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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Customized development of promising Cu-Cr-Ni-Co-Si alloys enabled by integrated machine learning and characterization
Shaobin Pan, Jinxin Yu, Jiajia Han, et al.
Acta Materialia (2022) Vol. 243, pp. 118484-118484
Closed Access | Times Cited: 25

Showing 25 citing articles:

Precipitation behavior of a new Cu-Ni-Co-Si-Fe-Mg alloy designed with low cobalt content
Liuxin Qin, Tao Zhou, Xu Wang, et al.
Materials & Design (2024) Vol. 240, pp. 112833-112833
Open Access | Times Cited: 16
Toward ultra-high strength high entropy alloys via feature engineering
Yan Zhang, Cheng Wen, Pengfei Dang, et al.
Journal of Material Science and Technology (2024) Vol. 200, pp. 243-252
Closed Access | Times Cited: 12
Developing softening-resistant Cu-Cr alloys and understanding their mechanisms via mechanism-informed interpretable machine learning
Muzhi Ma, Zhou Li, Yuyuan Zhao, et al.
Journal of Material Science and Technology (2025)
Closed Access | Times Cited: 1
Influence of trace silicon addition on the strengthening precipitates, mechanical properties and stress relaxation resistance of Cu-Cr alloy
Yibo Du, Yanjun Zhou, Kexing Song, et al.
Journal of Alloys and Compounds (2023) Vol. 948, pp. 169619-169619
Closed Access | Times Cited: 15
Coarsening mechanism of over-aged δ-Ni2Si nanoscale precipitates in Cu-Ni-Si-Cr-Mg alloy
Ying-Huan Li, Shuai-Feng Chen, Song-Wei Wang, et al.
Materials Characterization (2024), pp. 113693-113693
Closed Access | Times Cited: 5
Coupling strengthening mechanism of precipitate phases and sub-grain boundaries in the aging heat treatment of deformed Cu-Ni-Co-Si alloy
Xuetong Zhu, Huiqin Chen, Yong Hu, et al.
Journal of Alloys and Compounds (2025), pp. 179292-179292
Closed Access
Machine Learning Assisted Design of High Thermal Conductivity and High Strength Mg Alloys
Huafeng Liu, T. Nakata, Chao Xu, et al.
Metallurgical and Materials Transactions A (2025)
Closed Access
A multi‐objective feature optimization strategy for developing high‐entropy alloys with optimal strength and ductility
Yan Zhang, Shewei Xin, Wei Zhou, et al.
Materials Genome Engineering Advances (2025)
Open Access
Precipitation evolution and hardening in a Cu-1.5Ni-0.25P alloy during isothermal aging
Huiming Chen, S.N. Chen, Zhen Han, et al.
Journal of Alloys and Compounds (2025), pp. 179979-179979
Closed Access
Tunable phase structure via silicon addition to achieve high strength and high conductivity Cu-Zr-Si alloy
Jingjing Pang, Huai Zhang, Qi Fang, et al.
Journal of Alloys and Compounds (2025), pp. 180121-180121
Closed Access
Machine Learning-Driven Design of Rare Metal Doped Niobium Alloys with Enhanced Strength and Ductility
Z. Xiong, ZhaoKun Song, Jianwei Li, et al.
Journal of Materials Research and Technology (2025)
Open Access
Accelerated development of high-strength and high-conductivity Cu-Cr-Ti alloys based on data-driven design and experimental validation
Feng Li, Jiangnan Li, Qiong Lu, et al.
Materials & Design (2025), pp. 113948-113948
Open Access
Synergistic effects of trace silicon, calcium and cerium on the microstructure and properties of a novel Cu–Cr–Nb–Si–Ca–Ce alloy
Hanyu Cai, Weiqiang Lv, Erdi Pan, et al.
Materials Science and Engineering A (2024) Vol. 898, pp. 146419-146419
Closed Access | Times Cited: 4
The comprehensive performance and strengthening mechanism of the columnar crystal Cu-Ni-Si alloy after two large deformation rates of cryogenic rolling-aging
Wanneng Liao, Chenxing Zhang, Hui Qiang, et al.
Journal of Alloys and Compounds (2022) Vol. 936, pp. 168281-168281
Closed Access | Times Cited: 13
Prediction and Rational Design of Stacking Fault Energy of Austenitic Alloys Based on Interpretable Machine Learning and Chemical Composition
Chengcheng Liu, Hang Su
steel research international (2024) Vol. 95, Iss. 8
Closed Access | Times Cited: 2
Microstructure evolution and composite strengthening mechanism during solid solution and aging treatment for Cu-Ni-Co-Si sheet
Xuetong Zhu, Huiqin Chen, Zhonghua Zhang, et al.
Materials Today Communications (2024) Vol. 40, pp. 109707-109707
Closed Access | Times Cited: 2
Predicting the Hall-Petch slope of magnesium alloys by machine learning
Bo Guan, Chao Chen, Yunchang Xin, et al.
Journal of Magnesium and Alloys (2023) Vol. 12, Iss. 11, pp. 4436-4442
Open Access | Times Cited: 6
Predicting the Average Composition of an AlFeNiTiVZr-Cr Alloy with Machine Learning and X-ray Spectroscopy
Tarik Sadat
Compounds (2023) Vol. 3, Iss. 1, pp. 224-232
Open Access | Times Cited: 5
Alloying element distributions of precipitates in Cu–Cr alloys aided by machine learning
Haipeng Jin, Hang Wang, Xin Wang, et al.
Materials Today Communications (2023) Vol. 36, pp. 106612-106612
Closed Access | Times Cited: 4
Accelerated composition-process-properties design of precipitation-strengthened copper alloys using machine learning based on Bayesian optimization
Longjian Li, Jinchuan Jie, Xiaoyu Guo, et al.
Materials Research Letters (2024), pp. 1-9
Open Access | Times Cited: 1
Intelligent Design of High Strength and High Conductivity Copper Alloys Using Machine Learning Assisted by Genetic Algorithm
Parth Khandelwal, I. Manna
Computers, materials & continua/Computers, materials & continua (Print) (2024) Vol. 79, Iss. 1, pp. 1727-1755
Open Access
Amorphous Oxide Dispersion Strengthened Cu-316l-Mo Immiscible Composites with Enhanced Strength-Ductility Synergy by In-Situ Laser Powder Bed Fusion
Chongyu Wen, Huan Yang, Baisong Guo, et al.
(2024)
Closed Access
Accelerating the design of multivariate Nb-Si based alloys with excellent strength and toughness matching through machine learning
Xiuyuan Yin, Jing Liang, Chen Liu, et al.
Next Materials (2024) Vol. 6, pp. 100294-100294
Closed Access
Investigation of the Softening Resistance Behavior and Its Mechanism in Cu-Ni-Si Alloys with Discontinuous Precipitates
Yicheng Cao, Wei Luo, Zhen Yang, et al.
Materials (2024) Vol. 17, Iss. 21, pp. 5323-5323
Open Access
Advances in machine learning methods in copper alloys: a review
Yingfan Zhang, Shue Dang, Huiqin Chen, et al.
Journal of Molecular Modeling (2024) Vol. 30, Iss. 12
Closed Access
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