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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:

Open Catalyst 2020 (OC20) Dataset and Community Challenges
Lowik Chanussot, Abhishek Das, Siddharth Goyal, et al.
ACS Catalysis (2021) Vol. 11, Iss. 10, pp. 6059-6072
Open Access | Times Cited: 388

Showing 1-25 of 388 citing articles:

Scientific discovery in the age of artificial intelligence
Hanchen Wang, Tianfan Fu, Yuanqi Du, et al.
Nature (2023) Vol. 620, Iss. 7972, pp. 47-60
Closed Access | Times Cited: 588
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: 543
Graph neural networks for materials science and chemistry
Patrick Reiser, Marlen Neubert, André Eberhard, et al.
Communications Materials (2022) Vol. 3, Iss. 1
Open Access | Times Cited: 299
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: 297
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, et al.
arXiv (Cornell University) (2020)
Closed Access | Times Cited: 267
Interpretable machine learning for knowledge generation in heterogeneous catalysis
Jacques A. Esterhuizen, Bryan R. Goldsmith, Suljo Linic
Nature Catalysis (2022) Vol. 5, Iss. 3, pp. 175-184
Closed Access | Times Cited: 223
A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis
Tobias Gensch, Gabriel dos Passos Gomes, Pascal Friederich, et al.
Journal of the American Chemical Society (2022) Vol. 144, Iss. 3, pp. 1205-1217
Closed Access | Times Cited: 213
Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements
So Takamoto, Chikashi Shinagawa, D. Motoki, et al.
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 197
DeePMD-kit v2: A software package for deep potential models
Jinzhe Zeng, Duo Zhang, Denghui Lu, et al.
The Journal of Chemical Physics (2023) Vol. 159, Iss. 5
Open Access | Times Cited: 188
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: 181
DFT exchange: sharing perspectives on the workhorse of quantum chemistry and materials science
Andrew M. Teale, Trygve Helgaker, Andreas Savin, et al.
Physical Chemistry Chemical Physics (2022) Vol. 24, Iss. 47, pp. 28700-28781
Open Access | Times Cited: 161
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
Bridging the complexity gap in computational heterogeneous catalysis with machine learning
Tianyou Mou, Hemanth Somarajan Pillai, Siwen Wang, et al.
Nature Catalysis (2023) Vol. 6, Iss. 2, pp. 122-136
Closed Access | Times Cited: 121
The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts
Richard Tran, Janice Lan, Muhammed Shuaibi, et al.
ACS Catalysis (2023) Vol. 13, Iss. 5, pp. 3066-3084
Open Access | Times Cited: 112
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: 84
Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics
Kedar Hippalgaonkar, Qianxiao Li, Xiaonan Wang, et al.
Nature Reviews Materials (2023) Vol. 8, Iss. 4, pp. 241-260
Closed Access | Times Cited: 81
Exploring catalytic reaction networks with machine learning
Johannes T. Margraf, Hyunwook Jung, Christoph Scheurer, et al.
Nature Catalysis (2023) Vol. 6, Iss. 2, pp. 112-121
Closed Access | Times Cited: 80
Machine Learning: A New Paradigm in Computational Electrocatalysis
Xu Zhang, Yun Tian, Letian Chen, et al.
The Journal of Physical Chemistry Letters (2022) Vol. 13, Iss. 34, pp. 7920-7930
Closed Access | Times Cited: 70
Crystal structure prediction by combining graph network and optimization algorithm
Guanjian Cheng, Xin-Gao Gong, Wan‐Jian Yin
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 64
Representations of Materials for Machine Learning
James Damewood, Jessica Karaguesian, Jaclyn R. Lunger, et al.
Annual Review of Materials Research (2023) Vol. 53, Iss. 1, pp. 399-426
Open Access | Times Cited: 47
Data-driven discovery of electrocatalysts for CO2 reduction using active motifs-based machine learning
Dong Hyeon Mok, Hong Li, Guiru Zhang, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 47
Neural scaling of deep chemical models
Nathan C. Frey, Ryan Soklaski, Simon Axelrod, et al.
Nature Machine Intelligence (2023) Vol. 5, Iss. 11, pp. 1297-1305
Open Access | Times Cited: 42
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: 39
Fast evaluation of the adsorption energy of organic molecules on metals via graph neural networks
Sergio Pablo‐García, Santiago Morandi, Rodrigo A. Vargas–Hernández, et al.
Nature Computational Science (2023) Vol. 3, Iss. 5, pp. 433-442
Open Access | Times Cited: 38
Predicting equilibrium distributions for molecular systems with deep learning
Shuxin Zheng, Jiyan He, Chang Liu, et al.
Nature Machine Intelligence (2024) Vol. 6, Iss. 5, pp. 558-567
Open Access | Times Cited: 23
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