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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:

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

Showing 1-25 of 33 citing articles:

Recent advances in artificial intelligence boosting materials design for electrochemical energy storage
X.-B. Liu, Kexin Fan, Xinmeng Huang, et al.
Chemical Engineering Journal (2024) Vol. 490, pp. 151625-151625
Open Access | Times Cited: 32
Designing membranes with specific binding sites for selective ion separations
Camille Violet, Akash Kumar Ball, Mohammad Heiranian, et al.
Nature Water (2024) Vol. 2, Iss. 8, pp. 706-718
Closed Access | Times Cited: 18
Diffusion-based generative AI for exploring transition states from 2D molecular graphs
Seong-Hwan Kim, Jeheon Woo, Woo Youn Kim
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 15
Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective
Yuheng Ding, Bo Qiang, Qixuan Chen, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 8, pp. 2955-2970
Closed Access | Times Cited: 10
Application-oriented design of machine learning paradigms for battery science
Ying Wang
npj Computational Materials (2025) Vol. 11, Iss. 1
Open Access | Times Cited: 1
Reinforcement Learning for Traversing Chemical Structure Space: Optimizing Transition States and Minimum Energy Paths of Molecules
Rhyan Barrett, Julia Westermayr
The Journal of Physical Chemistry Letters (2024) Vol. 15, Iss. 1, pp. 349-356
Open Access | Times Cited: 7
Analytical ab initio hessian from a deep learning potential for transition state optimization
Eric Chung‐Yueh Yuan, Anup Kumar, Xingyi Guan, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 5
Benchmarking machine-readable vectors of chemical reactions on computed activation barriers
Puck van Gerwen, Ksenia R. Briling, Yannick Calvino Alonso, et al.
Digital Discovery (2024) Vol. 3, Iss. 5, pp. 932-943
Open Access | Times Cited: 4
Review of External Field Effects on Electrocatalysis: Machine Learning Guided Design
Lei Wang, Xuyan Zhou, Zihan Luo, et al.
Advanced Functional Materials (2024)
Closed Access | Times Cited: 4
AGDIFF: Attention-Enhanced Diffusion for Molecular Geometry Prediction
André Brasil Vieira Wyzykowski, Fatemeh Fathi Niazi, Alex Dickson
Journal of Chemical Information and Modeling (2025)
Closed Access
Ten Problems in Polymer Reactivity Prediction
Nicholas E. Jackson, Brett M. Savoie
Macromolecules (2025)
Closed Access
Transition State Searching Accelerated by Neural Network Potential
Bowen Li, Jin Xiao, Ya Gao, et al.
Journal of Chemical Information and Modeling (2025)
Closed Access
Computational Exploration of Codoped Fe and Ru Single-Atom Catalysts for the Oxygen Reduction Reaction
Haojun Jia, Chenru Duan, Gianmarco Terrones, et al.
(2025)
Closed Access
Progress in Computational Methods and Mechanistic Insights on the Growth of Carbon Nanotubes
Linzheng Wang, Nicolas Tricard, Zi Chen, et al.
Nanoscale (2025)
Open Access
Generative molecular design and discovery on the rise

Nature Computational Science (2025)
Closed Access
A Deep Generative Model for the Inverse Design of Transition Metal Ligands and Complexes
Magnus Strandgaard, Trond Linjordet, Hannes Kneiding, et al.
JACS Au (2025)
Open 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
Physics-based modeling in the new era of enzyme engineering
Christopher Jurich, Qianzhen Shao, Xinchun Ran, et al.
Nature Computational Science (2025) Vol. 5, Iss. 4, pp. 279-291
Closed Access
Big data-driven machine learning transformation for atomic-scale heterogeneous catalyst design: a critical review
Xiaofan Zheng, Jia Yang, Lianyong Zhou, et al.
Chemical Engineering Science (2025), pp. 121740-121740
Closed Access
Computational exploration of codoped Fe and Ru single-atom catalysts for the oxygen reduction reaction
Haojun Jia, Chenru Duan, Gianmarco Terrones, et al.
Journal of Catalysis (2025), pp. 116163-116163
Closed Access
From pages to patterns: Towards extracting catalytic knowledge from structure and text for transition-metal complexes and metal-organic frameworks
Aditya Nandy
Journal of Catalysis (2025), pp. 116174-116174
Open Access
3DReact: Geometric Deep Learning for Chemical Reactions
Puck van Gerwen, Ksenia R. Briling, Charlotte Bunne, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 15, pp. 5771-5785
Open Access | Times Cited: 3
Active Learning of Boltzmann Samplers and Potential Energies with Quantum Mechanical Accuracy
Ana Molina-Taborda, Pilar Cossio, Olga Lopez‐Acevedo, et al.
Journal of Chemical Theory and Computation (2024) Vol. 20, Iss. 20, pp. 8833-8843
Open Access | Times Cited: 3
Construction of Highly Accurate Machine Learning Potential Energy Surfaces for Excited-State Dynamics Simulations Based on Low-Level Data Sets
Shuai Li, Bin‐Bin Xie, Bo‐Wen Yin, et al.
The Journal of Physical Chemistry A (2024) Vol. 128, Iss. 28, pp. 5516-5524
Closed Access | Times Cited: 2
Generative artificial intelligence in chemical engineering spans multiple scales
Benjamin Decardi‐Nelson, Abdulelah S. Alshehri, Fengqi You
Frontiers in Chemical Engineering (2024) Vol. 6
Open Access | Times Cited: 2
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