Predicting the ET(30) parameter of organic solvents via machine learning

被引：3
|
作者：
Saini, Vaneet [1 ,2 ]
Singh, Harsh [3 ]
机构：
[1] Panjab Univ, Dept Chem, Chandigarh 160014, India
[2] Panjab Univ, Ctr Adv Studies Chem, Chandigarh 160014, India
[3] Univ Engn & Management, Dept Comp Sci & Technol, Kolkata 700160, W Bengal, India
来源：
CHEMICAL PHYSICS LETTERS | 2023年 / 826卷
关键词：
Artificial intelligence; Machine learning; PaDEL; Organic solvents; Polarity; PHENOLATE BETAINE DYES; EMPIRICAL INDICATORS; POLARITY; CHEMISTRY; MODELS;
D O I：
10.1016/j.cplett.2023.140672
中图分类号：
O64 [物理化学（理论化学）、化学物理学];
学科分类号：
070304 ; 081704 ;
摘要：
Polarity of organic solvents is an important parameter which needs to be considered during a reaction design as it can drastically impact the rate and dynamics of a chemical reaction. Till now ET(30) scale is the only comprehensive scale which can accurately quantify various solute-solvent and solvent-solvent interactions, the experimental determination of which is an expensive and resource-intensive approach. Therefore, we have resorted to machine learning techniques for predicting the empirical polarity of organic solvents which would provide ET(30) values for new solvents in a fast and efficient manner without having to rely on experimental and computational setup.
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页数：8
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