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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:

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

Showing 1-25 of 42 citing articles:

MOFormer: Self-Supervised Transformer Model for Metal–Organic Framework Property Prediction
Zhonglin Cao, Rishikesh Magar, Yuyang Wang, et al.
Journal of the American Chemical Society (2023) Vol. 145, Iss. 5, pp. 2958-2967
Open Access | Times Cited: 79
Unified graph neural network force-field for the periodic table: solid state applications
Kamal Choudhary, Brian DeCost, Lily Major, et al.
Digital Discovery (2023) Vol. 2, Iss. 2, pp. 346-355
Open Access | Times Cited: 57
The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture
Anuroop Sriram, Sihoon Choi, Xiaohan Yu, et al.
ACS Central Science (2024) Vol. 10, Iss. 5, pp. 923-941
Open Access | Times Cited: 21
A systematic review of machine learning approaches in carbon capture applications
Farihahusnah Hussin, Siti Aqilah Nadhirah Md. Rahim, Nur Syahirah Mohamed Hatta, et al.
Journal of CO2 Utilization (2023) Vol. 71, pp. 102474-102474
Open Access | Times Cited: 26
Recent progress on advanced solid adsorbents for CO2 capture: From mechanism to machine learning
Mobin Safarzadeh Khosrowshahi, Amirhossein Afshari Aghajari, Mohammad Rahimi, et al.
Materials Today Sustainability (2024) Vol. 27, pp. 100900-100900
Closed Access | Times Cited: 15
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
Computational and Machine Learning Methods for CO2 Capture Using Metal–Organic Frameworks
Hossein Mashhadimoslem, Mohammad Ali Abdol, Peyman Karimi, et al.
ACS Nano (2024) Vol. 18, Iss. 35, pp. 23842-23875
Closed Access | Times Cited: 14
Informative Training Data for Efficient Property Prediction in Metal–Organic Frameworks by Active Learning
Ashna Jose, Emilie DEVIJVER, N. Jakse, et al.
Journal of the American Chemical Society (2024) Vol. 146, Iss. 9, pp. 6134-6144
Closed Access | Times Cited: 12
Development of the design and synthesis of metal–organic frameworks (MOFs) – from large scale attempts, functional oriented modifications, to artificial intelligence (AI) predictions
Zongsu Han, Yihao Yang, Joshua Rushlow, et al.
Chemical Society Reviews (2024)
Open Access | Times Cited: 11
Reviewing direct air capture startups and emerging technologies
Eryu Wang, Rahul Navik, Yihe Miao, et al.
Cell Reports Physical Science (2024) Vol. 5, Iss. 2, pp. 101791-101791
Open Access | Times Cited: 9
Machine Learning Descriptors for CO2 Capture Materials
Ibrahim Orhan, Yuankai Zhao, Ravichandar Babarao, et al.
Molecules (2025) Vol. 30, Iss. 3, pp. 650-650
Open Access | Times Cited: 1
Recent progress in the JARVIS infrastructure for next-generation data-driven materials design
Daniel Wines, Ramya Gurunathan, Kevin F. Garrity, et al.
Applied Physics Reviews (2023) Vol. 10, Iss. 4
Open Access | Times Cited: 16
Artificial Intelligence in Material Engineering: A Review on Applications of Artificial Intelligence in Material Engineering
Lipichanda Goswami, Manoj Kumar Deka, Mohendra Roy
Advanced Engineering Materials (2023) Vol. 25, Iss. 13
Open Access | Times Cited: 14
Can a deep-learning model make fast predictions of vacancy formation in diverse materials?
Kamal Choudhary, Bobby G. Sumpter
AIP Advances (2023) Vol. 13, Iss. 9
Open Access | Times Cited: 14
Accelerated Discovery of Metal–Organic Frameworks for CO2 Capture by Artificial Intelligence
Hasan Can Gülbalkan, Gokhan Onder Aksu, Goktug Ercakir, et al.
Industrial & Engineering Chemistry Research (2023) Vol. 63, Iss. 1, pp. 37-48
Open Access | Times Cited: 14
MOSAEC-DB: a comprehensive database of experimental metal–organic frameworks with verified chemical accuracy suitable for molecular simulations
Marco Gibaldi, Anna Kapeliukha, Andrew J. P. White, et al.
Chemical Science (2025)
Open Access
High-Throughput Prediction of Metal-Embedded Complex Properties with a New GNN-Based Metal Attention Framework
X Zhao, Bao Wang, Kun Zhou, et al.
Journal of Chemical Information and Modeling (2025)
Closed Access
Machine Learning-Driven Web Tools for Predicting Properties of Materials and Molecules
Dmitriy M. Makarov, Pavel S. Bocharov, Michail M. Lukanov, et al.
Challenges and advances in computational chemistry and physics (2025), pp. 273-292
Closed Access
Nanoarchitectonics: the role of artificial intelligence in the design and application of nanoarchitectures
L. R. Oviedo, V. R. Oviedo, Mirkos Ortiz Martins, et al.
Journal of Nanoparticle Research (2022) Vol. 24, Iss. 8
Closed Access | Times Cited: 20
An XGBoost Algorithm Based on Molecular Structure and Molecular Specificity Parameters for Predicting Gas Adsorption
Lujun Li, Yiming Zhao, Haibin Yu, et al.
Langmuir (2023) Vol. 39, Iss. 19, pp. 6756-6766
Closed Access | Times Cited: 11
Pretraining Strategies for Structure Agnostic Material Property Prediction
Hongshuo Huang, Rishikesh Magar, Amir Barati Farimani
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 3, pp. 627-637
Open Access | Times Cited: 4
InterMat: Accelerating Band Offset Prediction in Semiconductor Interfaces with DFT and Deep Learning
Kamal Choudhary, Kevin F. Garrity
Digital Discovery (2024) Vol. 3, Iss. 7, pp. 1365-1377
Open Access | Times Cited: 4
A generalized machine learning framework for brittle crack problems using transfer learning and graph neural networks
Roberto Perera, Vinamra Agrawal
Mechanics of Materials (2023) Vol. 181, pp. 104639-104639
Open Access | Times Cited: 10
End-to-end AI framework for interpretable prediction of molecular and crystal properties
Hyun Park, Ruijie Zhu, E. A. Huerta, et al.
Machine Learning Science and Technology (2023) Vol. 4, Iss. 2, pp. 025036-025036
Open Access | Times Cited: 9
Flat-Histogram Monte Carlo Simulation of Water Adsorption in Metal–Organic Frameworks
Daniel W. Siderius, Harold W. Hatch, Vincent K. Shen
The Journal of Physical Chemistry B (2024) Vol. 128, Iss. 19, pp. 4830-4845
Open Access | Times Cited: 3
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