Predicting the Mechanical Properties of Polyurethane Elastomers Using Machine Learning-Reference-Cited by-同舟云学术

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Predicting the Mechanical Properties of Polyurethane Elastomers Using Machine Learning

Published:2022-10-08 Issue:3 Volume:41 Page:422-431
ISSN:0256-7679
Container-title:Chinese Journal of Polymer Science
language:en
Short-container-title:Chin J Polym Sci

Author:

Ding Fang,Liu Lun-Yang,Liu Ting-Li,Li Yun-Qi,Li Jun-Peng,Sun Zhao-Yan

Publisher

Springer Science and Business Media LLC

Subject

Polymers and Plastics,Organic Chemistry,General Chemical Engineering

Link

https://link.springer.com/content/pdf/10.1007/s10118-022-2838-6.pdf

Reference52 articles.

1. Liu, T.; Liu, L.; Cui, F.; Ding, F.; Zhang, Q.; Li, Y. Predicting the performance of polyvinylidene fluoride, polyethersulfone and polysulfone filtration membranes using machine learning. J. Mater. Chem. A 2020, 8, 21862–21871.

2. Liu, L.; Chen, W.; Li, Y. A statistical study of proton conduction in nafion®-based composite membranes: prediction, filler selection and fabrication methods. J. Membr. Sci. 2018, 549, 393–402.

3. Liu, L.; Liu, T.; Ding, F.; Zhang, H.; Zheng, J.; Li, Y. Exploration of the polarization curve for proton-exchange membrane fuel cells. ACS Appl. Mater. Interfaces 2021, 13, 58838–58847.

4. Jin, K.; Luo, H.; Wang, Z.; Wang, H.; Tao, J. Composition optimization of a high-performance epoxy resin based on molecular dynamics and machine learning. Mater. Des. 2020, 194, 108932.

5. Liu, L. Y.; Chen, W. D.; Liu, T. L.; Kong, X. X.; Zheng, J. F.; Li, Y. Q. Rational design of hydrocarbon-based sulfonated copolymers for proton exchange membranes. J. Mater. Chem. A 2019, 7, 11847–11857.

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