Article
Reichardt’s Dye-Based Solvent Polarity and Abraham Solvent Parameters: Examining Correlations and Predictive Modeling
Liquids
(2023)
Abstract
The concept of “solvent polarity” is widely used to explain the effects of using different solvents in various scientific applications. However, a consensus regarding its definition and quantitative measure is still lacking, hindering progress in solvent-based research. This study hopes to add to the conversation by presenting the development of two linear regression models for solvent polarity, based on Reichardt’s ET(30) solvent polarity scale, using Abraham solvent parameters and a transformer-based model for predicting solvent polarity directly from molecular structure. The first linear model incorporates the standard Abraham solvent descriptors s, a, b, and the extended model ionic descriptors j+ and j−, achieving impressive test-set statistics of R2 = 0.940 (coefficient of determination), MAE = 0.037 (mean absolute error), and RMSE = 0.050 (Root-Mean-Square Error). The second model, covering a more extensive chemical space but only using the descriptors s, a, and b, achieves test-set statistics of R2 = 0.842, MAE = 0.085, and RMSE = 0.104. The transformer-based model, applicable to any solvent with an associated SMILES string, achieves test-set statistics of R2 = 0.824, MAE = 0.066, and RMSE = 0.095. Our findings highlight the significance of Abraham solvent parameters, especially the dipolarity/polarizability, hydrogen-bond acidity/basicity, and ionic descriptors, in predicting solvent polarity. These models offer valuable insights for researchers interested in Reichardt’s ET(30) solvent polarity parameter and solvent polarity in general.
Keywords
- Reichardt’s dye,
- ET(30) solvent polarity parameter,
- hydrogen bonding,
- solvatochromism,
- Abraham model,
- predictive modeling
Disciplines
Publication Date
August 2, 2023
DOI
10.3390/liquids3030020
Citation Information
William E. Acree and Andrew SID Lang. "Reichardt’s Dye-Based Solvent Polarity and Abraham Solvent Parameters: Examining Correlations and Predictive Modeling" Liquids Vol. 3 Iss. 3 (2023) p. 303 - 313 ISSN: 2673-8015 Available at: http://works.bepress.com/andrew-sid-lang/45/
Creative Commons license
This work is licensed under a Creative Commons CC_BY International License.