tang, Liu, Bin, 1960-, Yu Ling. Zhang, Bin Hu, Qun Luo, Qian Li

    Read original paper ↗ ·

  45. Machine learning for high-performance alloy design: A review

    Metals Advances · 2026-10-01

    Zinuo Wang, Qi Xu, Peng Zhou, Tao Zhang, Fuhui Wang

    Read original paper ↗ ·
Citation Atlas · A map of research, not a ranking.