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- Title
Machine Learning Prediction of Critical Temperature of Organic Refrigerants by Molecular Topology.
- Authors
Que, Yi; Ren, Song; Hu, Zhiming; Ren, Jiahui
- Abstract
In this work, molecular structures, combined with machine learning algorithms, were applied to predict the critical temperatures (Tc) of a group of organic refrigerants. Aiming at solving the problem that previous models cannot distinguish isomers, a topological index was introduced. The results indicate that the novel molecular descriptor 'molecular fingerprint + topological index' can effectively differentiate isomers. The average absolute average deviation between the predicted and experimental values is 3.99%, which proves a reasonable prediction ability of the present method. In addition, the performance of the proposed model was compared with that of other previously reported methods. The results show that the present model is superior to other approaches with respect to accuracy.
- Subjects
CRITICAL temperature; MACHINE learning; MOLECULAR connectivity index; REFRIGERANTS; MOLECULAR structure
- Publication
Processes, 2022, Vol 10, Issue 3, p577
- ISSN
2227-9717
- Publication type
Article
- DOI
10.3390/pr10030577