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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:

Advancing material property prediction: using physics-informed machine learning models for viscosity
Alex K. Chew, Matthew Sender, Zachary Kaplan, et al.
Journal of Cheminformatics (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 20

Showing 20 citing articles:

Review of progress in calculation and simulation of high-temperature oxidation
Dongxin Gao, Zhao Shen, Kai Chen, et al.
Progress in Materials Science (2024) Vol. 147, pp. 101348-101348
Closed Access | Times Cited: 59
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
Multiscale computational modeling techniques in study and design of 2D materials: recent advances, challenges, and opportunities
Mohsen Asle Zaeem, Siby Thomas, Sepideh Kavousi, et al.
2D Materials (2024) Vol. 11, Iss. 4, pp. 042004-042004
Open Access | Times Cited: 6
A computational approach for prediction of viscosity of chemical compounds based on molecular structures
Sneha Das, Ram Kishore Roy, Tulshi Bezboruah
Results in Chemistry (2025), pp. 102039-102039
Open Access
ConvFeatNet Ensemble: Integrating Microstructure and Pre-defined Features for Enhanced Prediction of Porous Material Properties
Yuhai Li, Tianmu Li, Longwen Tang, et al.
Materials Science and Engineering A (2025), pp. 148173-148173
Closed Access
Physics-informed machine learning for Na-Ion conductivity and activation energy
Indrajeet Mandal, Sajid Mannan, Yuanqing Lu, et al.
Journal of Non-Crystalline Solids (2025) Vol. 657, pp. 123497-123497
Closed Access
Leveraging high-throughput molecular simulations and machine learning for the design of chemical mixtures
Alex K. Chew, Mohammad Atif Faiz Afzal, Zachary Kaplan, et al.
npj Computational Materials (2025) Vol. 11, Iss. 1
Open Access
Knowledge-driven eutectic electrolyte design for Zn-ion batteries
Jia‐Yaw Chang, Qianqian Liu, Chunwen Sun, et al.
Chemical Engineering Journal (2025), pp. 161712-161712
Closed Access
Role of artificial intelligence in the design and discovery of next-generation battery electrolytes
Manikantan R. Nair, Tribeni Roy
Chemical Physics Reviews (2025) Vol. 6, Iss. 1
Open Access
AI for AM: machine learning approach to design the base binder formulation for vat-photopolymerisation 3D printing of zirconia ceramics
Fatih Tarak, Leah Okoruwa, Basar Ozkan, et al.
Virtual and Physical Prototyping (2025) Vol. 20, Iss. 1
Open Access
Predictive quality analytics for the viscosity of water-based architectural paint manufacturing by using improved supervised machine learning and maximum dissimilarity algorithm
Robinson Barrionuevo, Diego Vallejo-Huanga, Paulina Morillo, et al.
Journal of Intelligent Manufacturing (2025)
Closed Access
Machine Learning-Based Process Optimization in Biopolymer Manufacturing: A Review
Ivan Malashin, D. A. Martysyuk, В С Тынченко, et al.
Polymers (2024) Vol. 16, Iss. 23, pp. 3368-3368
Open Access | Times Cited: 3
Optimal Molecular Design: Generative Active Learning Combining REINVENT with Precise Binding Free Energy Ranking Simulations
Hannes H. Loeffler, Shunzhou Wan, Marco Klähn, et al.
Journal of Chemical Theory and Computation (2024)
Open Access | Times Cited: 2
Machine Learning for Polymer Informatics
Ying Li, Tianle Yue
ACS in focus (2024)
Closed Access
Machine learning-based design of pincer catalysts for polymerization reaction
Shrabani Dinda, Tanvi Bhola, Suyash Pant, et al.
Journal of Catalysis (2024), pp. 115766-115766
Closed Access
Chemical Feature-Based Machine Learning Model for Predicting Photophysical Properties of BODIPY Compounds: Density Functional Theory and Quantitative Structure–Property Relationship Modeling
Gerardo M. Casañola‐Martín, Jing Wang, Jian‐Ge Zhou, et al.
Research Square (Research Square) (2024)
Open Access
Synergistic Modeling of Liquid Properties: Integrating Neural Network-Derived Molecular Features with Modified Kernel Models
Hyuntae Lim, YounJoon Jung
Journal of Chemical Theory and Computation (2024) Vol. 20, Iss. 22, pp. 9849-9856
Open Access
Exploring Molecules with Low Viscosity: Using Physics-Based Simulations and De Novo Design by Applying Reinforcement Learning
Nobuyuki Matsuzawa, Hiroyuki Maeshima, Keisuke Hayashi, et al.
Chemistry of Materials (2024)
Closed Access
Matini-Net: Versatile Material Informatics Research Framework for Feature Engineering and Deep Neural Network Design
Myeonghun Lee, Taehyun Park, Kyoungmin Min
Journal of Chemical Information and Modeling (2024)
Closed Access
Chemical feature-based machine learning model for predicting photophysical properties of BODIPY compounds: density functional theory and quantitative structure–property relationship modeling
Gerardo M. Casañola‐Martín, Jing Wang, Jian‐Ge Zhou, et al.
Journal of Molecular Modeling (2024) Vol. 31, Iss. 1
Closed Access
Application of machine learning in developing a quantitative structure–property relationship model for predicting the thermal decomposition temperature of nitrogen-rich energetic ionic salts
Yunling Zhang, Fan Liang, Chao Su, et al.
RSC Advances (2024) Vol. 14, Iss. 51, pp. 37737-37751
Open Access
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