Machine learning assisted prediction of absorption maxima in cyclohexene: A comparison using molecular descriptors and fingerprints

Abstract

Light absorption plays important role in different photovoltaics devices. Easy and fast prediction of absorption properties is essential for fast screening of efficient materials. In our pursuit of identifying the optimal model for predicting absorption maxima, we systematically evaluated over 40 machine learning models, employing both molecular descriptors and fingerprints as input features. Notably, models trained on molecular descriptors demonstrated superior predictive capabilities as compared to those relying on molecular fingerprints. This not only showcased the efficacy of molecular descriptors but also highlighted the potential of these models as rapid and efficient alternatives to the density functional theory (DFT) based approaches. The use of machine learning models based on molecular descriptors introduced a level of simplicity and speed in predictions, surpassing the computational demands associated with traditional DFT-based methods. Our introduced framework that is based on machine learning, offers a valuable tool for the easy and fast prediction of properties.