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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:

Metrics for Benchmarking and Uncertainty Quantification: Quality, Applicability, and Best Practices for Machine Learning in Chemistry
Gaurav Vishwakarma, Aditya Sonpal, Johannes Hachmann
Trends in Chemistry (2021) Vol. 3, Iss. 2, pp. 146-156
Open Access | Times Cited: 55

Showing 1-25 of 55 citing articles:

Machine Learning: New Ideas and Tools in Environmental Science and Engineering
Shifa Zhong, Kai Zhang, Majid Bagheri, et al.
Environmental Science & Technology (2021)
Closed Access | Times Cited: 690
How to validate machine-learned interatomic potentials
Joe D. Morrow, John L. A. Gardner, Volker L. Deringer
The Journal of Chemical Physics (2023) Vol. 158, Iss. 12
Open Access | Times Cited: 69
Accelerated chemical science with AI
Seoin Back, Alán Aspuru-Guzik, Michele Ceriotti, et al.
Digital Discovery (2023) Vol. 3, Iss. 1, pp. 23-33
Open Access | Times Cited: 46
Data Generation for Machine Learning Interatomic Potentials and Beyond
Maksim Kulichenko, Benjamin Nebgen, Nicholas Lubbers, et al.
Chemical Reviews (2024) Vol. 124, Iss. 24, pp. 13681-13714
Closed Access | Times Cited: 17
Omni-directionally flexible, high performance all-solid-state micro-supercapacitor array-based energy storage system for wearable electronics
Thi Huyen Chau Nguyen, Jeongho Lee, Dawoon Lee, et al.
Chemical Engineering Journal (2025), pp. 159375-159375
Closed Access | Times Cited: 2
Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023
Igor Poltavsky, Mirela Puleva, Anton Charkin-Gorbulin, et al.
Chemical Science (2025)
Open Access | Times Cited: 2
Machine learning-based prediction of supercapacitor performance for a novel electrode material: Cerium oxynitride
Sourav Ghosh, G. Ranga Rao, Tiju Thomas
Energy storage materials (2021) Vol. 40, pp. 426-438
Closed Access | Times Cited: 63
Emerging pathways to sustainable economic development: An interdisciplinary exploration of resource efficiency, technological innovation, and ecosystem resilience in resource-rich regions
Feipeng Wang, Wing‐Keung Wong, Zheng Wang, et al.
Resources Policy (2023) Vol. 85, pp. 103747-103747
Closed Access | Times Cited: 36
Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges
Swarn Jha, Matthew Yen, Yazmin Soto Salinas, et al.
Journal of Materials Chemistry A (2023) Vol. 11, Iss. 8, pp. 3904-3936
Closed Access | Times Cited: 26
Integrated data-driven modeling and experimental optimization of granular hydrogel matrices
Connor Verheyen, Sebastien G. M. Uzel, Armand Kurum, et al.
Matter (2023) Vol. 6, Iss. 3, pp. 1015-1036
Open Access | Times Cited: 26
Effects of image data quality on a convolutional neural network trained in-tank fish detection model for recirculating aquaculture systems
Rakesh Ranjan, Kata Sharrer, Scott Tsukuda, et al.
Computers and Electronics in Agriculture (2023) Vol. 205, pp. 107644-107644
Open Access | Times Cited: 25
Comparative Analysis of Physics-Guided Bayesian Neural Networks for Uncertainty Quantification in Dynamic Systems
Xiangzhi Xu, Julian Wang
Forecasting (2025) Vol. 7, Iss. 1, pp. 9-9
Open Access | Times Cited: 1
Pairwise Difference Regression: A Machine Learning Meta-algorithm for Improved Prediction and Uncertainty Quantification in Chemical Search
Michael Tynes, Wenhao Gao, Daniel J. Burrill, et al.
Journal of Chemical Information and Modeling (2021) Vol. 61, Iss. 8, pp. 3846-3857
Open Access | Times Cited: 45
Best practices for machine learning in antibody discovery and development
Leonard Wossnig, Norbert Furtmann, Andrew Buchanan, et al.
Drug Discovery Today (2024) Vol. 29, Iss. 7, pp. 104025-104025
Open Access | Times Cited: 8
Machine learning, artificial intelligence, and chemistry: How smart algorithms are reshaping simulation and the laboratory
David Kuntz, Angela K. Wilson
Pure and Applied Chemistry (2022) Vol. 94, Iss. 8, pp. 1019-1054
Open Access | Times Cited: 25
Technical Study of Deep Learning in Cloud Computing for Accurate Workload Prediction
Zaakki Ahamed, Maher Khemakhem, Fathy Eassa, et al.
Electronics (2023) Vol. 12, Iss. 3, pp. 650-650
Open Access | Times Cited: 14
Robust model benchmarking and bias-imbalance in data-driven materials science: a case study on MODNet
Pierre-Paul De Breuck, Matthew L. Evans, Gian‐Marco Rignanese
Journal of Physics Condensed Matter (2021) Vol. 33, Iss. 40, pp. 404002-404002
Open Access | Times Cited: 30
The long road to calibrated prediction uncertainty in computational chemistry
Pascal Pernot
The Journal of Chemical Physics (2022) Vol. 156, Iss. 11
Open Access | Times Cited: 20
Predicting Low-Modulus Biocompatible Titanium Alloys Using Machine Learning
Gordana Marković, Vaso Manojlović, Jovana Ružić, et al.
Materials (2023) Vol. 16, Iss. 19, pp. 6355-6355
Open Access | Times Cited: 12
Determining Ion Activity Coefficients in Ion-Exchange Membranes with Machine Learning and Molecular Dynamics Simulations
Hishara Keshani Gallage Dona, Teslim Olayiwola, Luis Briceño-Mena, et al.
Industrial & Engineering Chemistry Research (2023) Vol. 62, Iss. 24, pp. 9533-9548
Open Access | Times Cited: 11
Machine learning aided cyclic stability prediction for supercapacitors
Siddhartha Nanda, Sourav Ghosh, Tiju Thomas
Journal of Power Sources (2022) Vol. 546, pp. 231975-231975
Closed Access | Times Cited: 19
Forecasting Students Dropout: A UTAD University Study
Diogo E. Moreira da Silva, E. J. Solteiro Pires, Arsénio Reis, et al.
Future Internet (2022) Vol. 14, Iss. 3, pp. 76-76
Open Access | Times Cited: 17
Optimization of capacitance in supercapacitors by constructing an experimentally validated hybrid artificial neural networks-genetic algorithm framework
Duygu Kaya, Dilara Koroglu, Erdal Aydın, et al.
Journal of Power Sources (2023) Vol. 568, pp. 232987-232987
Closed Access | Times Cited: 10
State-of-the-art progress on artificial intelligence and machine learning in accessing molecular coordination and adsorption of corrosion inhibitors
Taiwo W. Quadri, Ekemini D. Akpan, Saheed E. Elugoke, et al.
Applied Physics Reviews (2025) Vol. 12, Iss. 1
Closed Access
Aleatory-aware deep uncertainty quantification for transfer learning
H M Dipu Kabir, Sadia Khanam, Fahime Khozeimeh, et al.
Computers in Biology and Medicine (2022) Vol. 143, pp. 105246-105246
Closed Access | Times Cited: 16
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