Not from Scratch: Predicting Thermophysical Properties through Model-Based Transfer Learning Using Graph Convolutional Networks-Reference-Cited by-同舟云学术

Not from Scratch: Predicting Thermophysical Properties through Model-Based Transfer Learning Using Graph Convolutional Networks

Published:2022-10-31 Issue:22 Volume:62 Page:5411-5424
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Hormazabal Rodrigo S.¹,Kang Jeong Won¹^ORCID,Park Kiho²^ORCID,Yang Dae Ryook¹

Affiliation:

1. Department of Chemical and Biological Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul02841, Republic of Korea

2. School of Chemical Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju61186, Republic of Korea

Funder

Korea University

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.2c00846

Reference48 articles.

1. Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy

2. Prediction of heat capacities and heats of vaporization of organic liquids by group contribution methods

3. ESTIMATION OF PURE-COMPONENT PROPERTIES FROM GROUP-CONTRIBUTIONS

4. Sales-Cruz, M.; Aca-Aca, G.; Sánchez-Daza, O.; López-Arenas, T.Predicting Critical Properties, Density and Viscosity of Fatty Acids, Triacylglycerols and Methyl Esters by Group Contribution Methods. ESCAPE20, 2010.

5. QSPR Correlation of Melting Point for Drug Compounds Based on Different Sources of Molecular Descriptors

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