Optimized Machine learning techniques Enable prediction of organic dyes photophysical Properties: Absorption Wavelengths, emission Wavelengths, and quantum yields

Kapil Dev Mahato; Uday Kumar

doi:10.1016/j.saa.2023.123768

Optimized Machine learning techniques Enable prediction of organic dyes photophysical Properties: Absorption Wavelengths, emission Wavelengths, and quantum yields

Spectrochim Acta A Mol Biomol Spectrosc. 2024 Mar 5:308:123768. doi: 10.1016/j.saa.2023.123768. Epub 2023 Dec 15.

Authors

Kapil Dev Mahato¹, Uday Kumar²

Affiliations

¹ Department of Physics, National Institute of Technology Jamshedpur, Jharkhand 831014, India. Electronic address: 2018rsphy005@nitjsr.ac.in.
² Department of Physics, National Institute of Technology Jamshedpur, Jharkhand 831014, India.

PMID: 38134661
DOI: 10.1016/j.saa.2023.123768

Abstract

Applications of organic dyes, ranging from basic research to industry, are functions of their photophysical properties. Two important aspects- (1) knowledge of the photophysical properties of existing dyes long before real applications and (2) discovery of new organic dyes with desired photophysical properties for either upgradation of existing or development of new applications-are needed to be addressed. These two cases are coupled together with the common goal of estimating photophysical properties with high accuracy at the minimum cost of time and money long before the hard-core laboratory experiment. For this purpose, machine learning-based techniques are the most suitable approach. In this study, we used optimized machine-learning techniques to assess a dataset of 3066 organic dyes, which were evaluated using three evaluation parameters: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The Quadratic Support Vector Machine (QSVM) was the best predictive model for RMSE-16.614, MAE-10.837, and R²-0.961 for absorption wavelengths and RMSE-23.636, MAE-16.278, and R²-0.929 for emission wavelengths. These R² values are 0.7% and 0.4% greater than the Gradient Boost Regression Tree (GBRT) model's recently reported values of 0.954 and 0.925 for absorption and emission wavelengths, respectively. Furthermore, we estimated the quantum yield and found that the Coarse Gaussian Support Vector Machine (CGSVM) outperformed all examined models. For more validation of these models, we compared the predicted results with the experimental results of selective dyes. The proposed automated approach can be used for predicting photophysical properties without much computer programming knowledge.

Keywords: Absorption; Emission; MATLAB regression learner; Machine learning; Organic dyes; Photophysical properties; and Quantum Yields.