Materials exhibiting
higher mobilities than conventional organic
semiconducting materials such as fullerenes and fused thiophenes are
in high demand for applications such as printed electronics, organic
solar cells, and image sensors. In order to discover new molecules
that might show improved charge mobility, combined density functional
theory (DFT) and molecular dynamics (MD) calculations were performed,
guided by predictions from machine learning (ML). A ML model was constructed
based on 32 values of theoretically calculated hole mobilities for
thiophene derivatives, benzodifuran derivatives, a carbazole derivative
and a perylene diimide derivative with the maximum value of 10–1.96 cm2/(V s). Sequential learning, also
known as active learning, was applied to select compounds on which
to perform DFT/MD calculation of hole mobility to simultaneously improve
the mobility surrogate model and identify high mobility compounds.
By performing 60 cycles of sequential learning with 165 DFT/MD calculations,
a molecule having a fused thioacene structure with its calculated
hole mobility of 10–1.86 cm2/(V s) was
identified. This values is higher than the maximum value of mobility
in the initial training data set, showing that an extrapolative discovery
could be made with the sequential learning.