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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:

A critical examination of robustness and generalizability of machine learning prediction of materials properties
Kangming Li, Brian DeCost, Kamal Choudhary, et al.
npj Computational Materials (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 54

Showing 1-25 of 54 citing articles:

In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science
Joshua Schrier, Alexander J. Norquist, Tonio Buonassisi, et al.
Journal of the American Chemical Society (2023) Vol. 145, Iss. 40, pp. 21699-21716
Open Access | Times Cited: 46
Uranium and lithium extraction from seawater: challenges and opportunities for a sustainable energy future
Yu Jie Lim, Kunli Goh, Atsushi Goto, et al.
Journal of Materials Chemistry A (2023) Vol. 11, Iss. 42, pp. 22551-22589
Closed Access | Times Cited: 46
Exploiting redundancy in large materials datasets for efficient machine learning with less data
Kangming Li, Daniel Persaud, Kamal Choudhary, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 44
Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning
Hajime Shimakawa, Akiko Kumada, Masahiro Sato
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 18
ET-AL: Entropy-targeted active learning for bias mitigation in materials data
James M. Rondinelli, Wei Chen
Applied Physics Reviews (2023) Vol. 10, Iss. 2
Open Access | Times Cited: 23
JARVIS-Leaderboard: a large scale benchmark of materials design methods
Kamal Choudhary, Daniel Wines, Kangming Li, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 14
Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study
Sadman Sadeed Omee, Nihang Fu, Rongzhi Dong, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 14
Quantitative predictive theories through integrating quantum, statistical, equilibrium, and nonequilibrium thermodynamics
Zi‐Kui Liu
Journal of Physics Condensed Matter (2024) Vol. 36, Iss. 34, pp. 343003-343003
Open Access | Times Cited: 11
Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials’ Surfaces
Bruno Focassio, Luis Paulo Mezzina Freitas, Gabriel R. Schleder
ACS Applied Materials & Interfaces (2024)
Open Access | Times Cited: 11
Optimization and prediction of dye adsorption utilising cross-linked chitosan-activated charcoal: Response Surface Methodology and machine learning
Arun Kumar Shukla, Javed Alam, Santanu Mallik, et al.
Journal of Molecular Liquids (2024) Vol. 411, pp. 125745-125745
Closed Access | Times Cited: 9
Probing out-of-distribution generalization in machine learning for materials
Kangming Li, Andre Niyongabo Rubungo, X. L. Lei, et al.
Communications Materials (2025) Vol. 6, Iss. 1
Open Access | Times Cited: 1
Machine learning in materials research: Developments over the last decade and challenges for the future
Anubhav Jain
Current Opinion in Solid State and Materials Science (2024) Vol. 33, pp. 101189-101189
Open Access | Times Cited: 7
Extrapolation validation (EV): a universal validation method for mitigating machine learning extrapolation risk
Mengxian Yu, Yin‐Ning Zhou, Qiang Wang, et al.
Digital Discovery (2024) Vol. 3, Iss. 5, pp. 1058-1067
Open Access | Times Cited: 6
Unveiling yield strength of metallic materials using physics-enhanced machine learning under diverse experimental conditions
Jeong‐Ah Lee, Roberto B. Figueiredo, Hyojin Park, et al.
Acta Materialia (2024) Vol. 275, pp. 120046-120046
Closed Access | Times Cited: 6
Generalization Across Experimental Parameters in Neural Network Analysis of High-Resolution Transmission Electron Microscopy Datasets
Katherine Sytwu, Luis Rangel DaCosta, Mary Scott
Microscopy and Microanalysis (2024) Vol. 30, Iss. 1, pp. 85-95
Open Access | Times Cited: 5
Efficient first principles based modeling via machine learning: from simple representations to high entropy materials
Kangming Li, Kamal Choudhary, Brian DeCost, et al.
Journal of Materials Chemistry A (2024) Vol. 12, Iss. 21, pp. 12412-12422
Open Access | Times Cited: 5
Predicting thermodynamic stability of inorganic compounds using ensemble machine learning based on electron configuration
Hao Zou, Haochen Zhao, Mingming Lu, et al.
Nature Communications (2025) Vol. 16, Iss. 1
Open Access
AI-empowered digital design of zeolites: Progress, challenges, and perspectives
Mengfan Wu, Shiyi Zhang, Jie Ren
APL Materials (2025) Vol. 13, Iss. 2
Open Access
Improved hardness prediction for reduced-activation high-entropy alloys by incorporating symbolic regression and domain adaptation on small datasets
Hao Pan, Mingjie Zheng, Xiaochen Li, et al.
Journal of Materials Informatics (2025) Vol. 5, Iss. 1
Open Access
Foundation models for materials discovery – current state and future directions
Edward O. Pyzer‐Knapp, Matteo Manica, Peter Staar, et al.
npj Computational Materials (2025) Vol. 11, Iss. 1
Open Access
Machine Learning to Adaptively Predict Gold Nanorod Sizes on Different Substrates
Katsuya Shiratori, Claire A. West, Zhenyang Jia, et al.
The Journal of Physical Chemistry C (2025)
Closed Access
Out-of-Distribution Material Property Prediction Using Adversarial Learning
Qinyang Li, Nicholas Miklaucic, Jianjun Hu
The Journal of Physical Chemistry C (2025)
Closed Access
Flipping the script: Predicting chemical composition in metal-halide perovskites from optical spectroscopy
Alexander J. Norquist
Chem (2025), pp. 102535-102535
Closed Access
NHC-Cracker: A Platform for the In Silico Engineering of N-Heterocyclic Carbenes for Diverse Chemical Applications
Gentoku Takasao, Bholanath Maity, Sayan Dutta, et al.
ACS Catalysis (2025), pp. 5915-5927
Open Access
Electron-Informed Coarse-Graining Molecular Representation Learning for Real-World Molecular Physics
Gyoung S. Na, Chanyoung Park
(2025), pp. 1080-1091
Closed Access
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