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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:

ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT-Derived Partial Atomic Charges and Descriptors for Machine Learning
Jake Burner, Jun Luo, Andrew J. P. White, et al.
Chemistry of Materials (2023) Vol. 35, Iss. 3, pp. 900-916
Open Access | Times Cited: 57

Showing 1-25 of 57 citing articles:

Recent advances in computational modeling of MOFs: From molecular simulations to machine learning
Hakan Demir, Hilal Daglar, Hasan Can Gülbalkan, et al.
Coordination Chemistry Reviews (2023) Vol. 484, pp. 215112-215112
Open Access | Times Cited: 97
Progress toward the computational discovery of new metal–organic framework adsorbents for energy applications
Peyman Z. Moghadam, Yongchul G. Chung, Randall Q. Snurr
Nature Energy (2024) Vol. 9, Iss. 2, pp. 121-133
Closed Access | Times Cited: 43
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: 16
Inverse design of metal–organic frameworks for direct air capture of CO2via deep reinforcement learning
Hyunsoo Park, Sauradeep Majumdar, Xiaoqi Zhang, et al.
Digital Discovery (2024) Vol. 3, Iss. 4, pp. 728-741
Open Access | Times Cited: 15
A database of ultrastable MOFs reassembled from stable fragments with machine learning models
Aditya Nandy, Shuwen Yue, Changhwan Oh, et al.
Matter (2023) Vol. 6, Iss. 5, pp. 1585-1603
Open Access | Times Cited: 32
Exploring the Structural, Dynamic, and Functional Properties of Metal‐Organic Frameworks through Molecular Modeling
Filip Formalik, Kaihang Shi, Faramarz Joodaki, et al.
Advanced Functional Materials (2023) Vol. 34, Iss. 43
Open Access | Times Cited: 26
Accelerating In Silico Discovery of Metal–Organic Frameworks for Ethane/Ethylene and Propane/Propylene Separation: A Synergistic Approach Integrating Molecular Simulation, Machine Learning, and Active Learning
Varad Daoo, Jayant K. Singh
ACS Applied Materials & Interfaces (2024) Vol. 16, Iss. 6, pp. 6971-6987
Closed Access | Times Cited: 10
Topological Characterization of Metal–Organic Frameworks: A Perspective
Lawson T. Glasby, Joan Cordiner, Jason C. Cole, et al.
Chemistry of Materials (2024) Vol. 36, Iss. 19, pp. 9013-9030
Open Access | Times Cited: 7
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: 6
Leveraging Cross-Diversity Machine Learning to Unveil Metal–Organic Frameworks with Open Copper Sites for Biogas Upgrading
Xiaoyu Wu, Rui Zheng, Jianwen Jiang
Journal of Chemical Theory and Computation (2025)
Closed Access
Machine learning-assisted development of gas separation membranes: A review
An Li, Jianchun Chu, Shaoxuan Huang, et al.
Carbon Capture Science & Technology (2025) Vol. 14, pp. 100374-100374
Closed Access
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
Analyzing of metal organic frameworks performance in CH4 adsorption using machine learning techniques: A GBRT model based on small training dataset
Xin Wei, Ding Peng, Lin Shen, et al.
Journal of environmental chemical engineering (2023) Vol. 11, Iss. 3, pp. 110086-110086
Closed Access | Times Cited: 17
The Chemistry of Metal Organic Framework Materials
Sara E. Skrabalak, Ramanathan Vaidhyanathan
Chemistry of Materials (2023) Vol. 35, Iss. 15, pp. 5713-5722
Closed Access | Times Cited: 14
Leveraging Machine Learning for Metal–Organic Frameworks: A Perspective
Hongjian Tang, Lunbo Duan, Jianwen Jiang
Langmuir (2023) Vol. 39, Iss. 45, pp. 15849-15863
Closed Access | Times Cited: 14
Discovery of novel reticular materials for carbon dioxide capture using GFlowNets
Flaviu Cipcigan, Jonathan Booth, Rodrigo Neumann Barros Ferreira, et al.
Digital Discovery (2024) Vol. 3, Iss. 3, pp. 449-455
Open Access | Times Cited: 4
Active learning graph neural networks for partial charge prediction of metal-organic frameworks via dropout Monte Carlo
Stephan Thaler, Felix Mayr, Siby Thomas, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 4
Computational Simulations of Metal–Organic Frameworks to Enhance Adsorption Applications
Hilal Daglar, Hasan Can Gülbalkan, Gokhan Onder Aksu, et al.
Advanced Materials (2024)
Open Access | Times Cited: 4
How Can (or Why Should) Process Engineering Aid the Screening and Discovery of Solid Sorbents for CO2 Capture?
Arvind Rajendran, Sai Gokul Subraveti, Kasturi Nagesh Pai, et al.
Accounts of Chemical Research (2023) Vol. 56, Iss. 17, pp. 2354-2365
Closed Access | Times Cited: 12
Efficient Xylene Isomer Separation: Accelerated Screening with Active Learning and Molecular Simulation
Mohd. Aqib, Varad Daoo, Jayant K. Singh
Energy & Fuels (2024) Vol. 38, Iss. 11, pp. 9381-9394
Closed Access | Times Cited: 3
From Data to Discovery: Recent Trends of Machine Learning in Metal–Organic Frameworks
Junkil Park, Honghui Kim, Yeonghun Kang, et al.
JACS Au (2024) Vol. 4, Iss. 10, pp. 3727-3743
Open Access | Times Cited: 3
Metal–Organic Frameworks through the Lens of Artificial Intelligence: A Comprehensive Review
Kevizali Neikha, Амрит Пузари
Langmuir (2024) Vol. 40, Iss. 42, pp. 21957-21975
Closed Access | Times Cited: 3
High-Throughput, Multiscale Computational Screening of Metal–Organic Frameworks for Xe/Kr Separation with Machine-Learned Parameters
Guobin Zhao, Yu Chen, Yongchul G. Chung
Industrial & Engineering Chemistry Research (2023) Vol. 62, Iss. 37, pp. 15176-15189
Closed Access | Times Cited: 10
Integrating stability metrics with high-throughput computational screening of metal–organic frameworks for CO2 capture
Saad Aldin Mohamed, Daohui Zhao, Jianwen Jiang
Communications Materials (2023) Vol. 4, Iss. 1
Open Access | Times Cited: 10
Multiscale Modeling of Physical Properties of Nanoporous Frameworks: Predicting Mechanical, Thermal, and Adsorption Behavior
Arthur Hardiagon, François‐Xavier Coudert
Accounts of Chemical Research (2024) Vol. 57, Iss. 11, pp. 1620-1632
Open Access | Times Cited: 2
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