Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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 and Shapley Additive Explanation-based interpretable prediction of the electrocatalytic performance of N-doped carbon materials
Shiteng Tan, Ruikun Wang, Gaoke Song, et al.
Fuel (2023) Vol. 355, pp. 129469-129469
Closed Access | Times Cited: 18

Showing 18 citing articles:

Rare earth modified carbon-based catalysts for oxygen electrode reactions: A machine learning assisted density functional theory investigation
Qiming Fu, Tao Xu, Daomiao Wang, et al.
Carbon (2024) Vol. 223, pp. 119045-119045
Closed Access | Times Cited: 21
Machine Learning-Assisted Study of RENxC6–x-Doped Graphene as Potential Electrocatalysts for Oxygen Electrode Reactions
Qiming Fu, Tao Xu, Chenggong He, et al.
Langmuir (2024) Vol. 40, Iss. 20, pp. 10726-10736
Closed Access | Times Cited: 9
Optimizing building energy performance predictions: A comparative study of artificial intelligence models
Omer A. Alawi, Haslinda Mohamed Kamar, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬
Journal of Building Engineering (2024) Vol. 88, pp. 109247-109247
Closed Access | Times Cited: 8
Self-assembly of architected macromolecules: Bridging a gap between experiments and simulations
Ji Woong Yu, Changsu Yoo, Suchan Cho, et al.
Chemical Physics Reviews (2025) Vol. 6, Iss. 1
Open Access
Interpretable physics-informed machine learning approaches to accelerate electrocatalyst development
Hao Wu, Mingxuan Chen, Hao Cheng, et al.
Journal of Materials Informatics (2025) Vol. 5, Iss. 2
Open Access
Design principles of non-noble metal catalysts for high-performance rechargeable Zn-air batteries
Pengxiang Liu, Yaqian Wang, Ruitao Lv, et al.
Energy storage materials (2025), pp. 104155-104155
Closed Access
Interpretable machine learning model for temperature prediction in coal pulverizer of thermal power plants
Suhailam Pullanikkattil, Raju Yerolla, Ramón Vilanova, et al.
International Journal of Coal Preparation and Utilization (2025), pp. 1-25
Closed Access
Fine-tuned and explainable machine learning models for temperature-dependent rheological behavior prediction of magnetorheological materials
Irfan Bahiuddin, Fitrian Imaduddin, Kasma Diana Saharuddin, et al.
Neural Computing and Applications (2025)
Closed Access
Machine Learning-Driven Design of Rare Metal Doped Niobium Alloys with Enhanced Strength and Ductility
Z. Xiong, ZhaoKun Song, Jianwei Li, et al.
Journal of Materials Research and Technology (2025)
Open Access
Data‐Driven Design of Spinodal Decomposition in (Ti, Zr, Hf)C Composite Carbides for Optimizing the Hardness‐Toughness Trade‐Off
Zhixuan Zhang, Chengyu Hou, Zongyao Zhang, et al.
Advanced Functional Materials (2025)
Closed Access
Evaluation of empirical and machine learning models for predicting shear wave velocity of granular soils based on laboratory element tests
Zohreh Mousavi, Meysam Bayat, Jun Yang, et al.
Soil Dynamics and Earthquake Engineering (2024) Vol. 183, pp. 108805-108805
Closed Access | Times Cited: 3
Machine learning framework for evaluating fracturing-flooding effectiveness: From prediction to decision recommendations
Xiang Wang, Xianxiang Chu, Yixin Xie, et al.
Fuel (2024) Vol. 381, pp. 133299-133299
Closed Access | Times Cited: 3
Machine Learning Assisted Selection of Catalyst for γ-Valerolactone Hydrogenation from Levulinic Acid
Liu Dongyu, Zhen Jia, Lu Shen, et al.
ACS Sustainable Chemistry & Engineering (2024) Vol. 12, Iss. 44, pp. 16340-16353
Closed Access | Times Cited: 2
Machine learning electron-phonon interactions in 2D semiconducting materials: The case of zero-point renormalization
Anubhab Haldar, Quentin Clark, Marios Zacharias, et al.
Research Square (Research Square) (2024)
Open Access | Times Cited: 1
Redefining the Stability of Water Oxidation Electrocatalysts: Insights from Materials Databases and Machine Learning
Raúl A. Márquez, Erin Elizabeth Oefelein, Thuy Vy Le, et al.
ACS Materials Letters (2024) Vol. 6, Iss. 7, pp. 2905-2918
Closed Access | Times Cited: 1
Digitization impact on future housing building industry mode
Yao Wang, Hongyu Ye, Jiexi Xiong, et al.
Journal of Building Engineering (2024) Vol. 96, pp. 110202-110202
Closed Access | Times Cited: 1
Prediction and analysis of mechanical properties of hot-rolled strip steel based on an interpretable machine learning
Xiaojun Wang, Xu Li, Hao Yuan, et al.
Materials Today Communications (2024) Vol. 40, pp. 109997-109997
Closed Access | Times Cited: 1
Significantly enhanced properties of micro-ionic thermocells through the microstructure interfacial effect
Hongwei Chen, Shuqi Zhao, Haoyu Zou, et al.
Journal of Materials Chemistry A (2024) Vol. 12, Iss. 36, pp. 24488-24498
Closed Access | Times Cited: 1
Machine learning electron-phonon interactions in two-dimensional semiconducting materials: The case of zero-point renormalization
Anubhab Haldar, Quentin Clark, Marios Zacharias, et al.
Physical Review Materials (2024) Vol. 8, Iss. 10
Closed Access
Page 1

Scroll to top