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:

Data-driven approaches for structure-property relationships in polymer science for prediction and understanding
Yoshifumi Amamoto
Polymer Journal (2022) Vol. 54, Iss. 8, pp. 957-967
Closed Access | Times Cited: 32

Showing 1-25 of 32 citing articles:

Machine learning for analyses and automation of structural characterization of polymer materials
Shizhao Lu, Arthi Jayaraman
Progress in Polymer Science (2024) Vol. 153, pp. 101828-101828
Closed Access | Times Cited: 12
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
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
Recent advances and challenges in experiment-oriented polymer informatics
Kan Hatakeyama‐Sato
Polymer Journal (2022) Vol. 55, Iss. 2, pp. 117-131
Closed Access | Times Cited: 22
Rapid and accurate predictions of perfect and defective material properties in atomistic simulation using the power of 3D CNN-based trained artificial neural networks
Iman Peivaste, Saba Ramezani, Ghasem Alahyarizadeh, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 4
Applications of Predictive Modeling for Polymers
Fangyou Yan, Jiayi Zhu, Xiaojie Feng, et al.
Challenges and advances in computational chemistry and physics (2025), pp. 149-173
Closed Access
Applications of Predictive Modeling for Selected Properties of Polymers
Mohammad Hossein Keshavarz
Challenges and advances in computational chemistry and physics (2025), pp. 85-117
Closed Access
Data-Driven Prediction of Flory–Huggins Parameter for Quantifying Polymer–Solvent Interaction
Jiayi Zhu, Lihong Lin, H. Niu, et al.
Industrial & Engineering Chemistry Research (2025)
Closed Access
How useful are data-driven methods for building-level greywater treatment from a process engineering perspective?
Siyuan Wang, Mariane Yvonne Schneider, Eveline I.P. Volcke, et al.
Water Research X (2025), pp. 100343-100343
Open Access
Pair-Variational Autoencoders for Linking and Cross-Reconstruction of Characterization Data from Complementary Structural Characterization Techniques
Shizhao Lu, Arthi Jayaraman
JACS Au (2023) Vol. 3, Iss. 9, pp. 2510-2521
Open Access | Times Cited: 9
Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation
Raghav Dangayach, Nohyeong Jeong, Elif Demirel, et al.
Environmental Science & Technology (2024)
Open Access | Times Cited: 3
Application of Digital Methods in Polymer Science and Engineering
Timo Schuett, Patrick Endres, Tobias Standau, et al.
Advanced Functional Materials (2023) Vol. 34, Iss. 8
Open Access | Times Cited: 8
Complex network analysis of completely extended chains in rubber elasticity
Yoshifumi Amamoto, Ken Kojio, Atsushi Takahara, et al.
(2024)
Open Access | Times Cited: 2
Data Science-Centric Design, Discovery, and Evaluation of Novel Synthetically Accessible Polyimides with Desired Dielectric Constant
Mengxian Yu, Qingzhu Jia, Qiang Wang, et al.
Chemical Science (2024)
Open Access | Times Cited: 2
Exploring Metal Nanocluster Catalysts for Ammonia Synthesis Using Informatics Methods: A Concerted Effort of Bayesian Optimization, Swarm Intelligence, and First-Principles Computation
Yuta Tsuji, Yuta Yoshioka, Kazuki Okazawa, et al.
ACS Omega (2023) Vol. 8, Iss. 33, pp. 30335-30348
Open Access | Times Cited: 5
A QSPR study for predicting θ(LCST) and θ(UCST) in binary polymer solutions
Jiaqi Wu, Xue‐Qing Gong, Qiang Wang, et al.
Chemical Engineering Science (2022) Vol. 267, pp. 118326-118326
Closed Access | Times Cited: 8
Graph neural networks with molecular segmentation for property prediction and structure–property relationship discovery
Zhudan Chen, Dazi Li, Minghui Liu, et al.
Computers & Chemical Engineering (2023) Vol. 179, pp. 108403-108403
Closed Access | Times Cited: 4
Leveraging Triphenylphosphine-Containing Polymers to Explore Design Principles for Protein-Mimetic Catalysts
Matthew A. Sanders, Supraja S. Chittari, Jack R. Foley, et al.
(2024)
Open Access | Times Cited: 1
Predicting and screening high-performance polyimide membranes using negative correlation based deep ensemble methods
Ruochen Xi, Hongjing Liu, Xueli Liu, et al.
Analytical Methods (2024) Vol. 16, Iss. 34, pp. 5845-5863
Closed Access | Times Cited: 1
Self-supervised graph neural networks for polymer property prediction
Qinghe Gao, Tammo Dukker, Artur M. Schweidtmann, et al.
Molecular Systems Design & Engineering (2024)
Open Access | Times Cited: 1
Assessment of Solubility Behavior of a Copolymer in Supercritical CO2 and Organic Solvents: Neural Network Prediction and Statistical Analysis
Divya Baskaran, Uma Sankar Behera, Hun‐Soo Byun
ACS Omega (2024) Vol. 9, Iss. 39, pp. 40941-40955
Open Access | Times Cited: 1
A machine learning approach to designing tough and degradable polyamides based on multiblock structures
Yoshifumi Amamoto, Chie Koganemaru, Ken Kojio, et al.
(2023)
Open Access | Times Cited: 3
A perspective on data-driven screening and discovery of polymer membranes for gas separation, from the molecular structure to the industrial performance
Eleonora Ricci, María Grazia De Angelis
Reviews in Chemical Engineering (2023) Vol. 40, Iss. 5, pp. 567-600
Closed Access | Times Cited: 3
Leveraging Triphenylphosphine-Containing Polymers to Explore Design Principles for Protein-Mimetic Catalysts
Matthew A. Sanders, Supraja S. Chittari, Jack R. Foley, et al.
Journal of the American Chemical Society (2024) Vol. 146, Iss. 25, pp. 17404-17413
Closed Access
Semiautomated experiment with a robotic system and data generation by foundation models for synthesis of polyamic acid particles
Kan Hatakeyama‐Sato, Hiroki Ishikawa, Shinya Takaishi, et al.
Polymer Journal (2024) Vol. 56, Iss. 11, pp. 977-986
Open Access
Page 1 - Next Page

Scroll to top