Mapping Chemical Structure–Glass Transition Temperature Relationship through Artificial Intelligence-Reference-Cited by-同舟云学术

Mapping Chemical Structure–Glass Transition Temperature Relationship through Artificial Intelligence

Published:2021-02-05 Issue:4 Volume:54 Page:1811-1817
ISSN:0024-9297
Container-title:Macromolecules
language:en
Short-container-title:Macromolecules

Author:

Miccio Luis A.¹²³^ORCID,Schwartz Gustavo A.¹²^ORCID

Affiliation:

1. Centro de Física de Materiales (CSIC-UPV/EHU)-Materials Physics Center (MPC), P. M. de Lardizábal 5, 20018 San Sebastián, Spain

2. Donostia International Physics Center, P. M. de Lardizábal 4, 20018 San Sebastián, Spain

3. Institute of Materials Science and Technology (INTEMA), National Research Council (CONICET), Colón 10850, 7600 Mar del Plata, Buenos Aires, Argentina

Funder

Ministerio de Ciencia e Innovaci?n

Ekonomiaren Garapen eta Lehiakortasun Saila, Eusko Jaurlaritza

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,Inorganic Chemistry,Polymers and Plastics,Organic Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.macromol.0c02594

Reference32 articles.

1. Neural network prediction of glass-transition temperatures from monomer structure

2. Computational neural networks and the rational design of polymeric materials: the next generation polycarbonates

3. Prediction of Rheometric Properties of Compounds by Using Artificial Neural Networks

4. Prediction of Glass Transition Temperatures from Monomer and Repeat Unit Structure Using Computational Neural Networks

5. Artificial neural networks applied to polymer composites: a review

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