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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:

Quantum Machine Learning Approach for Studying Atmospheric Cluster Formation
Jakub Kubečka, Anders S. Christensen, Freja Rydahl Rasmussen, et al.
Environmental Science & Technology Letters (2022) Vol. 9, Iss. 3, pp. 239-244
Open Access | Times Cited: 27

Showing 1-25 of 27 citing articles:

The application of machine learning to air pollution research: A bibliometric analysis
Yunzhe Li, Zhipeng Sha, Aohan Tang, et al.
Ecotoxicology and Environmental Safety (2023) Vol. 257, pp. 114911-114911
Open Access | Times Cited: 29
Atmospheric Sulfuric Acid–Multi-Base New Particle Formation Revealed through Quantum Chemistry Enhanced by Machine Learning
Jakub Kubečka, Ivo Neefjes, Vitus Besel, et al.
The Journal of Physical Chemistry A (2023) Vol. 127, Iss. 9, pp. 2091-2103
Closed Access | Times Cited: 24
Clusteromics III: Acid Synergy in Sulfuric Acid–Methanesulfonic Acid–Base Cluster Formation
Jonas Elm
ACS Omega (2022) Vol. 7, Iss. 17, pp. 15206-15214
Open Access | Times Cited: 34
Massive Assessment of the Binding Energies of Atmospheric Molecular Clusters
Andreas Buchgraitz Jensen, Jakub Kubečka, Gunnar Alexander Schmitz, et al.
Journal of Chemical Theory and Computation (2022) Vol. 18, Iss. 12, pp. 7373-7383
Open Access | Times Cited: 28
Toward Modeling the Growth of Large Atmospheric Sulfuric Acid–Ammonia Clusters
Morten Engsvang, Jakub Kubečka, Jonas Elm
ACS Omega (2023) Vol. 8, Iss. 38, pp. 34597-34609
Open Access | Times Cited: 16
Quantum chemical modeling of atmospheric molecular clusters involving inorganic acids and methanesulfonic acid
Morten Engsvang, Haide Wu, Yosef Knattrup, et al.
Chemical Physics Reviews (2023) Vol. 4, Iss. 3
Open Access | Times Cited: 15
Accelerated screening of sensitive and selective MoO3-based gas sensing materials by combining first-principles and machine learning approach
Qi Zhou, Sifan Luo, Wei Xue, et al.
Chemical Engineering Journal (2023) Vol. 475, pp. 146318-146318
Closed Access | Times Cited: 15
Reparameterization of GFN1-xTB for atmospheric molecular clusters: applications to multi-acid–multi-base systems
Yosef Knattrup, Jakub Kubečka, Haide Wu, et al.
RSC Advances (2024) Vol. 14, Iss. 28, pp. 20048-20055
Open Access | Times Cited: 5
Classification of Potentially Hazardous Asteroids Using Supervised Quantum Machine Learning
Rushir Bhavsar, Nilesh Kumar Jadav, Umesh Bodkhe, et al.
IEEE Access (2023) Vol. 11, pp. 75829-75848
Open Access | Times Cited: 13
Clusterome: A Comprehensive Data Set of Atmospheric Molecular Clusters for Machine Learning Applications
Yosef Knattrup, Jakub Kubečka, Daniel Ayoubi, et al.
ACS Omega (2023) Vol. 8, Iss. 28, pp. 25155-25164
Open Access | Times Cited: 12
Computational Tools for Handling Molecular Clusters: Configurational Sampling, Storage, Analysis, and Machine Learning
Jakub Kubečka, Vitus Besel, Ivo Neefjes, et al.
ACS Omega (2023) Vol. 8, Iss. 47, pp. 45115-45128
Open Access | Times Cited: 11
Current and future machine learning approaches for modeling atmospheric cluster formation
Jakub Kubečka, Yosef Knattrup, Morten Engsvang, et al.
Nature Computational Science (2023) Vol. 3, Iss. 6, pp. 495-503
Closed Access | Times Cited: 10
Overview on Building Blocks and Applications of Efficient and Robust Extended Tight Binding
Abylay Katbashev, Marcel Stahn, Thomas Rose, et al.
The Journal of Physical Chemistry A (2025)
Closed Access
Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions
Noora Hyttinen, Antti Pihlajamäki, Hannu Häkkinen
The Journal of Physical Chemistry Letters (2022) Vol. 13, Iss. 42, pp. 9928-9933
Open Access | Times Cited: 12
Bridging the Gap Between High-Level Quantum Chemical Methods and Deep Learning Models
Viki Kumar Prasad, Alberto Otero‐de‐la‐Roza, Gino A. DiLabio
Machine Learning Science and Technology (2024) Vol. 5, Iss. 1, pp. 015035-015035
Open Access | Times Cited: 2
Reparameterization of GFN1-xTB for Atmospheric Molecular Clusters: Applications to Multi-Acid–Multi-Base Systems
Yosef Knattrup, Jakub Kubečka, Haide Wu, et al.
(2024)
Open Access | Times Cited: 2
Mechanistic Insights into UV Spectral Changes of Pyruvic Acid and Pyruvate Part 1: Interaction with Water Molecules
Petersen-Sonn Emma A, J. F., Jacob Rosarian Joy S, et al.
International Journal of Physics Research and Applications (2024) Vol. 7, Iss. 2, pp. 100-107
Open Access | Times Cited: 2
Computational Tools for Handling Molecular Clusters: Configurational Sampling, Storage, Analysis, and Machine Learning
Jakub Kubečka, Vitus Besel, Ivo Neefjes, et al.
(2023)
Open Access | Times Cited: 6
Computational chemistry of cluster: Understanding the mechanism of atmospheric new particle formation at the molecular level
Xiaomeng Zhang, Shendong Tan, Xi Chen, et al.
Chemosphere (2022) Vol. 308, pp. 136109-136109
Closed Access | Times Cited: 9
Benchmarking general neural network potential ANI‐2x on aerosol nucleation molecular clusters
Shuai Jiang, Yi‐Rong Liu, Chunyu Wang, et al.
International Journal of Quantum Chemistry (2023) Vol. 123, Iss. 10
Closed Access | Times Cited: 5
Accurate Modeling of the Potential Energy Surface of Molecular Clusters Boosted by Neural Networks
Jakub Kubečka, Daniel Ayoubi, Zeyuan Tang, et al.
Environmental Science Advances (2024) Vol. 3, Iss. 10, pp. 1438-1451
Open Access | Times Cited: 1
Sulfuric Acid-Driven Nucleation Enhanced by Amines from Ethanol Gasoline Vehicle Emission: Machine Learning Model and Mechanistic Study
Fangfang Ma, Lihao Su, Weihao Tang, et al.
Environmental Science & Technology (2024)
Closed Access | Times Cited: 1
Modeling the magnetocaloric effect of spinel ferrites for magnetic refrigeration technology using extreme learning machine and genetically hybridized support vector regression computational methods
Wasiu Adeyemi Oke, Nahier Aldhafferi, Saibu Saliu, et al.
Cogent Engineering (2023) Vol. 10, Iss. 2
Open Access | Times Cited: 3
Modeling Energy Gap of Doped Tin (II) Sulfide Metal Semiconductor Nanocatalyst Using Genetic Algorithm‐Based Support Vector Regression
Peter Chibuike Okoye, Samuel Ogochukwu Azi, Taoreed O. Owolabi, et al.
Journal of Nanomaterials (2022) Vol. 2022, Iss. 1
Open Access | Times Cited: 4
Ionization of small atmospheric acid–base clusters and its prospective role in seeding the growth of aqueous clusters
Bun Chan
International Journal of Mass Spectrometry (2024) Vol. 503, pp. 117285-117285
Open Access
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