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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:

Machine learning on neutron and x-ray scattering and spectroscopies
Zhantao Chen, Nina Andrejevic, Nathan C. Drucker, et al.
Chemical Physics Reviews (2021) Vol. 2, Iss. 3
Open Access | Times Cited: 79

Showing 1-25 of 79 citing articles:

Machine-learning and high-throughput studies for high-entropy materials
E‐Wen Huang, Wen‐Jay Lee, Sudhanshu S. Singh, et al.
Materials Science and Engineering R Reports (2022) Vol. 147, pp. 100645-100645
Open Access | Times Cited: 91
Artificial Intelligence and Advanced Materials
Cefe López
Advanced Materials (2022) Vol. 35, Iss. 23
Open Access | Times Cited: 56
Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification
Nathan J. Szymanski, Christopher J. Bartel, Yan Zeng, et al.
npj Computational Materials (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 29
The Autonomous Formulation Laboratory: An Open Liquid Handling Platform for Formulation Discovery Using X-ray and Neutron Scattering
Peter A. Beaucage, Tyler B. Martin
Chemistry of Materials (2023) Vol. 35, Iss. 3, pp. 846-852
Closed Access | Times Cited: 25
Machine learning for analyses and automation of structural characterization of polymer materials
Shizhao Lu, Arthi Jayaraman
Progress in Polymer Science (2024) Vol. 153, pp. 101828-101828
Closed Access | Times Cited: 12
Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization
Yiming Chen, Chi Chen, Inhui Hwang, et al.
Chemistry of Materials (2024) Vol. 36, Iss. 5, pp. 2304-2313
Open Access | Times Cited: 9
Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction
Jordan Venderley, Krishnanand Mallayya, Michael Matty, et al.
Proceedings of the National Academy of Sciences (2022) Vol. 119, Iss. 24
Open Access | Times Cited: 31
Designing materials acceleration platforms for heterogeneous CO2 photo(thermal)catalysis
Andrew Wang, Carlota Bozal‐Ginesta, Sai Govind Hari Kumar, et al.
Matter (2023) Vol. 6, Iss. 5, pp. 1334-1347
Open Access | Times Cited: 17
Multicode benchmark on simulated Ti K-edge x-ray absorption spectra of Ti-O compounds
Fanchen Meng, Benedikt Maurer, Fabian Peschel, et al.
Physical Review Materials (2024) Vol. 8, Iss. 1
Open Access | Times Cited: 7
Accurate, affordable, and generalizable machine learning simulations of transition metal x-ray absorption spectra using the XANESNET deep neural network
Conor D. Rankine, Thomas J. Penfold
The Journal of Chemical Physics (2022) Vol. 156, Iss. 16
Open Access | Times Cited: 27
A perspective on machine learning and data science for strongly correlated electron problems
Steven Johnston, Ehsan Khatami, Richard Scalettar
Carbon Trends (2022) Vol. 9, pp. 100231-100231
Open Access | Times Cited: 23
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: 14
Topological Superconductors from a Materials Perspective
Manasi Mandal, Nathan C. Drucker, Phum Siriviboon, et al.
Chemistry of Materials (2023) Vol. 35, Iss. 16, pp. 6184-6200
Open Access | Times Cited: 13
DLSIA: Deep Learning for Scientific Image Analysis
Eric J. Roberts, Tanny Chávez, Alexander Hexemer, et al.
Journal of Applied Crystallography (2024) Vol. 57, Iss. 2, pp. 392-402
Open Access | Times Cited: 5
The Phonon Theory of Liquids and Biological Fluids: Developments and Applications
Dima Bolmatov
The Journal of Physical Chemistry Letters (2022) Vol. 13, Iss. 31, pp. 7121-7129
Closed Access | Times Cited: 22
Machine learning for scattering data: strategies, perspectives and applications to surface scattering
Alexander Hinderhofer, Alessandro Greco, Vladimir Starostin, et al.
Journal of Applied Crystallography (2023) Vol. 56, Iss. 1, pp. 3-11
Open Access | Times Cited: 12
Harnessing Neural Networks for Elucidating X-ray Absorption Structure–Spectrum Relationships in Amorphous Carbon
Hyuna Kwon, Wenyu Sun, Tim Hsu, et al.
The Journal of Physical Chemistry C (2023) Vol. 127, Iss. 33, pp. 16473-16484
Open Access | Times Cited: 11
Symmetry breaking in 2D materials for optimizing second-harmonic generation
Nguyen Tuan Hung, Thanh Nguyen, Vuong Van Thanh, et al.
Journal of Physics D Applied Physics (2024) Vol. 57, Iss. 33, pp. 333002-333002
Closed Access | Times Cited: 4
Unsupervised Machine Learning-Based Image Recognition of Raw Infrared Spectra: Toward Chemist-like Chemical Structural Classification and Beyond Numerical Data
Kentaro Fuku, Takefumi Yoshida
Journal of Chemical Information and Modeling (2025)
Closed Access
Nuclear phase retrieval spectroscopy using resonant x-ray scattering
Ziyang Yuan, Hongxia Wang, Zhiwei Li, et al.
Nature Communications (2025) Vol. 16, Iss. 1
Open Access
Unlocking Hidden Information in Sparse Small-Angle Neutron Scattering Measurements
Chi-Huan Tung, Sidney Yip, Guan-Rong Huang, et al.
Journal of Colloid and Interface Science (2025), pp. 137554-137554
Closed Access
Extraction of interaction parameters for α−RuCl3 from neutron data using machine learning
Anjana Samarakoon, Pontus Laurell, Christian Balz, et al.
Physical Review Research (2022) Vol. 4, Iss. 2
Open Access | Times Cited: 18
Machine Learning at the Interface of Polymer Science and Biology: How Far Can We Go?
Eleonora Gianti, Simona Percec
Biomacromolecules (2022) Vol. 23, Iss. 3, pp. 576-591
Closed Access | Times Cited: 16
Using generative adversarial networks to match experimental and simulated inelastic neutron scattering data
Andy S. Anker, Keith T. Butler, Manh Duc Le, et al.
Digital Discovery (2023) Vol. 2, Iss. 3, pp. 578-590
Open Access | Times Cited: 10
Closing the loop: autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments
Linus Pithan, Vladimir Starostin, David Mareček, et al.
Journal of Synchrotron Radiation (2023) Vol. 30, Iss. 6, pp. 1064-1075
Open Access | Times Cited: 9
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