Towards Organic Photodetectors Functioning Under Strong Sunlight. Machine-learning-assisted Design of Diarylethene n-type Dopants to Mix with p-type Organic Semiconductor P3HT

Abstract

Linear dynamic range of organic photodetectors, which is typically narrow due to low mobility of organic semiconductor, has been extended by diarylethene (DAE) photochromic switches doped to a poly-3-hexylthiophene photoactive layer. A speculated mechanism is that DAE acts as n-type electron traps only in its aromatic closed form, which is predominant only under strong sunlight, addressing the early saturation problem on sunny days. We herein identified two optimal DAE derivatives out of ~100 candidates, using the TDDFT calculations on the HOMO-LUMO energies of their open-closed isomers (~400 data). Since this is only a small subset of ~105 candidates, we predicted the HOMO-LUMO energies of the remaining candidates by machine learning with various artificial neural network models and molecule representation methods. We were able to identify additional optimal candidates, which were screened by machine learning prediction, and were confirmed by TDDFT calculations.