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

Abstract

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.