High-throughput studies and machine learning for design of β titanium alloys with optimum properties

Papers that cite this work

12 direct citing papers saved.

1. Design of new β-type Ti alloys with outstanding corrosion and wear resistance for dental application

   Corrosion Science · 2024-10-18

   Hao-qin LIN, Guangxu Zhang, Weimin Chen, Kaihong Zheng, Fuxing Yin

   Read original paper ↗ · Show in map

2. High-throughput exploration of composition-dependent mechanical and diffusion properties of Ti-Al-V-Cr alloys

   Journal of Alloys and Compounds · 2025-03-10

   Hongyu Zhang, Hongyu Zhang, Ping Ding, Fali Liu, Fali Liu, Zhe Yuan, Weimin Bai, Yuan Yuan, Maohua Rong, Jiang Wang, Ligang Zhang, Libin Liu

   Read original paper ↗ · Show in map

3. Optimal structural characteristics of osteoinductivity in bioceramics derived from a novel high-throughput screening plus machine learning approach

   Biomaterials · 2025-04-17

   Yunyi Liu, Quanle Cao, Shengyi Yong, Jing Wang, Jing Wang, Xuening Chen, Yumei Xiao, Jiangli Lin, Mingli Yang, Mingli Yang, Xiangfeng Li, Xiangdong Zhu, Xingdong Zhang

   Read original paper ↗ · Show in map

4. A machine learning multitasking integrated framework and its derived [Mo]eq-[Fe]eq map for designing TRIP titanium alloys

   Materials & Design · 2025-12-01

   Zhiduo Liu, Haoyu Zhang, Linfan Sun, Chuan Wang, Ge Zhou, Ximin Zang, Lijia Chen

   Read original paper ↗ · Show in map

5. Structure-function-integrated microwave-sintered SiC/Ti-Cu composites: exploring optimal nano-SiC content on microstructural, mechanical, tribological, electrochemical, antibacterial, and biological properties

   Chemical Engineering Journal · 2025-12-06

   Xin Li, Ming‐Chun Zhao, Zhiyu Ding, Dengfeng Yin, Cuié Wen, Andrej Atrens, Jinsong Li

   Read original paper ↗ · Show in map

6. High-throughput investigations on mechanical properties of Co–Cr–Ni–W system via diffusion couple technique and machine learning method

   Journal of Alloys and Compounds · 2026-01-01

   G. A. Zhang, Jingang Qi, Weimin Chen, Qiang Zhang, Liying Sun, Kaihong Zheng, Fuxing Yin

   Read original paper ↗ · Show in map

7. Computational modeling and interpretability analysis of manufacturing parameters in titanium matrix composites

   Computational Materials Science · 2026-03-21

   Qichao Wei, Hongmei Zhang, Xingwang Cheng, Pingluo Zhao, Qunbo Fan, Xiaonan Mu, Yu Wang, H. M. Wang, Jiaqi Zhang

   Read original paper ↗ · Show in map

8. Machine learning for titanium alloy development: Composition design, process optimization, microstructure informatics, and performance prediction

   Journal of Materials Research and Technology · 2026-08-17

   Chenxuan Liu, Mingxuan Ling, Chen Luo, Yujie Cui

   Read original paper ↗ · Show in map

9. Research progress on advanced titanium-molybdenum alloy design driven by machine learning

   Journal of Alloys and Compounds · 2026-09-01

   Shuai Ji, Jinhang Bai, Quanyin Meng

   Read original paper ↗ · Show in map

10. CALPHAD-type model aided elastic properties screening across the full compositional space of BCC Mo-Nb-ta-Ti alloys

    International Journal of Refractory Metals and Hard Materials · 2026-09-08

    Mingqing Liao, Haoxin Jiang, Nan Qu, Fengjiang Wang

    Read original paper ↗ · Show in map

11. Low-cost and performance-balanced Cu-Ni-Cr-Si copper strips designed with machine learning and fabricated with short processing route

    Materials & Design · 2026-09-16

    Shuang Zhou, Qian Lei, Jie Yin, Deyao Qin, Hailong Hu, 范景莲, Yanbin Jiang, Zhou Li

    Read original paper ↗ · Show in map

12. AI-assisted quantification of osteogenesis in X-ray microscopy images of biodegradable Mg implants: methodology, benchmarking, and open dataset

    Journal of Magnesium and Alloys · 2026-09-23

    Xingyu Gao, Wenxiao Wang, Juan Xie, Xiaonong Zhang, Wenhui Wang

    Read original paper ↗ · Show in map