BetaCitation Atlas

RESEARCH PAPER · npj Computational Materials · 2025

Elemental numerical descriptions to enhance classification and regression model performance for high-entropy alloys

Read original paper ↗

See where this research leads.

11 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

Elemental numerical descriptions to enhance classification and regression model performance for high-entropy alloysOriginalLeveraging feature gradient for efficient acquisition function maximization in material composition design3AI Design for High Entropy Alloys: Progress, Challenges and Future Prospects16Enhancing Reverse Design Ability of Functional Materials Based on Data Quality Management: Taking Biomedical Zinc Alloy as an Example0Advanced energy systems and technologies0Machine learning-guided screening of phase-stable high-entropy Na cathodes to enable EVs and low-cost charging storage systems5Large Language Models for High-Entropy Alloys: Literature Mining, Design Orchestration, and Evaluation Standards2Mechanical properties optimization of Al-Cr-Co-Fe-Ni high-entropy alloys: An integrated high-throughput molecular dynamics and machine learning approach0Nanostructure strengthening strategies for high-entropy alloys: From microstructure design to outstanding performance3Domain-Specific Retrieval-Augmented Generation for Metallurgical R&D Knowledge Bases: A Hybrid Graph-Enhanced Approach0Phase-stability-guided explainable machine learning for mechanical-property prediction and experimentally supported candidate screening of high-entropy alloys0Interpretable machine learning identifies chemical factors associated with deweathered ozone pollution in Suzhou, Yangtze River Delta0

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

Browse Matching Papers

Papers that cite this work

11 direct citing papers saved.

  1. Leveraging feature gradient for efficient acquisition function maximization in material composition design

    Digital Discovery · 2025-01-01

    Yuehui Xian, Yunfan Wang, Pengfei Dang, Xinquan Wan, Yumei Zhou, Xiangdong Ding, Jun Sun, Dezhen Xue

    Read original paper ↗ ·
  2. AI Design for High Entropy Alloys: Progress, Challenges and Future Prospects

    Metals · 2025-09-11

    Enzhi Xie, Chao Yang

    Read original paper ↗ ·
  3. Enhancing Reverse Design Ability of Functional Materials Based on Data Quality Management: Taking Biomedical Zinc Alloy as an Example

    Materials · 2025-10-15

    Xujie Gong, Xue Feng Jiang, Shiyu Huang, Yize Wang, Lishen Ding, Yanjing Su, Yu Jing Yan

    Read original paper ↗ ·
  4. Advanced energy systems and technologies

    Elsevier eBooks · 2026-01-01

    G. Sriharan, Santhanam Harikrishnan, Hafiz Muhammad Ali

    Read original paper ↗ ·
  5. Machine learning-guided screening of phase-stable high-entropy Na cathodes to enable EVs and low-cost charging storage systems

    Journal of Materials Chemistry A · 2026-01-01

    Ahhyun Jeong, Seungpyo Kang, Taeseo Ko, JunHo Song, Yerim Jung, Woojin Shin, Jiwon Sun, Kyoungmin Min

    Read original paper ↗ ·
  6. Large Language Models for High-Entropy Alloys: Literature Mining, Design Orchestration, and Evaluation Standards

    Metals · 2026-01-29

    Yutong GUO, Chao Yang

    Read original paper ↗ ·
  7. Mechanical properties optimization of Al-Cr-Co-Fe-Ni high-entropy alloys: An integrated high-throughput molecular dynamics and machine learning approach

    Intermetallics · 2026-06-30

    Zijian He, Yi Zhao, D P Wang, Xuan Liu, Yiheng Chao, Yan Zhong, Chaohao Hu, Kecheng Lu, Jinning Huang, Z Y Chen, Z H Wang

    Read original paper ↗ ·
  8. Nanostructure strengthening strategies for high-entropy alloys: From microstructure design to outstanding performance

    International Journal of Minerals Metallurgy and Materials · 2026-08-01

    Li Feng, Gang Qin, Xu Yang, Liang Wang, Yanqing Su, Ruirun Chen

    Read original paper ↗ ·
  9. Domain-Specific Retrieval-Augmented Generation for Metallurgical R&D Knowledge Bases: A Hybrid Graph-Enhanced Approach

    Data · 2026-08-27

    Viktor A. Vedeneev, Viktor V. Kondratiev, А. Н. Назарычев, Roman V. Kononenko, A S Govorkov, Vitaliy A. Gladkikh, Yu I Karlina, Antonina I. Karlina

    Read original paper ↗ ·
  10. Phase-stability-guided explainable machine learning for mechanical-property prediction and experimentally supported candidate screening of high-entropy alloys

    Frontiers in Materials · 2026-09-14

    Derong Yuan, Derong Yuan, Delin Yuan, Delin Yuan, Xiaoyu Xu

    Read original paper ↗ ·
  11. Interpretable machine learning identifies chemical factors associated with deweathered ozone pollution in Suzhou, Yangtze River Delta

    Environmental Pollution · 2026-09-29

    Yirun Wu, Yuezhi Zhong, Hui Zhang, Lei Liu, Xin Zhang, Shanshan Tang, Hongliang Zhang, Lin Du

    Read original paper ↗ ·