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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:

Science‐Driven Atomistic Machine Learning
Johannes T. Margraf
Angewandte Chemie International Edition (2023) Vol. 62, Iss. 26
Open Access | Times Cited: 18

Showing 18 citing articles:

Leave No Photon Behind: Artificial Intelligence in Multiscale Physics of Photocatalyst and Photoreactor Design
Joel Y. Y. Loh, Andrew Wang, Abhinav Mohan, et al.
Advanced Science (2024) Vol. 11, Iss. 18
Open Access | Times Cited: 10
Prediction model for spinal cord injury in spinal tuberculosis patients using multiple machine learning algorithms: a multicentric study
Sitan Feng, Shujiang Wang, Chong Liu, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 5
An intelligent approach: Integrating ChatGPT for experiment planning in biochar immobilization of soil cadmium
Hongwei Yang, Jie Wang, Rumeng Mo, et al.
Separation and Purification Technology (2024) Vol. 352, pp. 128170-128170
Closed Access | Times Cited: 3
Physics-inspired machine learning of localized intensive properties
Ke Chen, Christian Künkel, Bingqing Cheng, et al.
Chemical Science (2023) Vol. 14, Iss. 18, pp. 4913-4922
Open Access | Times Cited: 11
Leveraging Machine Learning Potentials for In-Situ Searching of Active sites in Heterogeneous Catalysis
Xiran Cheng, Chenyu Wu, Jiayan Xu, et al.
Precision Chemistry (2024) Vol. 2, Iss. 11, pp. 570-586
Open Access | Times Cited: 3
Assessment of Fine-Tuned Large Language Models for Real-World Chemistry and Material Science Applications
Joren Van Herck, M.V. Gil, Kevin Maik Jablonka, et al.
Chemical Science (2024)
Open Access | Times Cited: 3
AI for organic and polymer synthesis
Hong Xin, Qi Yang, Kuangbiao Liao, et al.
Science China Chemistry (2024) Vol. 67, Iss. 8, pp. 2461-2496
Closed Access | Times Cited: 2
Constructing Accurate and Efficient General-Purpose Atomistic Machine Learning Model with Transferable Accuracy for Quantum Chemistry
Yi‐Cheng Chen, Wenjie Yan, Zhanfeng Wang, et al.
Journal of Chemical Theory and Computation (2024) Vol. 20, Iss. 21, pp. 9500-9511
Open Access | Times Cited: 2
q-pac: A Python package for machine learned charge equilibration models
Martin Vondrák, Karsten Reuter, Johannes T. Margraf
The Journal of Chemical Physics (2023) Vol. 159, Iss. 5
Open Access | Times Cited: 5
Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case
Rafael G. Uceda, Alfonso Gijón, Sandra Míguez‐Lago, et al.
(2024)
Open Access | Times Cited: 1
Predicting melting temperatures across the periodic table with machine learning atomistic potentials
Christopher M. Andolina, Wissam A. Saidi
Digital Discovery (2024) Vol. 3, Iss. 7, pp. 1421-1429
Open Access
Efficient Composite Infrared Spectroscopy: Combining the Double-Harmonic Approximation with Machine Learning Potentials
Philipp Pracht, Yuthika Pillai, Venkat Kapil, et al.
Journal of Chemical Theory and Computation (2024)
Closed Access
An Intelligent Approach: Integrating Openai for Experiment Planning in Biochar Immobilization of Soil Cd
Hongwei Yang, Jie Wang, Rumeng Mo, et al.
(2024)
Closed Access
Assessment of Fine-Tuned Large Language Models for Real-World Chemistry and Material Science Applications
Joren Van Herck, M.V. Gil, Kevin Maik Jablonka, et al.
(2024)
Open Access
Infrared Spectra Prediction for Functional Group Region Utilizing a Machine Learning Approach with Structural Neighboring Mechanism
Chengchun Liu, Ruqiang Zou, Fanyang Mo
Analytical Chemistry (2024)
Closed Access
q-pac: A Python Package for Machine Learned Charge Equilibration Models
Martin Vondrák, Karsten Reuter, Johannes T. Margraf
(2023)
Open Access
An Intelligent Approach: Integrating Openai for Experiment Planning in Biochar Immobilization of Soil Cd
Hongwei Yang, Rumeng Mo, Pengxiang Hu, et al.
(2023)
Closed Access
Trendbericht Theoretische Chemie 2023 (1/3): Maschinelles Lernen jenseits von Potenzialenergiehyperflächen
Johannes T. Margraf
Nachrichten aus der Chemie (2023) Vol. 71, Iss. 11, pp. 60-62
Open Access
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