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RESEARCH PAPER · Materials & Design · 2021

Study on strengthening effects of Zr-Ti-Nb-O alloys via high throughput powder metallurgy and data-driven machine learning

Shengping Si, Bojian Fan, Xuemei Liu, Tian Zhou, Chuan He, Dandan Song, Jinxu Liu

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Study on strengthening effects of Zr-Ti-Nb-O alloys via high throughput powder metallurgy and data-driven machine learningOriginalPrediction of Ti-Zr-Nb-Ta high-entropy alloys with desirable hardness by combining machine learning and experimental data48Influence of impact velocity on impact-initiated reaction behavior of Zr-Ti-Nb alloy42Establishing reduced-order process-structure linkages from phase field simulations of dendritic grain growth during solidification6Effect of scanning strategies on the microstructure and mechanical properties of Ti–15Mo alloy fabricated by selective laser melting37Industrial big data-driven mechanical performance prediction for hot-rolling steel using lower upper bound estimation method82Study on the key features of dynamic compressive fracture strain of Ti-Zr-Nb solid solution alloys through random forest regressor2High-throughput characterization of elastic moduli of Ti-Nb-Zr-O biomedical alloys fabricated by field-assisted sintering technique12Review on surface-characterization applications of X-ray photoelectron spectroscopy (XPS): Recent developments and challenges751Physics-Constrained Neural Networks for Predicting Phase Transformation Pathways in Multi-Component Alloys0Accelerated discovery of high-performance Al-Si-Mg-Sc casting alloys by integrating active learning with high-throughput CALPHAD calculations40Data-driven discovery of a formation prediction rule on high-entropy ceramics42Material Property Prediction by Integrating Literature-mining and Crossed Feature Selection0A literature-mining method of integrating text and table extraction for materials science publications15Phase prediction for high-entropy alloys using generative adversarial network and active learning based on small datasets20Descriptors for phase prediction of high entropy alloys using interpretable machine learning36Feasibility of Powder‐Based High‐Throughput Synthesis for Ceramics Development: Case Study on the Influence of Calcination Temperature in BiFeO 3 ‐BaTiO 36High-throughput and data-driven machine learning techniques for discovering high-entropy alloys77Enhanced ductility of PM Ti-Nb-Ta alloys via reducing solution oxygen by YH2 addition3Multi-objective optimisation and verification of creep-resistant Ni-base superalloy for electron-beam powder-bed-fusion7Recent progress of top-performing electrocatalytic materials for water oxidation and recent machine learning edge: An overview upto 20242From Pairwise Comparisons of Complex Behavior to an Overall Performance Rank: A Novel Alloy Design Strategy0Influence of the mechanical properties of the reactive Zr alloy liner on the multiple damaged effects post-target4Revealing the Influence of Material Properties of Shaped Charge Liner on Penetration Performance via Numerical Simulation and Machine Learning4Machine Learning Enabled Estimation of Formability for Anisotropic Sheet Metals10Sintering-induced phase evolution and its influence on compressive strength, elastic modulus and bioactivity of Ti-Nb-CaSiO3 composites0Application of machine learning methods in the metallurgical industry: A bibliometric analysis and systematic literature review0Predicting dislocation patterns and discovering the law of similitude: Machine learning based on fully reversed fatigue of FCC metals3Numerical Simulation Study on Region Tracking of Jet Formation and Armor-Piercing Process of Zirconium Alloy Shaped Charge Liner0Machine learning-aided material selection, parameter optimization, and process monitoring in laser powder bed fusion and laser directed energy deposition with powder feed0

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  1. Prediction of Ti-Zr-Nb-Ta high-entropy alloys with desirable hardness by combining machine learning and experimental data

    Applied Physics Letters · 2021-11-15

    Yan Hui Sun, Zhichao Lu, Zhichao Lu, Xiongjun Liu, Qing Du, Huamin Xie, Jiecheng Lv, Ruoxuan Song, Yuan Wu, Hui Wang, Suihe Jiang, Zhaoping Lu, Zhaoping Lu

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  2. Influence of impact velocity on impact-initiated reaction behavior of Zr-Ti-Nb alloy

    Materials & Design · 2022-06-13

    Shengping Si, Chuan He, Shuang Liu, Bojian Fan, Ruyue Xie, Xinying Xue, Jinxu Liu

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  3. Establishing reduced-order process-structure linkages from phase field simulations of dendritic grain growth during solidification

    Computational Materials Science · 2022-08-03

    Jiajun Zhao, Junjie Li, Xiaobing Hu, Yujian Wang, Yiming Chen, Feng He, Zhijun Wang, Zhanglong Zhao, Jincheng Wang

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  4. Effect of scanning strategies on the microstructure and mechanical properties of Ti–15Mo alloy fabricated by selective laser melting

    Vacuum · 2022-09-05

    Libo Zhou, Jinshan Sun, Xisheng Bi, Jian Chen, Wei Chen, Yanjie Ren, Yan Niu, Cong Li, Wei Qiu, Tiechui Yuan

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  5. Industrial big data-driven mechanical performance prediction for hot-rolling steel using lower upper bound estimation method

    Journal of Manufacturing Systems · 2022-09-12

    Gongzhuang Peng, Yinliang Cheng, Yufei Zhang, Jian Shao, Hongwei Wang, Weiming Shen

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  6. 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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  7. High-throughput characterization of elastic moduli of Ti-Nb-Zr-O biomedical alloys fabricated by field-assisted sintering technique

    Journal of Alloys and Compounds · 2022-10-20

    Dalibor Preisler, Michaela Janovská, Hanuš S. Seiner, Lucie Bodnárová, Jitka Nejezchlebová, Martin Koller, Petr Sedlák, Petr Harcuba, Jozef Veselý, Jiří Kozlík, Tomáš Chráska, Josef Stráský, Miloš Janeček

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  8. Review on surface-characterization applications of X-ray photoelectron spectroscopy (XPS): Recent developments and challenges

    Applied Surface Science Advances · 2022-11-14

    D. Nanda Gopala Krishna, John Philip

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  9. Physics-Constrained Neural Networks for Predicting Phase Transformation Pathways in Multi-Component Alloys

    Journal of Artificial Intelligence for Materials Science · 2023-01-01

    Wei Chen, Li Zhang, Rui Zhao

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  10. 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

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  11. Data-driven discovery of a formation prediction rule on high-entropy ceramics

    Acta Materialia · 2023-04-30

    Yonggang Yan, Zongrui Pei, Michael C. Gao, Scott T. Misture, Kun Wang

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  12. Material Property Prediction by Integrating Literature-mining and Crossed Feature Selection

    International Conference on Information Systems Engineering (ICISE) · 2023-06-23

    Rui Zhang, Jiawang Zhang, Yuexing Han, Qiaochuan Chen

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  13. A literature-mining method of integrating text and table extraction for materials science publications

    Computational Materials Science · 2023-08-31

    Rui Zhang, Jiawang Zhang, Qiaochuan Chen, Bing Wang, Yi Liu, Quan Qian, Deng Pan, Jinhua Xia, Yinggang Wang, Yuexing Han

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  14. Phase prediction for high-entropy alloys using generative adversarial network and active learning based on small datasets

    Science China Technological Sciences · 2023-11-24

    Cun Chen, HengRu Zhou, Weimin Long, Gang Wang, Jingli Ren

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  15. Descriptors for phase prediction of high entropy alloys using interpretable machine learning

    Journal of Materials Chemistry A · 2023-12-28

    Shang Zhao, Ruihao Yuan, Weijie Liao, Yatong Zhao, Jun Wang, Jinshan Li, Turab Lookman

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  16. Feasibility of Powder‐Based High‐Throughput Synthesis for Ceramics Development: Case Study on the Influence of Calcination Temperature in BiFeO 3 ‐BaTiO 3

    Advanced Engineering Materials · 2024-02-15

    Udo R. Eckstein, Michel Kuhfuß, Tobias Fey, Kyle Grant Webber

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  17. High-throughput and data-driven machine learning techniques for discovering high-entropy alloys

    Communications Materials · 2024-05-17

    Lu Zhichao, Dong Ma, Xiongjun Liu, Zhaoping Lü

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  18. Enhanced ductility of PM Ti-Nb-Ta alloys via reducing solution oxygen by YH2 addition

    Journal of Alloys and Compounds · 2024-06-19

    Xifeng Yan, Jinxu Liu, Xuemei Liu, Yan Wang, Jiang Chen

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  19. Multi-objective optimisation and verification of creep-resistant Ni-base superalloy for electron-beam powder-bed-fusion

    Journal of Material Science and Technology · 2024-10-16

    Shen Tao, Yansong Li, Hui Fen Peng, Hongbo Guo, Bo Chen

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  20. Recent progress of top-performing electrocatalytic materials for water oxidation and recent machine learning edge: An overview upto 2024

    Journal of Industrial and Engineering Chemistry · 2024-12-05

    Jayaraman Jayabharathi, Venugopal Thanikachalam, Balakrishnan Karthikeyan, Muthukumaran Sangamithirai, Murugan Vijayarangan

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  21. From Pairwise Comparisons of Complex Behavior to an Overall Performance Rank: A Novel Alloy Design Strategy

    Metals · 2024-12-10

    Rafael Herschberg, Lisa Rateau, Laure Martinelli, Fanny Balbaud‐Célérier, Jean Dhers, Anna Fraczkiewicz, Gérard Ramstein, Franck Tancret

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  22. Influence of the mechanical properties of the reactive Zr alloy liner on the multiple damaged effects post-target

    Journal of Materials Research and Technology · 2025-06-05

    Xinying Xue, Xuemei Liu, Shuang Liu, Jie Cao, Jinxu Liu

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  23. Revealing the Influence of Material Properties of Shaped Charge Liner on Penetration Performance via Numerical Simulation and Machine Learning

    Materials · 2025-06-11

    Yan Wang, Jinxu Liu, Xuemei Liu, Xinya Feng, Yifan Du, Jie Cao

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  24. Machine Learning Enabled Estimation of Formability for Anisotropic Sheet Metals

    Metals and Materials International · 2025-06-19

    Abdul Samad, Ankit Kumar Thakur, Shamik Basak, Kaushik Pal

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  25. Sintering-induced phase evolution and its influence on compressive strength, elastic modulus and bioactivity of Ti-Nb-CaSiO3 composites

    Journal of Alloys and Metallurgical Systems · 2025-07-31

    Nur Atiqah Ideris, Zuhailawati Hussain, Ahmad Farrahnoor

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  26. Application of machine learning methods in the metallurgical industry: A bibliometric analysis and systematic literature review

    Mineral Processing and Extractive Metallurgy Transactions of the Institutions of Mining and Metallurgy · 2025-09-08

    Marco Antonio Cotrina Teatino, Jairo Jhonatan Marquina Araujo, Jose Nestor Mamani-Quispe, Solio Marino Arango-Retamozo, Hans Roger Portilla Rodríguez, Flavio A Lezcano-Montoya, Jorge A Woolcott-Honores

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  27. Predicting dislocation patterns and discovering the law of similitude: Machine learning based on fully reversed fatigue of FCC metals

    International Journal of Plasticity · 2025-10-29

    Ronghai Wu, Lei Zeng, Zanpeng Shangguan, Yuxin Zhang, Zichao Peng, Xu‐Qing Wang, Heng Li

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  28. Numerical Simulation Study on Region Tracking of Jet Formation and Armor-Piercing Process of Zirconium Alloy Shaped Charge Liner

    Technologies · 2026-04-08

    Yan Wang, Yifan Du, Xingwei Liu, Jianxing Liu

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  29. Machine learning-aided material selection, parameter optimization, and process monitoring in laser powder bed fusion and laser directed energy deposition with powder feed

    Journal of Manufacturing Systems · 2026-07-01

    Jiahui Zhang, Zhiquan Yang, Andrew Smith, Ajay Talbot, Zhiying Liu, Xiao Shang, S.S. Dash, Yu Zou

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