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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:

Predicting electronic structures at any length scale with machine learning
Lenz Fiedler, Normand A. Modine, Steve Schmerler, et al.
npj Computational Materials (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 34

Showing 1-25 of 34 citing articles:

Machine Learning‐Assisted Property Prediction of Solid‐State Electrolyte
Jin Li, Meisa Zhou, Hong‐Hui Wu, et al.
Advanced Energy Materials (2024) Vol. 14, Iss. 20
Closed Access | Times Cited: 44
Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures
Vera Kuznetsova, Áine Coogan, Dmitry Botov, et al.
Advanced Materials (2024) Vol. 36, Iss. 18
Open Access | Times Cited: 22
From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design
Jorge Benavides-Hernández, Franck Dumeignil
ACS Catalysis (2024) Vol. 14, Iss. 15, pp. 11749-11779
Closed Access | Times Cited: 21
MatGPT: A Vane of Materials Informatics from Past, Present, to Future
Zhilong Wang, An Chen, Kehao Tao, et al.
Advanced Materials (2023) Vol. 36, Iss. 6
Closed Access | Times Cited: 29
Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials—A Review
Kaiwei Wan, Jianxin He, Xinghua Shi
Advanced Materials (2023) Vol. 36, Iss. 22
Closed Access | Times Cited: 22
Density‐Functional Theory Studies on Photocatalysis and Photoelectrocatalysis: Challenges and Opportunities
Chun‐Han Lin, Jyoti Rohilla, Hsuan‐Hung Kuo, et al.
Solar RRL (2024) Vol. 8, Iss. 10
Closed Access | Times Cited: 14
A review of interface engineering characteristics for high performance perovskite solar cells
George G. Njema, Joshua K. Kibet, Silas M. Ngari
Deleted Journal (2024) Vol. 2, pp. 100005-100005
Open Access | Times Cited: 14
Machine-learning potentials for nanoscale simulations of tensile deformation and fracture in ceramics
Shuyao Lin, Luis Casillas‐Trujillo, Ferenc Tasnádi, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 8
Machine Learning-Based Molecular Dynamics Studies on Predicting Thermophysical Properties of Ethanol–Octane Blends
Amirali Shateri, Zhiyin Yang, Jianfei Xie
Energy & Fuels (2025)
Closed Access | Times Cited: 1
A Rapid UV/Vis Assisted Designing of Benzodithiophene Based Polymers by Machine Learning to Predict Their Light Absorption for Photovoltaics
Abrar U. Hassan, Cihat Güleryüz, Sajjad Hussain Sumrra, et al.
Organic Electronics (2025), pp. 107227-107227
Closed Access | Times Cited: 1
Toward first principles-based simulations of dense hydrogen
M. Bönitz, Jan Vorberger, Mandy Bethkenhagen, et al.
Physics of Plasmas (2024) Vol. 31, Iss. 11
Closed Access | Times Cited: 7
Generalizing deep learning electronic structure calculation to the plane-wave basis
Xiaoxun Gong, Steven G. Louie, Wenhui Duan, et al.
Nature Computational Science (2024) Vol. 4, Iss. 10, pp. 752-760
Open Access | Times Cited: 5
Machine learning in electrocatalysis - living up to the hype?
Árni Björn Höskuldsson
Current Opinion in Electrochemistry (2025), pp. 101649-101649
Closed Access
Machine Learning-Driven Prediction of Composite Materials Properties Based on Experimental Testing Data
Kristina Berladir, Katarzyna Antosz, Vitalii Ivanov, et al.
Polymers (2025) Vol. 17, Iss. 5, pp. 694-694
Open Access
Electronic structure prediction of multi-million atom systems through uncertainty quantification enabled transfer learning
Shashank Pathrudkar, Ponkrshnan Thiagarajan, Shivang Agarwal, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 4
Efficient Sampling for Machine Learning Electron Density and Its Response in Real Space
Chaoqiang Feng, Yaolong Zhang, Bin Jiang
Journal of Chemical Theory and Computation (2025)
Open Access
Development of a machine learning finite-range nonlocal density functional
Zehua Chen, Weitao Yang
The Journal of Chemical Physics (2024) Vol. 160, Iss. 1
Closed Access | Times Cited: 3
Comparing ANI-2x, ANI-1ccx neural networks, force field, and DFT methods for predicting conformational potential energy of organic molecules
Mozafar Rezaee, Saeid Ekrami, Seyed Majid Hashemianzadeh
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 3
Unsupervised representation learning of Kohn–Sham states and consequences for downstream predictions of many-body effects
Bowen Hou, Jinyuan Wu, Diana Y. Qiu
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 3
Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives
Cheng-Zhou 城洲 Zhang 张, Xiao-Qian 小倩 Fu 付
Chinese Physics B (2023) Vol. 32, Iss. 12, pp. 126103-126103
Closed Access | Times Cited: 5
Unlocking Potential of Pyrochlore in Energy Systems via Soft Voting Ensemble Learning
Kehao Tao, Zhilong Wang, An Chen, et al.
Small (2024) Vol. 20, Iss. 42
Closed Access | Times Cited: 1
Bridging the gap in electronic structure calculations via machine learning
Attila Cangi
Nature Computational Science (2024) Vol. 4, Iss. 10, pp. 729-730
Closed Access | Times Cited: 1
Accurate formation enthalpies of solids using reaction networks
Rasmus Fromsejer, Bjørn Maribo‐Mogensen, Georgios M. Kontogeorgis, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 1
X-ray-induced atomic transitions via machine learning: A computational investigation
Laura Budewig, Sang-Kil Son, Zoltán Jurek, et al.
Physical Review Research (2024) Vol. 6, Iss. 1
Open Access
Deductive Machine Learning Challenges and Opportunities in Chemical Applications
Tianfan Jin, Brett M. Savoie
Annual Review of Chemical and Biomolecular Engineering (2024) Vol. 15, Iss. 1, pp. 343-360
Closed Access
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