Machine learning assisted prediction of band gaps and designing of new polymers for photodetectors: A complete pipeline

Abstract

The band gap is a crucial parameter of photovoltaic materials, which primarily affects their applicability and performance. Therefore, predicting band gaps of targeted materials accurately and rapidly is of utmost importance for designing photodetectors. In comparison to computationally costly first-principles calculations, machine-learned models can enable accurate, rapid, and low-cost predictions of the bandgaps. In this study, we employ machine-learning tools for the accurate and fast prediction of band gaps of polymers for photodetectors. Importantly, about fifteen (15) regression models are developed and tested to screen the models with highest predictive capability. The best models for targeted predictions among others are the light gradient boosting and hist gradient boosting models. Moreover, similarity analysis is applied to screen/search potential materials for photodetectors with high performance using reference building blocks. Gasteiger atomic charges of reference building blocks are also computed. Harvard organic photovoltaic database is explored to screen new building blocks/ monomers using their Tanimoto index. Both Breaking Retro Synthetically Interesting Chemical Substructures (BRICS) method and human controlled monomer designing methods are used to design monomers using previously searched buildings blocks automatically and manually, respectively. The band gaps of selected organic semiconductors are predicted. In addition, clustering of compounds and heatmaps of selected compounds is constructed. The synthetic accessibility score shows that BRICS-based automatic monomer designing shows negative score (i.e., difficult to synthesize), while human controlled/manual monomer designing shows positive score (i.e., easy to synthesize). Our methodology has immense potential in screening polymers with targeted bandgaps prior to experimental synthesis that could accelerate the designing of new polymers for photodetector and other photovoltaic applications.