Open Access
Open access
volume 7 issue 48 pages 43678-43691

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

Pavel A Batyr 2
Andrey V Matseevich 3
Alexey Yu Dobrovskiy 1
Maria V. Andreeva 1
S V Larin 1
Mikhail Ya Goikhman 1
Yury V. Vizilter 2
Andrey A Askadskii 3, 4
Publication typeJournal Article
Publication date2022-11-17
scimago Q1
wos Q2
SJR0.773
CiteScore7.1
Impact factor4.3
ISSN24701343
General Chemistry
General Chemical Engineering
Found 
Found 

Top-30

Journals

1
2
3
Computational Materials Science
3 publications, 5.77%
Polymers
2 publications, 3.85%
Journal of Chemical Information and Modeling
2 publications, 3.85%
ACS applied materials & interfaces
2 publications, 3.85%
ACS Omega
2 publications, 3.85%
Macromolecules
2 publications, 3.85%
Chemical Engineering Journal
1 publication, 1.92%
Digital Discovery
1 publication, 1.92%
Polymer Science - Series A
1 publication, 1.92%
Journal of Physical Chemistry A
1 publication, 1.92%
Journal of Membrane Science
1 publication, 1.92%
Nanomanufacturing
1 publication, 1.92%
Physical Chemistry Chemical Physics
1 publication, 1.92%
Russian Journal of Organic Chemistry
1 publication, 1.92%
ACS Applied Polymer Materials
1 publication, 1.92%
npj Computational Materials
1 publication, 1.92%
Journal of Materials Informatics
1 publication, 1.92%
Journal of Physical Chemistry B
1 publication, 1.92%
Bioconjugate Chemistry
1 publication, 1.92%
Langmuir
1 publication, 1.92%
Высокомолекулярные соединения А
1 publication, 1.92%
Журнал органической химии
1 publication, 1.92%
Applied Sciences (Switzerland)
1 publication, 1.92%
Chinese Journal of Polymer Science (English Edition)
1 publication, 1.92%
High Performance Polymers
1 publication, 1.92%
Advanced Materials
1 publication, 1.92%
Polymer Science - Series C
1 publication, 1.92%
Colloid and Polymer Science
1 publication, 1.92%
Polymer
1 publication, 1.92%
1
2
3

Publishers

2
4
6
8
10
12
14
American Chemical Society (ACS)
14 publications, 26.92%
Elsevier
7 publications, 13.46%
Wiley
6 publications, 11.54%
MDPI
5 publications, 9.62%
Springer Nature
5 publications, 9.62%
Pleiades Publishing
4 publications, 7.69%
Institute of Electrical and Electronics Engineers (IEEE)
3 publications, 5.77%
Royal Society of Chemistry (RSC)
2 publications, 3.85%
OAE Publishing Inc.
1 publication, 1.92%
The Russian Academy of Sciences
1 publication, 1.92%
Akademizdatcenter Nauka
1 publication, 1.92%
SAGE
1 publication, 1.92%
Taylor & Francis
1 publication, 1.92%
2
4
6
8
10
12
14
  • We do not take into account publications without a DOI.
  • Statistics recalculated weekly.

Are you a researcher?

Create a profile to get free access to personal recommendations for colleagues and new articles.
Metrics
52
Share
Cite this
GOST |
Cite this
GOST Copy
Volgin I. V. et al. Machine Learning with Enormous “Synthetic” Data Sets: Predicting Glass Transition Temperature of Polyimides Using Graph Convolutional Neural Networks // ACS Omega. 2022. Vol. 7. No. 48. pp. 43678-43691.
GOST all authors (up to 50) Copy
Volgin I. V., Batyr P. A., Matseevich A. V., Dobrovskiy A. Yu., Andreeva M. V., Nazarychev V. M., Larin S. V., Goikhman M. Ya., Vizilter Y. V., Askadskii A. A., Lyulin S. V. Machine Learning with Enormous “Synthetic” Data Sets: Predicting Glass Transition Temperature of Polyimides Using Graph Convolutional Neural Networks // ACS Omega. 2022. Vol. 7. No. 48. pp. 43678-43691.
RIS |
Cite this
RIS Copy
TY - JOUR
DO - 10.1021/acsomega.2c04649
UR - https://pubs.acs.org/doi/10.1021/acsomega.2c04649
TI - Machine Learning with Enormous “Synthetic” Data Sets: Predicting Glass Transition Temperature of Polyimides Using Graph Convolutional Neural Networks
T2 - ACS Omega
AU - Volgin, Igor V
AU - Batyr, Pavel A
AU - Matseevich, Andrey V
AU - Dobrovskiy, Alexey Yu
AU - Andreeva, Maria V.
AU - Nazarychev, V M
AU - Larin, S V
AU - Goikhman, Mikhail Ya
AU - Vizilter, Yury V.
AU - Askadskii, Andrey A
AU - Lyulin, Sergey V.
PY - 2022
DA - 2022/11/17
PB - American Chemical Society (ACS)
SP - 43678-43691
IS - 48
VL - 7
PMID - 36506114
SN - 2470-1343
ER -
BibTex |
Cite this
BibTex (up to 50 authors) Copy
@article{2022_Volgin,
author = {Igor V Volgin and Pavel A Batyr and Andrey V Matseevich and Alexey Yu Dobrovskiy and Maria V. Andreeva and V M Nazarychev and S V Larin and Mikhail Ya Goikhman and Yury V. Vizilter and Andrey A Askadskii and Sergey V. Lyulin},
title = {Machine Learning with Enormous “Synthetic” Data Sets: Predicting Glass Transition Temperature of Polyimides Using Graph Convolutional Neural Networks},
journal = {ACS Omega},
year = {2022},
volume = {7},
publisher = {American Chemical Society (ACS)},
month = {nov},
url = {https://pubs.acs.org/doi/10.1021/acsomega.2c04649},
number = {48},
pages = {43678--43691},
doi = {10.1021/acsomega.2c04649}
}
MLA
Cite this
MLA Copy
Volgin, Igor V., et al. “Machine Learning with Enormous “Synthetic” Data Sets: Predicting Glass Transition Temperature of Polyimides Using Graph Convolutional Neural Networks.” ACS Omega, vol. 7, no. 48, Nov. 2022, pp. 43678-43691. https://pubs.acs.org/doi/10.1021/acsomega.2c04649.