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:

Phase classification of multi-principal element alloys via interpretable machine learning
Kyungtae Lee, Mukil V. Ayyasamy, Paige Delsa, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 57

Showing 1-25 of 57 citing articles:

Interpretable hardness prediction of high-entropy alloys through ensemble learning
Yifan Zhang, Wei Ren, Weili Wang, et al.
Journal of Alloys and Compounds (2023) Vol. 945, pp. 169329-169329
Closed Access | Times Cited: 43
From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design
Jorge Benavides-Hernández, Franck Dumeignil
ACS Catalysis (2024) Vol. 14, Iss. 15, pp. 11749-11779
Closed Access | Times Cited: 23
Machine Learning Paves the Way for High Entropy Compounds Exploration: Challenges, Progress, and Outlook
Xuhao Wan, Zeyuan Li, Wei Yu, et al.
Advanced Materials (2023)
Closed Access | Times Cited: 30
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
A comparative study of predicting high entropy alloy phase fractions with traditional machine learning and deep neural networks
Shusen Liu, Brandon Bocklund, James Diffenderfer, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 8
Mechanical property prediction and configuration effect exploration of particulate reinforced metal matrix composites via an interpretable deep learning approach
Xushun Chai, Yishi Su, Zichang Lin, et al.
Materials Science and Engineering A (2025), pp. 147880-147880
Closed Access | Times Cited: 1
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
Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning
Andy S. Anker, Emil T. S. Kjær, Mikkel Juelsholt, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 29
Prediction and design of high hardness high entropy alloy through machine learning
Wei Ren, Yifan Zhang, Weili Wang, et al.
Materials & Design (2023) Vol. 235, pp. 112454-112454
Open Access | Times Cited: 21
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
A comparison of explainable artificial intelligence methods in the phase classification of multi-principal element alloys
Kyungtae Lee, Mukil V. Ayyasamy, Yangfeng Ji, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 25
Distilling physical origins of hardness in multi-principal element alloys directly from ensemble neural network models
Dishant Beniwal, Preeti Singh, Shivam Gupta, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 24
Exploring the relationship between lattice distortion and phase stability in a multi-principal element alloy system based on machine learning method
Jiaxin Huang, Wei Fang, Congcong Xue, et al.
Computational Materials Science (2023) Vol. 221, pp. 112089-112089
Closed Access | Times Cited: 14
Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature
Mengwei Wu, Wei Yong, Cunqin Fu, et al.
International Journal of Minerals Metallurgy and Materials (2024) Vol. 31, Iss. 4, pp. 773-785
Closed Access | Times Cited: 5
Integrating machine learning with mechanistic models for predicting the yield strength of high entropy alloys
Shunshun Liu, Kyungtae Lee, Prasanna V. Balachandran
Journal of Applied Physics (2022) Vol. 132, Iss. 10
Open Access | Times Cited: 19
A Novel Hyperparameter Optimization Approach for Supervised Classification: Phase Prediction of Multi-Principal Element Alloys
Syed Hassan Fatimi, Zidong Wang, I.T.H. Chang, et al.
Cognitive Computation (2025) Vol. 17, Iss. 1
Open Access
Developing new high-entropy alloys with enhanced hardness using a hybrid machine learning approach: integrating interpretability and NSGA-II optimization
Debsundar Dey, Anik Pal, P. Biyani, et al.
Journal of Materials Science (2025)
Closed Access
Breaking through the trade-off between saturation magnetization and coercivity: a data-driven strategy
Peixin Liu, Hao Lü, Guojing Xu, et al.
Acta Materialia (2025), pp. 120945-120945
Closed Access
Elemental numerical descriptions to enhance classification and regression model performance for high-entropy alloys
Yan Zhang, Cheng Wen, Pengfei Dang, et al.
npj Computational Materials (2025) Vol. 11, Iss. 1
Open Access
Phase formation prediction in magnetron sputtered Cu(Ti)Zn thin films: Numerical vs experimental approaches
Dimitri Boivin, Andrea Jagodar, Pascal Brault, et al.
Journal of Applied Physics (2025) Vol. 137, Iss. 14
Open Access
Pgcnn: an interpretable graph convolutional neural network for predicting the mechanical properties of Ti-6Al-4V alloy
Zihao Gao, Changsheng Zhu, Yafeng Shu, et al.
Applied Intelligence (2025) Vol. 55, Iss. 7
Closed Access
A framework to predict binary liquidus by combining machine learning and CALPHAD assessments
Guillaume Deffrennes, Kei Terayama, Taichi Abe, et al.
Materials & Design (2023) Vol. 232, pp. 112111-112111
Open Access | Times Cited: 10
Phase Selection Rules of Multi‐Principal Element Alloys
Lin Wang, Bin Ouyang
Advanced Materials (2023) Vol. 36, Iss. 16
Closed Access | Times Cited: 10
Machine Learning-Based Classification, Interpretation, and Prediction of High-Entropy-Alloy Intermetallic Phases
Jie Qi, Diego Ibarra Hoyos, S. Joseph Poon
Deleted Journal (2023) Vol. 1, Iss. 2, pp. 312-326
Open Access | Times Cited: 9
On the application of high‐throughput experimentation and data‐driven approaches in metallic glasses
Weijie Xie, Weihua Wang, Yanhui Liu
Materials Genome Engineering Advances (2023) Vol. 1, Iss. 1
Open Access | Times Cited: 9
Page 1 - Next Page

Scroll to top