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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:

A transferable recommender approach for selecting the best density functional approximations in chemical discovery
Chenru Duan, Aditya Nandy, Ralf Meyer, et al.
Nature Computational Science (2022) Vol. 3, Iss. 1, pp. 38-47
Open Access | Times Cited: 22

Showing 22 citing articles:

Machine Learning Methods for Small Data Challenges in Molecular Science
Bozheng Dou, Zailiang Zhu, Ekaterina Merkurjev, et al.
Chemical Reviews (2023) Vol. 123, Iss. 13, pp. 8736-8780
Open Access | Times Cited: 184
Modeling Interfacial Dynamics on Single Atom Electrocatalysts: Explicit Solvation and Potential Dependence
Zisheng Zhang, Jun Li, Yang‐Gang Wang
Accounts of Chemical Research (2024) Vol. 57, Iss. 2, pp. 198-207
Closed Access | Times Cited: 30
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: 33
Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model
Chenru Duan, Yuanqi Du, Haojun Jia, et al.
Nature Computational Science (2023) Vol. 3, Iss. 12, pp. 1045-1055
Closed Access | Times Cited: 33
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
Hyperspectral reporters for long-distance and wide-area detection of gene expression in living bacteria
Yonatan Chemla, Itai Levin, Yueyang Fan, et al.
Nature Biotechnology (2025)
Closed Access | Times Cited: 1
Identifying and embedding transferability in data-driven representations of chemical space
Tim Gould, Bun Chan, Stephen G. Dale, et al.
Chemical Science (2024) Vol. 15, Iss. 28, pp. 11122-11133
Open Access | Times Cited: 6
DELFI: a computer oracle for recommending density functionals for excited states calculations
Davide Avagliano, Marta Skreta, Sebastian Arellano-Rubach, et al.
Chemical Science (2024) Vol. 15, Iss. 12, pp. 4489-4503
Open Access | Times Cited: 5
Improving the Reliability of, and Confidence in, DFT Functional Benchmarking through Active Learning
Javier Emilio Alfonso Ramos, Carlo Adamo, Éric Brémond, et al.
Journal of Chemical Theory and Computation (2025)
Closed Access
Optimal transport for generating transition states in chemical reactions
Chenru Duan, Guan-Horng Liu, Yuanqi Du, et al.
Nature Machine Intelligence (2025) Vol. 7, Iss. 4, pp. 615-626
Open Access
Combining Molecular Quantum Mechanical Modeling and Machine Learning for Accelerated Reaction Screening and Discovery
Nicholas Casetti, Javier Emilio Alfonso Ramos, Connor W. Coley, et al.
Chemistry - A European Journal (2023) Vol. 29, Iss. 60
Closed Access | Times Cited: 5
Transferable diversity – a data-driven representation of chemical space
Tim Gould, Bun Chang, Stephen G. Dale, et al.
(2024)
Open Access | Times Cited: 1
Learning Design Rules for Catalysts Through Computational Chemistry and Machine Learning
Aditya Nandy, Heather J. Kulik
(2024), pp. 513-558
Closed Access | Times Cited: 1
The AABBA Graph Kernel: Atom–Atom, Bond–Bond, and Bond–Atom Autocorrelations for Machine Learning
Lucía Morán‐González, Jørn Eirik Betten, Hannes Kneiding, et al.
(2024)
Open Access | Times Cited: 1
AABBA Graph Kernel: Atom-Atom, Bond-Bond, and Bond-Atom Autocorrelations for Machine Learning
Lucía Morán‐González, Jørn Eirik Betten, Hannes Kneiding, et al.
Journal of Chemical Information and Modeling (2024)
Open Access | Times Cited: 1
Using AI to navigate through the DFA zoo
Stefan Vuckovic
Nature Computational Science (2023) Vol. 3, Iss. 1, pp. 6-7
Closed Access | Times Cited: 2
Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models
Yael Cytter, Aditya Nandy, Chenru Duan, et al.
Physical Chemistry Chemical Physics (2023) Vol. 25, Iss. 11, pp. 8103-8116
Open Access | Times Cited: 2
Right Answer for the Right Reason? Benchmarking Protocols and Pitfalls on a Ru-Metathesis Example
Naziha Tarannam, Nebal Alassad, N. Gabriel Lemcoff, et al.
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 15, pp. 5024-5035
Closed Access | Times Cited: 2
Prediction of Reaction Orthogonality using Machine Learning
Hootan Roshandel, Santiago Vargas, Amy Lai, et al.
(2023)
Open Access | Times Cited: 1
DELFI: A computer oracle for recommending the best density functional for excited states calculations
Davide Avagliano, Marta Skreta, Sebastian Arellano-Rubach, et al.
(2023)
Open Access
DELFI: A computer oracle for recommending density functionals for excited states calculations
Davide Avagliano, Marta Skreta, Sebastian Arellano-Rubach, et al.
(2023)
Open Access
Informative Training Data for Efficient Property Prediction in Metal-Organic Frameworks by Active Learning
Ashna Jose, Emilie DEVIJVER, N. Jakse, et al.
(2023)
Open Access
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