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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 with Enormous “Synthetic” Data Sets: Predicting Glass Transition Temperature of Polyimides Using Graph Convolutional Neural Networks
Igor V. Volgin, Pavel A. Batyr, Andrey Matseevich, et al.
ACS Omega (2022) Vol. 7, Iss. 48, pp. 43678-43691
Open Access | Times Cited: 30

Showing 1-25 of 30 citing articles:

Quantitative structure-property relationship (QSPR) framework assists in rapid mining of highly Thermostable polyimides
Mengxian Yu, Yajuan Shi, Xiao Liu, et al.
Chemical Engineering Journal (2023) Vol. 465, pp. 142768-142768
Closed Access | Times Cited: 15
Application of machine learning in polyimide structure design and property regulation
Wenjia Huo, Haiyue Wang, Liying Guo, et al.
High Performance Polymers (2025)
Closed Access
Machine Learning in Polymer Research
Wei Ge, R. Silva‐González, Yanan Fan, et al.
Advanced Materials (2025)
Open Access
Generalized Model for Predicting Gas Permeability of Glassy Polymers and Residual Neural Networks as a Tool for Its Improvement
D. A. Tsarev, В. Е. Рыжих, Н. Н. Белов, et al.
Polymer Science Series C (2025)
Closed Access
A glimpse inside materials: Polymer structure – Glass transition temperature relationship as observed by a trained artificial intelligence
Luis A. Miccio, Claudia Borredon, Gustavo A. Schwartz
Computational Materials Science (2024) Vol. 236, pp. 112863-112863
Closed Access | Times Cited: 4
Prediction and Interpretability of Glass Transition Temperature of Homopolymers by Data-Augmented Graph Convolutional Neural Networks
Junyang Hu, Zean Li, Jiaping Lin, et al.
ACS Applied Materials & Interfaces (2023) Vol. 15, Iss. 46, pp. 54006-54017
Closed Access | Times Cited: 11
Synthetic data enable experiments in atomistic machine learning
John L. A. Gardner, Zoé Faure Beaulieu, Volker L. Deringer
Digital Discovery (2023) Vol. 2, Iss. 3, pp. 651-662
Open Access | Times Cited: 10
Enhancing deep learning predictive models with HAPPY (Hierarchically Abstracted rePeat unit of PolYmers) representation
Jihun Ahn, Gabriella Pasya Irianti, Yeojin Choe, et al.
npj Computational Materials (2024) Vol. 10, Iss. 1
Open Access | Times Cited: 3
Thermal stability prediction of copolymerized polyimides via an interpretable transfer learning model
Yu Zhang, Yating Fang, Ling Li, et al.
Journal of Materials Informatics (2024) Vol. 4, Iss. 2
Open Access | Times Cited: 3
The Effect of Mechanical Elongation on the Thermal Conductivity of Amorphous and Semicrystalline Thermoplastic Polyimides: Atomistic Simulations
Victor M. Nazarychev, Sergey V. Lyulin
Polymers (2023) Vol. 15, Iss. 13, pp. 2926-2926
Open Access | Times Cited: 7
Property Prediction and Structural Feature Extraction of Polyimide Materials Based on Machine Learning
Han Zhang, Haoyuan Li, Hanshen Xin, et al.
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 17, pp. 5473-5483
Closed Access | Times Cited: 7
Toward the design of graft-type proton exchange membranes with high proton conductivity and low water uptake: A machine learning study
Shin‐ichi Sawada, Yukiko Sakamoto, Kimito Funatsu, et al.
Journal of Membrane Science (2023) Vol. 692, pp. 122169-122169
Closed Access | Times Cited: 6
Gibbs–Helmholtz Graph Neural Network for the Prediction of Activity Coefficients of Polymer Solutions at Infinite Dilution
Edgar Iván Sánchez Medina, Sreekanth Kunchapu, Kai Sundmacher
The Journal of Physical Chemistry A (2023) Vol. 127, Iss. 46, pp. 9863-9873
Open Access | Times Cited: 6
Prediction and Interpretability Study of the Glass Transition Temperature of Polyimide Based on Machine Learning with Quantitative Structure–Property Relationship (Tg-QSPR)
Tianyong Zhang, Suisui Wang, Yamei Chai, et al.
The Journal of Physical Chemistry B (2024) Vol. 128, Iss. 36, pp. 8807-8817
Closed Access | Times Cited: 1
Data-Driven Design of Novel Polymer Excipients for Pharmaceutical Amorphous Solid Dispersions
Elena J. Di Mare, Ashish Punia, Matthew S. Lamm, et al.
Bioconjugate Chemistry (2024) Vol. 35, Iss. 9, pp. 1363-1372
Closed Access | Times Cited: 1
Understanding Polymers Through Transfer Learning and Explainable AI
Luis A. Miccio
Applied Sciences (2024) Vol. 14, Iss. 22, pp. 10413-10413
Open Access | Times Cited: 1
Large-Scale Glass-Transition Temperature Prediction with an Equivariant Neural Network for Screening Polymers
Zheng Long, Hongmei Lü, Zhimin Zhang
ACS Omega (2024)
Open Access
Designing Heat-Resistant and Moldable Polyester Resin by the Integration of Machine Learning Models with Expert Knowledge
Fan Zhang, Tomoyuki Miyao, Yuuta Izumiya, et al.
ACS Applied Polymer Materials (2024) Vol. 6, Iss. 8, pp. 4579-4586
Open Access
Glass Transition Temperature Prediction of Polymers via Graph Reinforcement Learning
Caibo Dong, Dazi Li, Jun Liu
Langmuir (2024) Vol. 40, Iss. 35, pp. 18568-18580
Closed Access
Unified physio-thermodynamic descriptors via learned CO2 adsorption properties in metal-organic frameworks
Emily Lin, Yang Zhong, Gang Chen, et al.
(2024)
Closed Access
Pre-trained Mol2Vec Embeddings as a Tool for Predicting Polymer Properties
Ivan Zlobin, Nikita Toroptsev, Gleb M. Averochkin, et al.
Chinese Journal of Polymer Science (2024)
Closed Access
Permeability of Polymer Membranes Based on Polyimides Towards Helium
А.А. Аскадский, Andrey Matseevich, Igor V. Volgin, et al.
Polymer Science Series A (2023) Vol. 65, Iss. 2, pp. 192-212
Closed Access | Times Cited: 1
A Novel Machine Learning Solution for the Inverse Heat Conduction Problem with Synthetic Datasets
Zoltán Biczó, Sándor Szénási, Imre Felde
(2023), pp. 000117-000122
Closed Access | Times Cited: 1
Predicting the pair correlation functions of silicate and borosilicate glasses using machine learning
Kumar Ayush, Pooja Sahu, Sk. Musharaf Ali, et al.
Physical Chemistry Chemical Physics (2023) Vol. 26, Iss. 2, pp. 1094-1104
Closed Access | Times Cited: 1
New Polymers In Silico Generation and Properties Prediction
Andrey A. Knizhnik, П. В. Комаров, Б. В. Потапкин, et al.
Nanomanufacturing (2023) Vol. 4, Iss. 1, pp. 1-26
Open Access | Times Cited: 1
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