Electronic properties of oligothiophenes

Despite the remarkable progress of machine learning (ML) techniques in
chemistry, modeling the optoelectronic properties of long conjugated
oligomers and polymers with ML remains challenging due to the difficulty
in obtaining sufficient training data. Here we use transfer learning to
address the data scarcity issue by pre-training graph neural networks
using data from short oligomers. With only a few hundred training data, we
are able to achieve an average error of about 0.1 eV for excited state
energy of oligothiophenes against TDDFT calculations. We show that the
success of our transfer learning approach relies on the relative locality
of low-lying electronic excitations in long conjugated oligomers. Finally,
we demonstrate the transferability of our approach by modeling the
lowest-lying excited-state energies of poly(3-hexylthiopnene) (P3HT) in
its single-crystal and solution phases using the transfer learning models
trained with data of gas-phase oligothiophenes. The transfer learning
predicted excited-state energy distributions agree quantitatively with
TDDFT calculations and capture some important qualitative features
observed in experimental absorption spectra.