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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:

Adsorption Enthalpies for Catalysis Modeling through Machine-Learned Descriptors
Mie Andersen, Karsten Reuter
Accounts of Chemical Research (2021) Vol. 54, Iss. 12, pp. 2741-2749
Closed Access | Times Cited: 82

Showing 1-25 of 82 citing articles:

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: 277
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: 254
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: 130
Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale
Xiang Chen, Xinyan Liu, Xin Shen, et al.
Angewandte Chemie International Edition (2021) Vol. 60, Iss. 46, pp. 24354-24366
Closed Access | Times Cited: 125
Machine intelligence for chemical reaction space
Philippe Schwaller, Alain C. Vaucher, Rubén Laplaza, et al.
Wiley Interdisciplinary Reviews Computational Molecular Science (2022) Vol. 12, Iss. 5
Open Access | Times Cited: 91
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: 88
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: 47
Machine-Learning-Driven High-Throughput Screening of Transition-Metal Atom Intercalated g-C3N4/MX2 (M = Mo, W; X = S, Se, Te) Heterostructures for the Hydrogen Evolution Reaction
M. V. Jyothirmai, Roshini Dantuluri, Priyanka Sinha, et al.
ACS Applied Materials & Interfaces (2024) Vol. 16, Iss. 10, pp. 12437-12445
Closed Access | Times Cited: 24
Advancing electrocatalytic reactions through mapping key intermediates to active sites via descriptors
Xiaowen Sun, Rafael B. Araujo, Egon Campos dos Santos, et al.
Chemical Society Reviews (2024) Vol. 53, Iss. 14, pp. 7392-7425
Closed Access | Times Cited: 18
Surface-Enhanced Raman Spectroscopy for Biomedical Applications: Recent Advances and Future Challenges
Li Lin, Ramón A. Álvarez‐Puebla, Luis M. Liz‐Marzán, et al.
ACS Applied Materials & Interfaces (2025)
Closed Access | Times Cited: 3
Machine learning in solid heterogeneous catalysis: Recent developments, challenges and perspectives
Yani Guan, Donovan Chaffart, Guihua Liu, et al.
Chemical Engineering Science (2021) Vol. 248, pp. 117224-117224
Closed Access | Times Cited: 87
Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis
Miguel Steiner, Markus Reiher
Topics in Catalysis (2022) Vol. 65, Iss. 1-4, pp. 6-39
Open Access | Times Cited: 52
How machine learning can accelerate electrocatalysis discovery and optimization
Stephan N. Steinmann, Qing Wang, Zhi Wei Seh
Materials Horizons (2022) Vol. 10, Iss. 2, pp. 393-406
Open Access | Times Cited: 52
Physically Informed Machine Learning Prediction of Electronic Density of States
Victor Fung, Panchapakesan Ganesh, Bobby G. Sumpter
Chemistry of Materials (2022) Vol. 34, Iss. 11, pp. 4848-4855
Open Access | Times Cited: 45
Machine-learning driven global optimization of surface adsorbate geometries
Hyunwook Jung, Lena Sauerland, Sina Stocker, et al.
npj Computational Materials (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 29
A Surrogate Machine Learning Model for the Design of Single-Atom Catalyst on Carbon and Porphyrin Supports towards Electrochemistry
Mohsen Tamtaji, Shuguang Chen, Ziyang Hu, et al.
The Journal of Physical Chemistry C (2023) Vol. 127, Iss. 21, pp. 9992-10000
Closed Access | Times Cited: 27
Interpretable Catalysis Models Using Machine Learning with Spectroscopic Descriptors
Song Wang, Jun Jiang
ACS Catalysis (2023) Vol. 13, Iss. 11, pp. 7428-7436
Closed Access | Times Cited: 26
Data-driven design of electrocatalysts: principle, progress, and perspective
Shan Zhu, Kezhu Jiang, Biao Chen, et al.
Journal of Materials Chemistry A (2023) Vol. 11, Iss. 8, pp. 3849-3870
Closed Access | Times Cited: 23
Recent advances in the electrochemical production of hydrogen peroxide
Nishu Dhanda, Yogesh Kumar Panday, Sudesh Kumar
Electrochimica Acta (2024), pp. 143872-143872
Closed Access | Times Cited: 13
Electronic structure factors and the importance of adsorbate effects in chemisorption on surface alloys
Shikha Saini, Joakim Halldin Stenlid, Frank Abild‐Pedersen
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 37
Stability Design Principles of Manganese-Based Oxides in Acid
Jiayu Peng, Livia Giordano, Timothy C. Davenport, et al.
Chemistry of Materials (2022) Vol. 34, Iss. 17, pp. 7774-7787
Closed Access | Times Cited: 36
Predicting binding motifs of complex adsorbates using machine learning with a physics-inspired graph representation
Wenbin Xu, Karsten Reuter, Mie Andersen
Nature Computational Science (2022) Vol. 2, Iss. 7, pp. 443-450
Closed Access | Times Cited: 35
Data‐Driven Interpretable Descriptors for the Structure–Activity Relationship of Surface Lattice Oxygen on Doped Vanadium Oxides
Chenggong Jiang, Hongbo Song, Guodong Sun, et al.
Angewandte Chemie International Edition (2022) Vol. 61, Iss. 35
Closed Access | Times Cited: 35
Heterogeneous chemical reactions—A cornerstone in emission reduction of local pollutants and greenhouse gases
Patrick Lott, Olaf Deutschmann
Proceedings of the Combustion Institute (2022) Vol. 39, Iss. 3, pp. 3183-3215
Open Access | Times Cited: 32
Ab Initio to Activity: Machine Learning-Assisted Optimization of High-Entropy Alloy Catalytic Activity
Christian M. Clausen, Martin L. S. Nielsen, Jack K. Pedersen, et al.
Deleted Journal (2022) Vol. 1, Iss. 1, pp. 120-133
Open Access | Times Cited: 31
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