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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‐Assisted Determination of the Global Zero‐Temperature Phase Diagram of Materials
Jonathan Schmidt, Noah Hoffmann, Hai‐Chen Wang, et al.
Advanced Materials (2023) Vol. 35, Iss. 22
Open Access | Times Cited: 33

Showing 1-25 of 33 citing articles:

Prediction of ambient pressure conventional superconductivity above 80 K in hydride compounds
Antonio Sanna, Tiago F. T. Cerqueira, Yue‐Wen Fang, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 39
Searching Materials Space for Hydride Superconductors at Ambient Pressure
Tiago F. T. Cerqueira, Yue‐Wen Fang, Ion Errea, et al.
Advanced Functional Materials (2024) Vol. 34, Iss. 40
Open Access | Times Cited: 20
A generative model for inorganic materials design
Claudio Zeni, Robert Pinsler, Daniel Zügner, et al.
Nature (2025) Vol. 639, Iss. 8055, pp. 624-632
Closed Access | Times Cited: 18
Sampling the Materials Space for Conventional Superconducting Compounds
Tiago F. T. Cerqueira, Antonio Sanna, Miguel A. L. Marques
Advanced Materials (2023) Vol. 36, Iss. 1
Open Access | Times Cited: 33
AtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design
Kamal Choudhary
The Journal of Physical Chemistry Letters (2024) Vol. 15, Iss. 27, pp. 6909-6917
Open Access | Times Cited: 15
Machine learning in energy storage material discovery and performance prediction
Guo-Chang Huang, Fuqiang Huang, Wujie Dong
Chemical Engineering Journal (2024) Vol. 492, pp. 152294-152294
Closed Access | Times Cited: 14
Improving machine-learning models in materials science through large datasets
Jonathan Schmidt, Tiago F. T. Cerqueira, A. Romero, et al.
Materials Today Physics (2024) Vol. 48, pp. 101560-101560
Open Access | Times Cited: 12
Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange
Matthew L. Evans, J. Bergsma, Andrius Merkys, et al.
Digital Discovery (2024) Vol. 3, Iss. 8, pp. 1509-1533
Open Access | Times Cited: 10
Machine Learning for Optimising Renewable Energy and Grid Efficiency
Bankole I. Oladapo, Mattew A. Olawumi, Francis T. Omigbodun
Atmosphere (2024) Vol. 15, Iss. 10, pp. 1250-1250
Open Access | Times Cited: 10
Probing out-of-distribution generalization in machine learning for materials
Kangming Li, Andre Niyongabo Rubungo, X. L. Lei, et al.
Communications Materials (2025) Vol. 6, Iss. 1
Open Access | Times Cited: 1
Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies
Jiahao Xie, Yansong Zhou, Muhammad Faizan, et al.
Nature Computational Science (2024) Vol. 4, Iss. 5, pp. 322-333
Closed Access | Times Cited: 7
Machine-Learning-Assisted Construction of Ternary Convex Hull Diagrams
Hugo Rossignol, Michail Minotakis, Matteo Cobelli, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 6, pp. 1828-1840
Open Access | Times Cited: 5
Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage
Joshua Ojih, Mohammed Al‐Fahdi, Yagang Yao, et al.
Journal of Materials Chemistry A (2024) Vol. 12, Iss. 14, pp. 8502-8515
Open Access | Times Cited: 5
Searching for ductile superconducting Heusler X2YZ compounds
Noah Hoffmann, Tiago F. T. Cerqueira, Pedro Borlido, et al.
npj Computational Materials (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 13
High-refractive-index materials screening from machine learning and ab initio methods
Pedro J. M. A. Carriço, Márcio Ferreira, Tiago F. T. Cerqueira, et al.
Physical Review Materials (2024) Vol. 8, Iss. 1
Closed Access | Times Cited: 4
When Two Becomes Three: Predicting Stable Ternary Boron‐Based Compounds by Populating Unique Lattice Sites in Binary Prototype Structures
Adam Carlsson, Johanna Rosén, Martin Dahlqvist
Advanced Theory and Simulations (2025)
Open Access
Predictive modeling and optimization of CIGS thin film solar cells: A machine learning approach
K.R. Kumbhar, R.S. Redekar, A.B. Raule, et al.
Solar Energy (2025) Vol. 294, pp. 113509-113509
Closed Access
Crystal Structure Prediction of Cs–Te with Supervised Machine Learning
Holger‐Dietrich Saßnick, Caterina Cocchi
Advanced Theory and Simulations (2025)
Open Access
CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties
Daniel Wines, Kamal Choudhary
ACS Materials Letters (2025), pp. 2105-2114
Open Access
Symmetry-based computational search for novel binary and ternary 2D materials
Hai‐Chen Wang, Jonathan Schmidt, Miguel A. L. Marques, et al.
2D Materials (2023) Vol. 10, Iss. 3, pp. 035007-035007
Open Access | Times Cited: 8
Exploring Flat-Band Properties in Two-Dimensional M3QX7 Compounds
Hai‐Chen Wang, Tomáš Rauch, Andres Tellez, et al.
Physical Chemistry Chemical Physics (2024) Vol. 26, Iss. 32, pp. 21558-21567
Open Access | Times Cited: 2
Synthesis, Structural Analysis, and Degradation Behavior of Potassium Tin Chloride as Chloride‐Ion Batteries Conversion Electrode Material
Soutam Panja, Yidong Miao, Johannes Döhn, et al.
Advanced Functional Materials (2024)
Open Access | Times Cited: 2
Fully autonomous materials screening methodology combining first-principles calculations, machine learning and high-performance computing system
Akira Takahashi, Kei Terayama, Yu Kumagai, et al.
Science and Technology of Advanced Materials Methods (2023) Vol. 3, Iss. 1
Open Access | Times Cited: 5
Efficient structure-informed featurization and property prediction of ordered, dilute, and random atomic structures
Adam M. Krajewski, Jonathan W. Siegel, Zi‐Kui Liu
Computational Materials Science (2024) Vol. 247, pp. 113495-113495
Closed Access | Times Cited: 1
Deep learning of spectra: Predicting the dielectric function of semiconductors
Malte Grunert, Max Großmann, Erich Runge
Physical Review Materials (2024) Vol. 8, Iss. 12
Open Access | Times Cited: 1
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