Discovery of thermosetting polymers with low hygroscopicity, low thermal expansivity, and high modulus by machine learning-Reference-Cited by-同舟云学术

Discovery of thermosetting polymers with low hygroscopicity, low thermal expansivity, and high modulus by machine learning

Published:2023 Issue:24 Volume:11 Page:12918-12927
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Xu Xinyao¹,Zhao Wenlin¹,Hu Yaxi¹,Wang Liquan¹^ORCID,Lin Jiaping¹^ORCID,Qi Huimin¹^ORCID,Du Lei¹

Affiliation:

1. Shanghai Key Laboratory of Advanced Polymeric Materials, Key Laboratory for Ultrafine Materials of Ministry of Education, Frontiers Science Center for Materiobiology and Dynamic Chemistry, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

Abstract

Based on the workflow of the materials genome approach, the challenge of enhancing multiple conflicting properties of an advanced thermosetting polymer of polycyanurates was addressed through high-throughout screening using machine learning.

Funder

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/TA/D2TA09272G

Reference76 articles.

1. Polycyanate esters: Science and applications

2. Effect of Segmental Configuration on Properties of n-Propyl-Bridged Polycyanurate Networks

3. Examination of the Thermal and Thermomechanical Behavior of Novel Cyanate Ester Homopolymers and Blends with Low Coefficients of Thermal Expansion