Applying machine learning and quantum chemistry to predict the glass transition temperatures of polymers-Reference-Cited by-同舟云学术

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Applying machine learning and quantum chemistry to predict the glass transition temperatures of polymers

Published:2024-04 Issue: Volume:238 Page:112933
ISSN:0927-0256
Container-title:Computational Materials Science
language:en
Short-container-title:Computational Materials Science

Author:

Hickey Kevin^ORCID,Feinstein Jeremy^ORCID,Sivaraman Ganesh,MacDonell Margaret,Yan Eugene,Matherson Carlos^ORCID,Coia Scott^ORCID,Xu Jason^ORCID,Picel Kurt

Funder

Office of Science

Bioenergy Technologies Office

Argonne National Laboratory

Laboratory Directed Research and Development

Publisher

Elsevier BV

Reference107 articles.

1. Degradation of natural rubber and synthetic elastomers;Bin Samsuri;Reference Module Mater. Sci. Mater. Eng,2017

2. Semiconductor chip underfill materials;Buchwalter;Encycl. Mater.: Sci. Technol.,2001

3. Ranking environmental degradation trends of plastic marine debris based on physical properties and molecular structure;Min;Nat. Commun.,2020

4. Contribution of the methylene group to the glass transition temperatures of polymers;Barton;Polymer (guildf),1968

5. The relationship between glass temperature, molar cohesion, and polymer structure;Hayes;J. Appl. Polym. Sci.,1961

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