Designing of small molecule donors with the help of machine learning for organic solar cells and performance prediction

• Machine learning is used to predict power conversion efficiency of organic solar cells . • Gradient boosting regression has identified as best ML model. • Designed and evaluated 10,000 small molecule donors. • Chemical similarity analysis has revealed reasonable structural resemblance. • Synthetic accessibility has indicated easy synthesis for majority of selected donors. Designing of materials for organic solar cells is a tedious process. In present study, machine learning (ML) is used to predict the power conversion efficiency (PCE). Over 40 ML models are tried. Gradient boosting regression is appeared as best model. 10k small molecule donors are designed. Their PCE values are predicted using best model. The library of generated donors is visualized using various tools. Chemical similarity analysis is done to study structural behavior of selected donors. Reasonable resemblance is found. Synthetic accessibility assessment has indicated easy synthesis for majority of selected small molecule donors. The introduced framework has ability to find the efficient materials in short time.