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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-guided property prediction of energetic materials: Recent advances, challenges, and perspectives
Xiaolan Tian, Siwei Song, Fang Chen, et al.
Energetic Materials Frontiers (2022) Vol. 3, Iss. 3, pp. 177-186
Open Access | Times Cited: 50

Showing 26-50 of 50 citing articles:

High Glass Transition Temperature Fluorinated Polymers Based on Transfer Learning with Small Experimental Data
Jin‐Hoon Yang, Ji‐Young Lee, Hajin Kwon, et al.
Macromolecular Rapid Communications (2024) Vol. 45, Iss. 15
Open Access | Times Cited: 2
Nanosculptured tungsten oxide: High-efficiency SERS sensor for explosives tracing
Vasyl Shvalya, Jaka Olenik, Damjan Vengust, et al.
Journal of Hazardous Materials (2024) Vol. 476, pp. 135171-135171
Open Access | Times Cited: 2
Structural Control of Energetic Triaminoguanidine Nitrate Polymer Nanostructures for Reduced Thermal Reactivity
Xue-Xue Zhang, Zhihua Xue, Jun-Lian Hao, et al.
ACS Applied Nano Materials (2023) Vol. 6, Iss. 19, pp. 17890-17901
Closed Access | Times Cited: 6
Machine learning: An effective technical method for future use in assessing the effectiveness of phosphorus-dissolving microbial agroremediation
Juai Wu, Fangzhou Zhao
Frontiers in Bioengineering and Biotechnology (2023) Vol. 11
Open Access | Times Cited: 5
A novel AI-based combustion diagnostic technology for the identification of chemical source information via flame images: Fuel type and reaction condition
Mingfei Chen, Z. Zou, Kaile Zhou, et al.
Combustion and Flame (2023) Vol. 260, pp. 113208-113208
Closed Access | Times Cited: 4
OPTIMIZATION OF CHEMICAL SYNTHESIS OUTPUT WITH TOPSIS
Taraneh Javanbakht
Ukrainian Journal of Mechanical Engineering and Materials Science (2024) Vol. 10, Iss. 1, pp. 63-69
Closed Access | Times Cited: 1
Machine learning and experimental study on a novel Cr–Mo–V–Ti high manganese steel: Microstructure, mechanical properties and abrasive wear behavior
Tao Xu, Bin-guo Fu, Yanfei Jiang, et al.
Journal of Materials Research and Technology (2024) Vol. 31, pp. 1270-1281
Open Access | Times Cited: 1
Combustion condition predictions for C2-C4 alkane and alkene fuels via machine learning methods
Mingfei Chen, Jiaying He, Xuan Zhao, et al.
Fuel (2024) Vol. 373, pp. 132375-132375
Closed Access | Times Cited: 1
Covalent Organic Frameworks-Based Fluorescence Sensor Array and QSAR Study for Identification of Energetic Heterocyclic Compounds
Haikang Han, Longyi Zhu, Shengyuan Deng, et al.
Analytical Chemistry (2024)
Closed Access | Times Cited: 1
Molecular Data Programming: Towards Molecule Pseudo-labeling with Systematic Weak Supervision
Xin Juan, Kaixiong Zhou, Ninghao Liu, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024), pp. 308-318
Closed Access | Times Cited: 1
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
Screening adsorbent-working solution pairs for adsorption-driven osmotic heat engines based on experimental water adsorption isotherm database and machine learning
Yanan Zhao, Zhilu Liu, Mingliang Li, et al.
Process Safety and Environmental Protection (2022) Vol. 168, pp. 22-31
Closed Access | Times Cited: 7
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
Machine learning determination of new Hammett's constants for meta- and para-substituted benzoic acid derivatives employing quantum chemical atomic charge methods
Gabriel Monteiro‐de‐Castro, Júlio César Duarte, Itamar Borges
(2023)
Open Access | Times Cited: 2
Discovery of high energy and stable prismane derivatives by the high-throughput computation and machine learning combined strategy
Shitai Guo, Jing Huang, Wen Qian, et al.
FirePhysChem (2023) Vol. 4, Iss. 1, pp. 55-62
Open Access | Times Cited: 2
Rapid identification of cocrystal components of explosives based on Raman spectroscopy and principal component analysis
Weiping Xian, Zihan Wang, Lingyan Shi, et al.
Vibrational Spectroscopy (2024) Vol. 132, pp. 103689-103689
Closed Access
Optimizing the mechanical performance of A356–Sc–Sr alloy via combining machine learning and mechanical stirring under vacuum
Shuai Pan, Jingming Zheng, Yu Wang, et al.
Materials Characterization (2024) Vol. 212, pp. 114011-114011
Closed Access
Machine learning approaches for screening of materials in flexible electronic devices
Siyan Deng
(2024)
Open Access
Unraveling the Mechanism of Ductility Enhancement in W-Ta Alloys with Machine-Learning Interatomic Potentials
Haoyu Hu, Chao Zhang, Rui Yue, et al.
(2024)
Closed Access
Modeling High Energy Molecules and Screening to Find Novel High Energy Candidates
Mazal Rachamim, Abraham J. Domb, Amiram Goldblum
ACS Omega (2024) Vol. 9, Iss. 42, pp. 42709-42720
Open Access
Unraveling ductility enhancement mechanisms in W-Ta alloys using machine-learning potential
Haoyu Hu, Chao Zhang, Rui Yue, et al.
International Journal of Mechanical Sciences (2024), pp. 109911-109911
Closed Access
The use of artificial neural networks in creating ceramic and refractory materials
S. L. Ligezin, Yaroslav Pitak
Scientific research on refractories and technical ceramics (2024), Iss. 124, pp. 122-133
Open Access
Predicting Enthalpy of Formation of Energetic Compounds by Machine Learning: Comparison of Featurization Methods and Algorithms
Xiaolan Tian, Xiujuan Qi, Yi Wang, et al.
Propellants Explosives Pyrotechnics (2022) Vol. 48, Iss. 4
Closed Access | Times Cited: 2
Which molecular properties determine the impact sensitivity of an explosive? A machine learning quantitative investigation of nitroaromatic explosives
Itamar Borges, Júlio César Duarte, Romulo Dias da Rocha
(2022)
Open Access
Research on the Prediction of US House Prices Based on Machine Learning
Yunling He
BCP Business & Management (2022) Vol. 32, pp. 385-390
Open Access
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