Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Deep learning-based phase prediction of high-entropy alloys: Optimization, generation, and explanation
Soo Young Lee, Seokyeong Byeon, Hyoung Seop Kim, et al.
Materials & Design (2020) Vol. 197, pp. 109260-109260
Open Access | Times Cited: 153

Showing 26-50 of 153 citing articles:

Inverse machine learning framework for optimizing gradient honeycomb structure under impact loading
Xingyu Shen, Ke Yan, Difeng Zhu, et al.
Engineering Structures (2024) Vol. 309, pp. 118079-118079
Closed Access | Times Cited: 9
Recent progress in high-entropy alloys for laser powder bed fusion: Design, processing, microstructure, and performance
Asker Jarlöv, Zhiguang Zhu, Weiming Ji, et al.
Materials Science and Engineering R Reports (2024) Vol. 161, pp. 100834-100834
Closed Access | Times Cited: 9
Machine learning studies for magnetic compositionally complex alloys: A critical review
Xin Li, C.H. Shek, Peter K. Liaw, et al.
Progress in Materials Science (2024) Vol. 146, pp. 101332-101332
Closed Access | Times Cited: 8
Machine learning-enabled identification of new medium to high entropy alloys with solid solution phases
Ujjawal Kumar Jaiswal, Yegi Vamsi Krishna, M.R. Rahul, et al.
Computational Materials Science (2021) Vol. 197, pp. 110623-110623
Closed Access | Times Cited: 44
In situ monitoring and penetration prediction of plasma arc welding based on welder intelligence-enhanced deep random forest fusion
Di Wu, Minghua Hu, Yiming Huang, et al.
Journal of Manufacturing Processes (2021) Vol. 66, pp. 153-165
Closed Access | Times Cited: 42
A Generative Approach to Materials Discovery, Design, and Optimization
Dhruv Menon, Raghavan Ranganathan
ACS Omega (2022) Vol. 7, Iss. 30, pp. 25958-25973
Open Access | Times Cited: 36
Overview: recent studies of machine learning in phase prediction of high entropy alloys
Yonggang Yan, Dan Lü, Kun Wang
Tungsten (2022) Vol. 5, Iss. 1, pp. 32-49
Closed Access | Times Cited: 31
A two-step data augmentation method based on generative adversarial network for hardness prediction of high entropy alloy
Zhiyuan Yang, Shu Li, Shuai Li, et al.
Computational Materials Science (2023) Vol. 220, pp. 112064-112064
Closed Access | Times Cited: 21
Interpretable machine learning-based analysis of mechanical properties of extruded Mg-Al-Zn-Mn-Ca-Y alloys
Joung Sik Suh, Young Min Kim, Chang Dong Yim, et al.
Journal of Alloys and Compounds (2023) Vol. 968, pp. 172007-172007
Closed Access | Times Cited: 18
Deep learning modeling in microscopy imaging: A review of materials science applications
Marco Ragone, Reza Shahabazian-Yassar, Farzad Mashayek, et al.
Progress in Materials Science (2023) Vol. 138, pp. 101165-101165
Open Access | Times Cited: 17
Optimization of process parameters of selective laser melted nickel-based superalloy for densification by random forest regression algorithm and response surface methodology
Hsiang-Tse Chung, Chin-Cheng Tsai, Kuo‐Kuang Jen, et al.
Results in Engineering (2024) Vol. 22, pp. 102182-102182
Open Access | Times Cited: 7
Accelerating high-entropy alloy discovery: efficient exploration via active learning
Gloria A Sulley, Jonathan Raush, M. M. Montemore, et al.
Scripta Materialia (2024) Vol. 249, pp. 116180-116180
Open Access | Times Cited: 7
Unraveling phase prediction in high entropy alloys: A synergy of machine learning, deep learning, and ThermoCalc, validation by experimental analysis
Mokali Veeresham, Narayanaswamy Sake, Unhae Lee, et al.
Journal of Materials Research and Technology (2024) Vol. 29, pp. 1744-1755
Open Access | Times Cited: 6
Applications of generative adversarial networks in materials science
Yuan Jiang, Jinshan Li, Xiang Lin Yang, et al.
Materials Genome Engineering Advances (2024) Vol. 2, Iss. 1
Open Access | Times Cited: 6
Deep alloys: Metal materials empowered by deep learning
Kaiyuan Zheng, Zhongping He, Lun Che, et al.
Materials Science in Semiconductor Processing (2024) Vol. 179, pp. 108514-108514
Closed Access | Times Cited: 6
NSGAN: a non-dominant sorting optimisation-based generative adversarial design framework for alloy discovery
Ziyuan Li, N. Birbilis
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 6
Machine learning-assisted design of high-entropy alloys with superior mechanical properties
Jianye He, Zezhou Li, Pingluo Zhao, et al.
Journal of Materials Research and Technology (2024) Vol. 33, pp. 260-286
Open Access | Times Cited: 6
Emergence of machine learning in the development of high entropy alloy and their prospects in advanced engineering applications
Nirmal Kumar Katiyar, Gaurav Goel, Saurav Goel
Emergent Materials (2021) Vol. 4, Iss. 6, pp. 1635-1648
Open Access | Times Cited: 38
Quantitative analysis of mechanical properties associated with aging treatment and microstructure in Mg-Al-Zn alloys through machine learning
Joung Sik Suh, Byeong‐Chan Suh, Sang Eun Lee, et al.
Journal of Material Science and Technology (2021) Vol. 107, pp. 52-63
Closed Access | Times Cited: 37
A machine learning–based classification approach for phase diagram prediction
Guillaume Deffrennes, Kei Terayama, Taichi Abe, et al.
Materials & Design (2022) Vol. 215, pp. 110497-110497
Open Access | Times Cited: 27
Prediction of phase and hardness of HEAs based on constituent elements using machine learning models
Mahmoud Bakr, Junaidi Syarif, Mohamed Hashem
Materials Today Communications (2022) Vol. 31, pp. 103407-103407
Closed Access | Times Cited: 25
Machine Learning-Based Hardness Prediction of High-Entropy Alloys for Laser Additive Manufacturing
Wenhan Zhu, Wenyi Huo, Shiqi Wang, et al.
JOM (2023) Vol. 75, Iss. 12, pp. 5537-5548
Open Access | Times Cited: 14
Improving Hardenability Modeling: A Bayesian Optimization Approach to Tuning Hyperparameters for Neural Network Regression
Wendimu Fanta Gemechu, W. Sitek, Gilmar Ferreira Batalha
Applied Sciences (2024) Vol. 14, Iss. 6, pp. 2554-2554
Open Access | Times Cited: 5
Machine learning-assisted design of refractory high-entropy alloys with targeted yield strength and fracture strain
Jianye He, Zezhou Li, Jingchen Lin, et al.
Materials & Design (2024), pp. 113326-113326
Open Access | Times Cited: 5
Influence of mechanical alloying on structural, thermal, and magnetic properties of Fe50Ni10Co10Ti10B20 high entropy soft magnetic alloy
Mrutyunjay Panigrahi, Barış Avar
Journal of Materials Science Materials in Electronics (2021) Vol. 32, Iss. 16, pp. 21124-21134
Closed Access | Times Cited: 29

Scroll to top