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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:

Recent advances and applications of deep learning methods in materials science
Kamal Choudhary, Brian DeCost, Chi Chen, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 576

Showing 1-25 of 576 citing articles:

Structured information extraction from scientific text with large language models
John Dagdelen, Alexander Dunn, Sang‐Hoon Lee, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 125
14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon
Kevin Maik Jablonka, Qianxiang Ai, Alexander Al-Feghali, et al.
Digital Discovery (2023) Vol. 2, Iss. 5, pp. 1233-1250
Open Access | Times Cited: 114
Assessing optimization techniques for improving water quality model
Md Galal Uddin, Stephen Nash, Azizur Rahman, et al.
Journal of Cleaner Production (2022) Vol. 385, pp. 135671-135671
Open Access | Times Cited: 92
Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects
Yiming Liu, Xinyu Tan, Jie Liang, et al.
Advanced Functional Materials (2023) Vol. 33, Iss. 17
Closed Access | Times Cited: 79
Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction
Jin Li, Naiteng Wu, Jian Zhang, et al.
Nano-Micro Letters (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 66
Artificial Intelligence in Predicting Mechanical Properties of Composite Materials
Fasikaw Kibrete, Tomasz Trzepieciński, Hailu Shimels Gebremedhen, et al.
Journal of Composites Science (2023) Vol. 7, Iss. 9, pp. 364-364
Open Access | Times Cited: 63
Scope of machine learning in materials research—A review
Md Hosne Mobarak, Mariam Akter Mimona, Md Aminul Islam, et al.
Applied Surface Science Advances (2023) Vol. 18, pp. 100523-100523
Open Access | Times Cited: 59
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: 52
A Perspective on Explanations of Molecular Prediction Models
Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, et al.
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 8, pp. 2149-2160
Open Access | Times Cited: 47
Artificial-intelligence-led revolution of construction materials: From molecules to Industry 4.0
Xing Quan Wang, Pengguang Chen, Cheuk Lun Chow, et al.
Matter (2023) Vol. 6, Iss. 6, pp. 1831-1859
Open Access | Times Cited: 47
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
Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures
Vera Kuznetsova, Áine Coogan, Dmitry Botov, et al.
Advanced Materials (2024) Vol. 36, Iss. 18
Open Access | Times Cited: 22
Van der Waals Electrides
Jun Zhou, Jing‐Yang You, Yiming Zhao, et al.
Accounts of Chemical Research (2024) Vol. 57, Iss. 17, pp. 2572-2581
Closed Access | Times Cited: 19
Explainable artificial intelligence: A survey of needs, techniques, applications, and future direction
Melkamu Mersha, Khang Nhứt Lâm, Joseph Wood, et al.
Neurocomputing (2024) Vol. 599, pp. 128111-128111
Closed Access | Times Cited: 16
A Comprehensive Review on Deep Learning Applications in Advancing Biodiesel Feedstock Selection and Production Processes
Olugbenga Akande, Jude A. Okolie, Richard Kimera, et al.
Green Energy and Intelligent Transportation (2025), pp. 100260-100260
Open Access | Times Cited: 2
Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process
Md. Rifat Hasan, Ahad Amer Alsaiari, Burhan Zain Fakhurji, et al.
Molecules (2022) Vol. 27, Iss. 13, pp. 4169-4169
Open Access | Times Cited: 54
Deep learning object detection in materials science: Current state and future directions
Ryan Jacobs
Computational Materials Science (2022) Vol. 211, pp. 111527-111527
Open Access | Times Cited: 47
Designing high-TC superconductors with BCS-inspired screening, density functional theory, and deep-learning
Kamal Choudhary, Kevin F. Garrity
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 46
Graph neural network predictions of metal organic framework CO2 adsorption properties
Kamal Choudhary, Taner Yildirim, Daniel W. Siderius, et al.
Computational Materials Science (2022) Vol. 210, pp. 111388-111388
Open Access | Times Cited: 42
Band gap predictions of double perovskite oxides using machine learning
Anjana Talapatra, Blas P. Uberuaga, Christopher R. Stanek, et al.
Communications Materials (2023) Vol. 4, Iss. 1
Open Access | Times Cited: 38
Conditional diffusion-based microstructure reconstruction
Christian Düreth, Paul Seibert, Dennis Rücker, et al.
Materials Today Communications (2023) Vol. 35, pp. 105608-105608
Open Access | Times Cited: 37
Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review
Minhyeok Lee
Molecules (2023) Vol. 28, Iss. 13, pp. 5169-5169
Open Access | Times Cited: 37
Thermodynamics and its prediction and CALPHAD modeling: Review, state of the art, and perspectives
Zi‐Kui Liu
Calphad (2023) Vol. 82, pp. 102580-102580
Open Access | Times Cited: 35
Deep learning for automated size and shape analysis of nanoparticles in scanning electron microscopy
Jonas Bals, Matthias Epple
RSC Advances (2023) Vol. 13, Iss. 5, pp. 2795-2802
Open Access | Times Cited: 34
TRACING THE EVOLUTION OF AI AND MACHINE LEARNING APPLICATIONS IN ADVANCING MATERIALS DISCOVERY AND PRODUCTION PROCESSES
Nwakamma Ninduwezuor-Ehiobu, Olawe Alaba Tula, Chibuike Daraojimba, et al.
Engineering Science & Technology Journal (2023) Vol. 4, Iss. 3, pp. 66-83
Open Access | Times Cited: 33
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