Machine Learning with Enormous “Synthetic” Data Sets: Predicting Glass Transition Temperature of Polyimides Using Graph Convolutional Neural Networks-Reference-Cited by-同舟云学术

Machine Learning with Enormous “Synthetic” Data Sets: Predicting Glass Transition Temperature of Polyimides Using Graph Convolutional Neural Networks

Published:2022-11-17 Issue:48 Volume:7 Page:43678-43691
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

Volgin Igor V.¹,Batyr Pavel A.²,Matseevich Andrey V.³,Dobrovskiy Alexey Yu.¹,Andreeva Maria V.¹,Nazarychev Victor M.¹^ORCID,Larin Sergey V.¹^ORCID,Goikhman Mikhail Ya.¹,Vizilter Yury V.²,Askadskii Andrey A.³⁴,Lyulin Sergey V.¹^ORCID

Affiliation:

1. Institute of Macromolecular Compounds of the Russian Academy of Sciences (IMC RAS), St. Petersburg 199004, Russian Federation

2. Federal State Unitary Enterprise “State Research Institute of Aviation Systems” (GosNIIAS), Moscow 125167, Russian Federation

3. A.N. Nesmeyanov Institute of Organoelement Compounds of Russian Academy of Sciences (INEOS RAS), Moscow 119991, Russian Federation

4. Moscow State University of Civil Engineering (MGSU), Moscow 129337, Russian Federation

Funder

Russian Science Foundation

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acsomega.2c04649

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