Design of Molecules with Low Hole and Electron Reorganization Energy Using DFT Calculations and Bayesian Optimization

Materials
exhibiting higher mobility than conventional organic
semiconducting materials, such as fullerenes and fused thiophenes,
are in high demand for applications in printed electronics. To discover
new molecules that might show improved charge mobility, the adaptive
design of experiments (DoE) to design molecules with low reorganization
energy was performed by combining density functional theory (DFT)
methods and machine learning techniques. DFT-calculated values of
165 molecules were used as an initial training dataset for a Gaussian
process regression (GPR) model, and five rounds of molecular designs
applying the GPR model and validation via DFT calculations were executed.
As a result, new molecules whose reorganization energy is smaller
than the lowest value in the initial training dataset were successfully
discovered.