Machine Learning-Inspired Molecular Design, Divergent Syntheses, and X-Ray Analyses of Dithienobenzothiazole-Based Semiconductors Controlled by S⋅⋅⋅N and S⋅⋅⋅S Interactions.

Abstract

Inspired by the previous machine-learning study that the number of hydrogen-bonding acceptor (NHBA) is important index for the hole mobility of organic semiconductors, seven dithienobenzothiazole (DBT) derivatives 1 a-g (NHBA=5) were designed and synthesized by one-step functionalization from a common precursor. X-ray single-crystal structural analyses confirmed that the molecular arrangements of 1b (the diethyl and ethylthienyl derivative) and 1c (the di(n-propyl) and n-propylthienyl derivative) in the crystal are classified into brickwork structures with multidirectional intermolecular charge-transfer integrals, as a result of incorporation of multiple hydrogen-bond acceptors. The solution-processed top-gate bottom-contact devices of 1b and 1c had hole mobilities of 0.16 and 0.029 cm2 V-1s-1, respectively.