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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:

Unsupervised Learning Methods for Molecular Simulation Data
Aldo Glielmo, Brooke E. Husic, Álex Rodríguez, et al.
Chemical Reviews (2021) Vol. 121, Iss. 16, pp. 9722-9758
Open Access | Times Cited: 295

Showing 1-25 of 295 citing articles:

Physics-Inspired Structural Representations for Molecules and Materials
Félix Musil, Andrea Grisafi, Albert P. Bartók, et al.
Chemical Reviews (2021) Vol. 121, Iss. 16, pp. 9759-9815
Open Access | Times Cited: 430
SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
Oliver T. Unke, Stefan Chmiela, Michael Gastegger, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 205
Machine Learning Methods for Small Data Challenges in Molecular Science
Bozheng Dou, Zailiang Zhu, Ekaterina Merkurjev, et al.
Chemical Reviews (2023) Vol. 123, Iss. 13, pp. 8736-8780
Open Access | Times Cited: 170
SELFIES and the future of molecular string representations
Mario Krenn, Qianxiang Ai, Senja Barthel, et al.
Patterns (2022) Vol. 3, Iss. 10, pp. 100588-100588
Open Access | Times Cited: 154
Quantum machine learning for chemistry and physics
Manas Sajjan, Junxu Li, Raja Selvarajan, et al.
Chemical Society Reviews (2022) Vol. 51, Iss. 15, pp. 6475-6573
Open Access | Times Cited: 91
Machine Learning Interatomic Potentials and Long-Range Physics
Dylan M. Anstine, Olexandr Isayev
The Journal of Physical Chemistry A (2023) Vol. 127, Iss. 11, pp. 2417-2431
Open Access | Times Cited: 89
Machine Learning-Guided Protein Engineering
Petr Kouba, Pavel Kohout, Faraneh Haddadi, et al.
ACS Catalysis (2023) Vol. 13, Iss. 21, pp. 13863-13895
Open Access | Times Cited: 86
Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects
Natei Ermias Benti, Mesfin Diro Chaka, Addisu Gezahegn Semie
Sustainability (2023) Vol. 15, Iss. 9, pp. 7087-7087
Open Access | Times Cited: 85
Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics
Hong Zhou, Liangge Xu, Zhihao Ren, et al.
Nanoscale Advances (2022) Vol. 5, Iss. 3, pp. 538-570
Open Access | Times Cited: 73
Scope of machine learning in materials research—A review
Md Hosne Mobarak, Mariam Akter Mimona, Md Aminul Islam, et al.
Applied Surface Science Advances (2023) Vol. 18, pp. 100523-100523
Open Access | Times Cited: 59
Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments
Oliver T. Unke, Martin Stöhr, Stefan Ganscha, et al.
Science Advances (2024) Vol. 10, Iss. 14
Open Access | Times Cited: 26
Metabolomics and chemometrics: The next-generation analytical toolkit for the evaluation of food quality and authenticity
Pascual García-Pérez, Pier Paolo Becchi, Leilei Zhang, et al.
Trends in Food Science & Technology (2024) Vol. 147, pp. 104481-104481
Open Access | Times Cited: 22
Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array
Haixia Mei, Jingyi Peng, Tao Wang, et al.
Nano-Micro Letters (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 21
Navigating the landscape of enzyme design: from molecular simulations to machine learning
Jiahui Zhou, Meilan Huang
Chemical Society Reviews (2024) Vol. 53, Iss. 16, pp. 8202-8239
Open Access | Times Cited: 18
High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications
Jianhua Zhou, Jianpei Dong, Hongwei Hou, et al.
Lab on a Chip (2024) Vol. 24, Iss. 5, pp. 1307-1326
Open Access | Times Cited: 16
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
Roadmap on machine learning glassy dynamics
Gerhard Jung, Rinske M. Alkemade, Victor Bapst, et al.
Nature Reviews Physics (2025)
Open Access | Times Cited: 2
Markov State Models to Study the Functional Dynamics of Proteins in the Wake of Machine Learning
Kirill A. Konovalov, Ilona Christy Unarta, Siqin Cao, et al.
JACS Au (2021) Vol. 1, Iss. 9, pp. 1330-1341
Open Access | Times Cited: 96
Deeptime: a Python library for machine learning dynamical models from time series data
Moritz Hoffmann, Martin K. Scherer, Tim Hempel, et al.
Machine Learning Science and Technology (2021) Vol. 3, Iss. 1, pp. 015009-015009
Open Access | Times Cited: 82
Neural network potentials for chemistry: concepts, applications and prospects
Silvan Käser, Luis Itza Vazquez-Salazar, Markus Meuwly, et al.
Digital Discovery (2022) Vol. 2, Iss. 1, pp. 28-58
Open Access | Times Cited: 66
Chasing Collective Variables Using Autoencoders and Biased Trajectories
Zineb Belkacemi, Paraskevi Gkeka, Tony Lelièvre, et al.
Journal of Chemical Theory and Computation (2021) Vol. 18, Iss. 1, pp. 59-78
Open Access | Times Cited: 65
Newton-X Platform: New Software Developments for Surface Hopping and Nuclear Ensembles
Mario Barbatti, Mattia Bondanza, Rachel Crespo‐Otero, et al.
Journal of Chemical Theory and Computation (2022) Vol. 18, Iss. 11, pp. 6851-6865
Open Access | Times Cited: 62
The atomistic modeling of light-harvesting complexes from the physical models to the computational protocol
Edoardo Cignoni, Vladislav Sláma, Lorenzo Cupellini, et al.
The Journal of Chemical Physics (2022) Vol. 156, Iss. 12
Open Access | Times Cited: 54
Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation
Yunchao Xie, Kianoosh Sattari, Chi Zhang, et al.
Progress in Materials Science (2022) Vol. 132, pp. 101043-101043
Open Access | Times Cited: 54
Collective variable discovery in the age of machine learning: reality, hype and everything in between
Soumendranath Bhakat
RSC Advances (2022) Vol. 12, Iss. 38, pp. 25010-25024
Open Access | Times Cited: 46
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