Machine Learning Prediction of Critical Temperature of Organic Refrigerants by Molecular Topology

Yi Que; Song Ren; Zhiming Hu; Jiahui Ren

LAPSE:2023.2805

Published Article

LAPSE:2023.2805

Machine Learning Prediction of Critical Temperature of Organic Refrigerants by Molecular Topology

Yi Que, Song Ren, Zhiming Hu, Jiahui Ren

February 21, 2023

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.

Record ID

LAPSE:2023.2805

Keywords

critical temperature, Machine Learning, molecular structure, refrigerants

Subject

Modelling and Simulations

Suggested Citation

Que Y, Ren S, Hu Z, Ren J. Machine Learning Prediction of Critical Temperature of Organic Refrigerants by Molecular Topology. (2023). LAPSE:2023.2805

Author Affiliations

Que Y: China Petroleum Engineering and Construction Corporation Southwest Company, Chengdu 610041, China; Key Laboratory of Low-Grade Energy Utilization Technologies and Systems, Ministry of Education, School of Energy and Power Engineering, Chongqing University
Ren S: China Petroleum Engineering and Construction Corporation Southwest Company, Chengdu 610041, China; Key Laboratory of Low-Grade Energy Utilization Technologies and Systems, Ministry of Education, School of Energy and Power Engineering, Chongqing University
Hu Z: China Petroleum Engineering and Construction Corporation Southwest Company, Chengdu 610041, China
Ren J: Key Laboratory of Low-Grade Energy Utilization Technologies and Systems, Ministry of Education, School of Energy and Power Engineering, Chongqing University, Chongqing 400030, China

Journal Name

Processes

Volume

10

Issue

3

First Page

577

Year

2022

Publication Date

2022-03-16