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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:

Spectroscopy from Machine Learning by Accurately Representing the Atomic Polar Tensor
Philipp Schienbein
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 3, pp. 705-712
Open Access | Times Cited: 27

Showing 1-25 of 27 citing articles:

Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials
Amir Omranpour, Pablo Montero de Hijes, Jörg Behler, et al.
The Journal of Chemical Physics (2024) Vol. 160, Iss. 17
Open Access | Times Cited: 23
Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors
Miguel Gallegos, Valentín Vassilev-Galindo, Igor Poltavsky, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 16
Universal machine learning for the response of atomistic systems to external fields
Yaolong Zhang, Bin Jiang
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 34
First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects
Venkat Kapil, Dávid Péter Kovács, Gábor Cśanyi, et al.
Faraday Discussions (2023) Vol. 249, pp. 50-68
Open Access | Times Cited: 28
Neural Network-Based Sum-Frequency Generation Spectra of Pure and Acidified Water Interfaces with Air
Miguel de la Puente, Axel Gomez, Damien Laage
The Journal of Physical Chemistry Letters (2024) Vol. 15, Iss. 11, pp. 3096-3102
Open Access | Times Cited: 13
Machine learning the electric field response of condensed phase systems using perturbed neural network potentials
Kit Joll, Philipp Schienbein, Kevin M. Rosso, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 9
Fully First-Principles Surface Spectroscopy with Machine Learning
Yair Litman, Jinggang Lan, Yuki Nagata, et al.
The Journal of Physical Chemistry Letters (2023) Vol. 14, Iss. 36, pp. 8175-8182
Open Access | Times Cited: 18
In situ/Operando Investigation for Heterogeneous Electro-Catalysts: From Model Catalysts to State-of-the-Art Catalysts
Jingting Song, Zhengxin Qian, Ji Yang, et al.
ACS Energy Letters (2024) Vol. 9, Iss. 9, pp. 4414-4440
Closed Access | Times Cited: 8
Computation of CCSD(T)-Quality NMR Chemical Shifts via Δ-Machine Learning from DFT
Julius B. Kleine Büning, Stefan Grimme
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 12, pp. 3601-3615
Closed Access | Times Cited: 16
Integrating Explainability into Graph Neural Network Models for the Prediction of X-ray Absorption Spectra
Amir Kotobi, Kanishka Singh, Daniel Höche, et al.
Journal of the American Chemical Society (2023) Vol. 145, Iss. 41, pp. 22584-22598
Open Access | Times Cited: 15
Anharmonicity and quantum nuclear effects in theoretical vibrational spectroscopy: a molecular tale of two cities
Riccardo Conte, Chiara Aieta, Giacomo Botti, et al.
Theoretical Chemistry Accounts (2023) Vol. 142, Iss. 5
Open Access | Times Cited: 12
Raman Spectra of Amino Acids and Peptides from Machine Learning Polarizabilities
Ethan Berger, Juha Niemelä, Outi Lampela, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 12, pp. 4601-4612
Open Access | Times Cited: 4
Derivative learning of tensorial quantities—Predicting finite temperature infrared spectra from first principles
Bernhard Schmiedmayer, Georg Kresse
The Journal of Chemical Physics (2024) Vol. 161, Iss. 8
Open Access | Times Cited: 4
Revealing the molecular structures of α-Al2O3(0001)–water interface by machine learning based computational vibrational spectroscopy
Xianglong Du, W. W. Shao, Chenglong Bao, et al.
The Journal of Chemical Physics (2024) Vol. 161, Iss. 12
Closed Access | Times Cited: 4
First-principles machine-learning study of infrared spectra of methane under extreme pressure and temperature conditions
G.K. Liu, J. Y. Huang, Rui Hou, et al.
Chemical Physics Letters (2025), pp. 142036-142036
Closed Access
Quantum dynamics through a handful of semiclassical trajectories
Chiara Aieta, Marco Cazzaniga, Davide Moscato, et al.
Rendiconti lincei. Scienze fisiche e naturali (2025)
Open Access
Universal Neural Network Potential Study of the Pt/CO Heterogeneous Catalytic System
Gerardo Valadez Huerta, Yūsuke Nanba, Michihisa Koyama
Surfaces and Interfaces (2025), pp. 106542-106542
Closed Access
Unified differentiable learning of electric response
Stefano Falletta, Andrea Cepellotti, Anders Johansson, et al.
Nature Communications (2025) Vol. 16, Iss. 1
Open Access
Exploring new generation of characterization approaches for energy electrochemistry—from <italic>operando</italic> to artificial intelligence
Yu Qiao, Hu Ren, Yu Gu, et al.
Scientia Sinica Chimica (2023) Vol. 54, Iss. 3, pp. 338-352
Open Access | Times Cited: 8
Uncertainty quantification and propagation in atomistic machine learning
Jin Dai, Santosh Adhikari, Mingjian Wen
Reviews in Chemical Engineering (2024)
Open Access | Times Cited: 2
Boosting the Modeling of InfraRed and Raman Spectra of Bulk Phase Chromophores with Machine Learning
Abir KEBABSA, François Maurel, Éric Brémond
(2024)
Open Access | Times Cited: 1
State-of-the-art review on various applications of machine learning techniques in materials science and engineering
Bing Yu, Lai‐Chang Zhang, Xiaoxia Ye, et al.
Chemical Engineering Science (2024), pp. 121147-121147
Closed Access | Times Cited: 1
Machine learning approaches for modeling of molecular polarizability in gold nanoclusters
Abhishek Ojha, Satya Bulusu, Arup Banerjee
Artificial Intelligence Chemistry (2024) Vol. 2, Iss. 2, pp. 100080-100080
Open Access | Times Cited: 1
The heterogeneity of aqueous solutions: the current situation in the context of experiment and theory
G. О. Stepanov, Nikita V. Penkov, Natalia N. Rodionova, et al.
Frontiers in Chemistry (2024) Vol. 12
Open Access
Boosting the Modeling of Infrared and Raman Spectra of Bulk Phase Chromophores with Machine Learning
Abir KEBABSA, François Maurel, Éric Brémond
Journal of Chemical Theory and Computation (2024)
Open Access
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