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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:

Application of Machine Learning to the Design of Energetic Materials: Preliminary Experience and Comparison with Alternative Techniques
Clément Wespiser, Didier Mathieu
Propellants Explosives Pyrotechnics (2022) Vol. 48, Iss. 4
Closed Access | Times Cited: 16

Showing 16 citing articles:

Descriptors applicability in machine learning-assisted prediction of thermal decomposition temperatures for energetic materials: Insights from model evaluation and outlier analysis
Zhixiang Zhang, Chao Chen, Yilin Cao, et al.
Thermochimica Acta (2024) Vol. 735, pp. 179717-179717
Closed Access | Times Cited: 9
Thermal Stability of Several Organic Hydroxylamine Derivatives and a Broader Look at Explosive Hazard Identification
Dmitry K. Pronkin, Igor N. Melnikov, Dmitry B. Meerov, et al.
Industrial & Engineering Chemistry Research (2025)
Closed Access | Times Cited: 1
Introduction to Predicting Properties of Organic Materials
Didier Mathieu
Challenges and advances in computational chemistry and physics (2025), pp. 27-63
Closed Access | Times Cited: 1
Predicting the enthalpy of formation of energetic molecules via conventional machine learning and GNN
Di Zhang, Qingzhao Chu, Dongping Chen
Physical Chemistry Chemical Physics (2024) Vol. 26, Iss. 8, pp. 7029-7041
Closed Access | Times Cited: 6
Predicting the performance and stability parameters of energetic materials (EMs) using a machine learning-based q-RASPR approach
Shubham Kumar Pandey, Kunal Roy
Energy Advances (2024) Vol. 3, Iss. 6, pp. 1293-1306
Open Access | Times Cited: 5
Machine learning-assisted quantitative prediction of thermal decomposition temperatures of energetic materials and their thermal stability analysis
Zhixiang Zhang, Yilin Cao, Chao Chen, et al.
Energetic Materials Frontiers (2023)
Open Access | Times Cited: 12
Predictive Modeling for Energetic Materials
Didier Mathieu
Challenges and advances in computational chemistry and physics (2025), pp. 265-310
Closed Access
Applications of Predictive Modeling for Energetic Materials
Nasser Sheibani
Challenges and advances in computational chemistry and physics (2025), pp. 339-364
Closed Access
Enhancing energy materials with data-driven methods: A roadmap to long-term hydrogen energy sustainability using machine learning
Cheng Li, Jianjun Ma, D. R. Gibson, et al.
International Journal of Hydrogen Energy (2025) Vol. 119, pp. 108-125
Closed Access
Predicting the Melting Point of Energetic Molecules Using a Learnable Graph Neural Fingerprint Model
Siwei Song, Yi Wang, Xiaolan Tian, et al.
The Journal of Physical Chemistry A (2023) Vol. 127, Iss. 19, pp. 4328-4337
Closed Access | Times Cited: 8
Machine Learning Models for High Explosive Crystal Density and Performance
J. Davis, Frank W. Marrs, M. J. Cawkwell, et al.
Chemistry of Materials (2024) Vol. 36, Iss. 22, pp. 11109-11118
Open Access | Times Cited: 1
Progress of Artificial Intelligence in Drug Synthesis and Prospect of Its Application in Nitrification of Energetic Materials
Bojun Tan, Jing Zhang, Chuan Xiao, et al.
Molecules (2023) Vol. 28, Iss. 4, pp. 1900-1900
Open Access | Times Cited: 2
Quantitatively Determining Melting Properties for Energetic Compounds Via Knowledge-Infused Molecular Graphs and Interpretable Deep Learning
Peng Chen, Haitao Liu, Chaoyang Zhang, et al.
(2024)
Closed Access
Prediction of impact sensitivity and electrostatic spark sensitivity for energetic compounds by machine learning and density functional theory
Qiong Wu, Xinyu Wang, Bin Yan, et al.
Journal of Materials Science (2024) Vol. 59, Iss. 20, pp. 8894-8910
Closed Access
High precision deep-learning model combined with high-throughput screening to discover fused [5,5] biheterocyclic energetic materials with excellent comprehensive properties
Youhai Liu, Fusheng Yang, Wenquan Zhang, et al.
RSC Advances (2024) Vol. 14, Iss. 33, pp. 23672-23682
Open Access
Quantitatively determining melting properties for energetic compounds via knowledge-infused molecular graphs and interpretable deep learning
Peng Chen, Haitao Liu, Chaoyang Zhang, et al.
Energetic Materials Frontiers (2024)
Open Access
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