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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:

A graph representation of molecular ensembles for polymer property prediction
Matteo Aldeghi, Connor W. Coley
Chemical Science (2022) Vol. 13, Iss. 35, pp. 10486-10498
Open Access | Times Cited: 86

Showing 1-25 of 86 citing articles:

polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics
Christopher Kuenneth, Rampi Ramprasad
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 92
Emerging Trends in Machine Learning: A Polymer Perspective
Tyler B. Martin, Debra J. Audus
ACS Polymers Au (2023) Vol. 3, Iss. 3, pp. 239-258
Open Access | Times Cited: 85
TransPolymer: a Transformer-based language model for polymer property predictions
Changwen Xu, Yuyang Wang, Amir Barati Farimani
npj Computational Materials (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 85
Chemistry-Informed Machine Learning for Polymer Electrolyte Discovery
Gabriel Bradford, Jeffrey Lopez, Jurģis Ruža, et al.
ACS Central Science (2023) Vol. 9, Iss. 2, pp. 206-216
Open Access | Times Cited: 64
Applied machine learning as a driver for polymeric biomaterials design
Samantha M. McDonald, Emily K. Augustine, Quinn Lanners, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 55
Data-Driven Design of Polymer-Based Biomaterials: High-throughput Simulation, Experimentation, and Machine Learning
Roshan Patel, Michael Webb
ACS Applied Bio Materials (2023) Vol. 7, Iss. 2, pp. 510-527
Closed Access | Times Cited: 51
Advancing 3D bioprinting through machine learning and artificial intelligence
Srikanthan Ramesh, Akash Deep, Ali Tamayol, et al.
Bioprinting (2024) Vol. 38, pp. e00331-e00331
Closed Access | Times Cited: 34
Design of functional and sustainable polymers assisted by artificial intelligence
Tran Doan Huan, Rishi Gurnani, Chiho Kim, et al.
Nature Reviews Materials (2024) Vol. 9, Iss. 12, pp. 866-886
Closed Access | Times Cited: 26
Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm
Chunhui Xie, Haoke Qiu, Lu Liu, et al.
SmartMat (2025) Vol. 6, Iss. 1
Open Access | Times Cited: 2
Self-driving laboratories: A paradigm shift in nanomedicine development
Riley J. Hickman, Pauric Bannigan, Zeqing Bao, et al.
Matter (2023) Vol. 6, Iss. 4, pp. 1071-1081
Open Access | Times Cited: 41
Polymer graph neural networks for multitask property learning
Owen Queen, Gavin A. McCarver, Saitheeraj Thatigotla, et al.
npj Computational Materials (2023) Vol. 9, Iss. 1
Open Access | Times Cited: 40
Multitask Neural Network for Mapping the Glass Transition and Melting Temperature Space of Homo- and Co-Polyhydroxyalkanoates Using σProfiles Molecular Inputs
Abir Boublia, Tarek Lemaoui, Jawaher AlYammahi, et al.
ACS Sustainable Chemistry & Engineering (2022) Vol. 11, Iss. 1, pp. 208-227
Closed Access | Times Cited: 39
The Rise of Machine Learning in Polymer Discovery
Cheng Yan, Guoqiang Li
Advanced Intelligent Systems (2023) Vol. 5, Iss. 4
Open Access | Times Cited: 38
Polymer Informatics at Scale with Multitask Graph Neural Networks
Rishi Gurnani, Christopher Kuenneth, Aubrey Toland, et al.
Chemistry of Materials (2023) Vol. 35, Iss. 4, pp. 1560-1567
Open Access | Times Cited: 38
A Model Ensemble Approach Enables Data-Driven Property Prediction for Chemically Deconstructable Thermosets in the Low-Data Regime
Yasmeen AlFaraj, Somesh Mohapatra, Peyton Shieh, et al.
ACS Central Science (2023) Vol. 9, Iss. 9, pp. 1810-1819
Open Access | Times Cited: 24
Accelerating Discovery of Polyimides with Intrinsic Microporosity for Membrane‐Based Gas Separation: Synergizing Physics‐Informed Performance Metrics and Active Learning
Mao Wang, Jianwen Jiang
Advanced Functional Materials (2024) Vol. 34, Iss. 23
Open Access | Times Cited: 15
Artificial Intelligence for Conjugated Polymers
Qiaomu Yang, Aikaterini Vriza, Cesar A. Castro Rubio, et al.
Chemistry of Materials (2024) Vol. 36, Iss. 6, pp. 2602-2622
Closed Access | Times Cited: 14
Heat-Resistant Polymer Discovery by Utilizing Interpretable Graph Neural Network with Small Data
Haoke Qiu, Jingying Wang, Xuepeng Qiu, et al.
Macromolecules (2024) Vol. 57, Iss. 8, pp. 3515-3528
Closed Access | Times Cited: 13
AI for organic and polymer synthesis
Hong Xin, Qi Yang, Kuangbiao Liao, et al.
Science China Chemistry (2024) Vol. 67, Iss. 8, pp. 2461-2496
Closed Access | Times Cited: 11
When Do Quantum Mechanical Descriptors Help Graph Neural Networks to Predict Chemical Properties?
Shih‐Cheng Li, Haoyang Wu, Angiras Menon, et al.
Journal of the American Chemical Society (2024) Vol. 146, Iss. 33, pp. 23103-23120
Closed Access | Times Cited: 10
Machine Learning in Polymer Science: Emerging Trends and Future Directions
Pradeepta Kumar Sarangi, Nidhi Goel, Ashok Kumar Sahoo, et al.
Macromolecular Symposia (2025) Vol. 414, Iss. 1
Closed Access | Times Cited: 1
Representing Polymers as Periodic Graphs with Learned Descriptors for Accurate Polymer Property Predictions
Evan R. Antoniuk, Peggy Li, Bhavya Kailkhura, et al.
Journal of Chemical Information and Modeling (2022) Vol. 62, Iss. 22, pp. 5435-5445
Open Access | Times Cited: 34
Machine learning-guided discovery of polymer membranes for CO2 separation with genetic algorithm
Yasemin Basdogan, Dylan R. Pollard, Tejus Shastry, et al.
Journal of Membrane Science (2024) Vol. 712, pp. 123169-123169
Closed Access | Times Cited: 6
On-demand reverse design of polymers with PolyTAO
Haoke Qiu, Zhao‐Yan Sun
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 6
Electronic, redox, and optical property prediction of organic π-conjugated molecules through a hierarchy of machine learning approaches
Vinayak Bhat, Parker Sornberger, Balaji Sesha Sarath Pokuri, et al.
Chemical Science (2022) Vol. 14, Iss. 1, pp. 203-213
Open Access | Times Cited: 23
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