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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:

High-Throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Using Interpretable Machine Learning
Mood Mohan, Karuna Devi Jetti, Sreelekha Guggilam, et al.
ACS Sustainable Chemistry & Engineering (2024) Vol. 12, Iss. 18, pp. 7040-7054
Closed Access | Times Cited: 9

Showing 9 citing articles:

Machine learning driven advancements in catalysis for predicting hydrogen evolution reaction activity
Priyanka Sinha, M. V. Jyothirmai, B. Moses Abraham, et al.
Materials Chemistry and Physics (2024) Vol. 326, pp. 129805-129805
Closed Access | Times Cited: 5
Modeling the Viscosity of Ionic Liquids and Their Mixtures Using ePC-SAFT and Free Volume Theory with an Ion-Based Approach
Zhida Zuo, Xiaohua Lü, Xiaoyan Ji
Industrial & Engineering Chemistry Research (2025)
Open Access
Determining whether biochar can effectively increase crop yields: A machine learning model development with imbalanced data
Weidong Jiao, Kechao Li, Min Zhou, et al.
Environmental Technology & Innovation (2025), pp. 104154-104154
Open Access
Development of a Generalizable Gradient Boosting Regression-Based Framework for Predicting the Properties of Single and Binary IL Systems
Kimia Fereydoon, Oscar Nordness
ACS Applied Engineering Materials (2025)
Closed Access
Promoting lignocellulosic biorefinery by machine learning: progress, perspectives and challenges
Xiaoyan Huang, Xue Zhang, Lei Xing, et al.
Bioresource Technology (2025), pp. 132434-132434
Closed Access
Assessing the Effectiveness of Neural Networks and Molecular Dynamics Simulations in Predicting Viscosity of Small Organic Molecules
Tianle Yue, Thanh Danh Nguyen, Vikas Varshney, et al.
The Journal of Physical Chemistry B (2025)
Closed Access
New insight into viscosity prediction of imidazolium-based ionic liquids and their mixtures with machine learning models
Amir Hossein Sheikhshoaei, Ali Sanati
Research Square (Research Square) (2025)
Closed Access
Physics-informed machine learning to predict solvatochromic parameters of designer solvents with case studies in CO2 and lignin dissolution
Mood Mohan, Nikhitha Gugulothu, Sreelekha Guggilam, et al.
Green Chemical Engineering (2024)
Open Access | Times Cited: 1
Developing Machine Learning Models for Predicting Multiple Physical Properties of Ionic Liquids through a Combined Constitution-Structure-Interaction Descriptor
Chenyang Wang, Cancan Song, Guobing Zhou, et al.
Journal of Chemical & Engineering Data (2024) Vol. 69, Iss. 12, pp. 4350-4361
Closed Access
Evaluating ionic liquid toxicity with machine learning and structural similarity methods
Rongli Shan, Runqi Zhang, Ying Gao, et al.
Green Chemical Engineering (2024)
Open Access
Additive Manufacturing with Cellulose‐Based Composites: Materials, Modeling, and Applications
Shuvodeep De, Shalini J. Rukmani, Xianhui Zhao, et al.
Advanced Functional Materials (2024)
Closed Access
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