Designing of low band gap organic semiconductors through data mining from multiple databases and machine learning assisted property prediction.

Bandgap is a key parameter for selecting suitable materials for a broad range of applications. Organic solar cells (OSCs) are emerging as powerful devices due to their low-cost solution processing. Developing OSCs necessitates producing effective materials in a computationally cost-effective and rapid manner. Machine learning has become popular and well-recognized among researchers to screen and design high performance materials for OSCs. Machine learning models require data from the literature (reported studies or databases) to effectively predict targeted properties. To unveil the hidden dataset patterns, a thorough data visualization analysis is conducted. Importantly, multiple database mining is performed for designing low band gap organic semiconductors. Molecular descriptors are utilized to train machine learning models. Importantly, about 22 different machine learning models are tested. Among all models, extra trees regressor shows higher predictive capability. Residuals, learning curve and validation curve are also drawn for extra trees regressor. Feature importance analysis determines the significance of the features. Moreover, library enumeration and similarity analysis further facilitate designing of high-performance semiconductor materials. This work may help in screening and designing efficient semiconductors having low band gap for increasing the efficiency of OSCs. [Display omitted] • Machine learning is applied to design new low band gap organic semi-conductors for OSCs. • Data mining and property prediction strategies are applied to screen potential candidates for OSCs. • The chemical similarity analysis and library enumeration techniques are performed. • More than >20 different regression models are developed and used for better prediction. [ABSTRACT FROM AUTHOR]