Machine learning based hybrid ensemble models for prediction of organic dyes photophysical properties: Absorption wavelengths, emission wavelengths, and quantum yields

Abstract

Fluorescent organic dyes are extensively used in the design and discovery of new materials, photovoltaic cells, light sensors, imaging applications, medicinal chemistry, drug design, energy harvesting technologies, dye and pigment industries, and pharmaceutical industries, among other things. However, designing and synthesizing new fluorescent organic dyes with desirable properties for specific applications requires knowledge of the chemical and physical properties of previously studied molecules. It is a difficult task for experimentalists to identify the photophysical properties of the required chemical molecule at negligible time and financial cost. For this purpose, machine learning-based models are a highly demanding technique for estimating photophysical properties and may be an alternative approach to density functional theory. In this study, we used 15 single models and proposed three different hybrid models to assess a dataset of 3066 organic materials for predicting photophysical properties. The performance of these models was evaluated using three evaluation parameters: mean absolute error, root mean squared error, and the coefficient of determination (R2) on the test-size data. All the proposed hybrid models achieved the highest accuracy (R2) of 97.28%, 95.19%, and 74.01% for predicting the absorption wavelengths, emission wavelengths, and quantum yields, respectively. These resultant outcomes of the proposed hybrid models are ∼1.9%, ∼2.7%, and ∼2.4% higher than the recently reported best models’ values in the same dataset for absorption wavelengths, emission wavelengths, and quantum yields, respectively. This research promotes the quick and accurate production of new fluorescent organic dyes with desirable photophysical properties for specific applications.

Coupling the PROSAIL Model and Machine Learning Approach for Canopy Parameter Estimation of Moso Bamboo Forests from UAV Hyperspectral Data

“…MLRA, such as Artificial Neural Networks and Support Vector Machine Regression, have been widely used in parameter estimation, but for high-dimensional data, the training speed is slow and prone to overfitting problems. While Multilayer Perceptron Regression (MLPR), Extreme Random Tree Regression (ETR), and XGBoost Regression (XGBR) can better deal with the nonlinear problem, XGBR can automatically deal with the missing values and provide the model with higher prediction accuracy [39,40].…”