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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:

Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
Andrew Rosen, Shaelyn Iyer, Debmalya Ray, et al.
Matter (2021) Vol. 4, Iss. 5, pp. 1578-1597
Open Access | Times Cited: 293

Showing 1-25 of 293 citing articles:

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: 535
Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery
Haoxin Mai, Tu C. Le, Dehong Chen, et al.
Chemical Reviews (2022) Vol. 122, Iss. 16, pp. 13478-13515
Closed Access | Times Cited: 236
Machine learning for a sustainable energy future
Zhenpeng Yao, Yanwei Lum, Andrew Johnston, et al.
Nature Reviews Materials (2022) Vol. 8, Iss. 3, pp. 202-215
Open Access | Times Cited: 195
Benchmarking graph neural networks for materials chemistry
Victor Fung, Jiaxin Zhang, Eric Juarez, et al.
npj Computational Materials (2021) Vol. 7, Iss. 1
Open Access | Times Cited: 179
Molecular excited states through a machine learning lens
Pavlo O. Dral, Mario Barbatti
Nature Reviews Chemistry (2021) Vol. 5, Iss. 6, pp. 388-405
Closed Access | Times Cited: 170
Emerging porous organic polymers for biomedical applications
Youlong Zhu, Peiwen Xu, Xingcai Zhang, et al.
Chemical Society Reviews (2022) Vol. 51, Iss. 4, pp. 1377-1414
Closed Access | Times Cited: 153
Ab Initio Machine Learning in Chemical Compound Space
Bing Huang, O. Anatole von Lilienfeld
Chemical Reviews (2021) Vol. 121, Iss. 16, pp. 10001-10036
Open Access | Times Cited: 121
Material Evolution with Nanotechnology, Nanoarchitectonics, and Materials Informatics: What will be the Next Paradigm Shift in Nanoporous Materials?
Watcharop Chaikittisilp, Yusuke Yamauchi, Katsuhiko Ariga
Advanced Materials (2021) Vol. 34, Iss. 7
Closed Access | Times Cited: 112
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
High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration
Andrew Rosen, Victor Fung, Patrick Huck, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 87
Human- and machine-centred designs of molecules and materials for sustainability and decarbonization
Jiayu Peng, Daniel Schwalbe‐Koda, Karthik Akkiraju, et al.
Nature Reviews Materials (2022) Vol. 7, Iss. 12, pp. 991-1009
Closed Access | Times Cited: 83
A data-science approach to predict the heat capacity of nanoporous materials
Seyed Mohamad Moosavi, Balázs Álmos Novotny, Daniele Ongari, et al.
Nature Materials (2022) Vol. 21, Iss. 12, pp. 1419-1425
Open Access | Times Cited: 83
A multi-modal pre-training transformer for universal transfer learning in metal–organic frameworks
Yeonghun Kang, Hyunsoo Park, Berend Smit, et al.
Nature Machine Intelligence (2023) Vol. 5, Iss. 3, pp. 309-318
Open Access | Times Cited: 72
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: 70
From Platform to Knowledge Graph: Evolution of Laboratory Automation
Jiaru Bai, Liwei Cao, Sebastian Mosbach, et al.
JACS Au (2022) Vol. 2, Iss. 2, pp. 292-309
Open Access | Times Cited: 67
MOF based composites with engineering aspects and morphological developments for photocatalytic CO2 reduction and hydrogen production: A comprehensive review
Muhammad Tahir, Bilkis Ajiwokewu, Anifat Adenike Bankole, et al.
Journal of environmental chemical engineering (2023) Vol. 11, Iss. 2, pp. 109408-109408
Closed Access | Times Cited: 63
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
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: 55
Single‐Atom Nanozymes for Catalytic Therapy: Recent Advances and Challenges
Weiyi He, Jiahao Wu, Jianli Liu, et al.
Advanced Functional Materials (2024) Vol. 34, Iss. 16
Closed Access | Times Cited: 50
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
Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis
Jing Lin, Zhimeng Liu, Yujie Guo, et al.
Nano Today (2023) Vol. 49, pp. 101802-101802
Closed Access | Times Cited: 36
ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models
Yeonghun Kang, Jihan Kim
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 21
Unconventional mechanical and thermal behaviours of MOF CALF-20
Dong Fan, Supriyo Naskar, Guillaume Maurin
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 17
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
Generative AI and process systems engineering: The next frontier
Benjamin Decardi‐Nelson, Abdulelah S. Alshehri, Akshay Ajagekar, et al.
Computers & Chemical Engineering (2024) Vol. 187, pp. 108723-108723
Open Access | Times Cited: 15
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