Exploring Molecular Descriptors and Acquisition Functions in Bayesian Optimization for Designing Molecules with Low Hole Reorganization Energy.

Organic semiconductors have been widely studied owing to their potential applications in various devices, such as field-effect transistors, light-emitting diodes, solar cells, and image sensors. However, they have a limitation of significantly lower carrier mobility compared to silicon, which is a widely used inorganic semiconductor. Therefore, to address such limitations, these molecules should be further explored. Hole reorganization energy has been known to influence carrier mobility; that is, lower energy results in higher mobility. This study uses Bayesian optimization (BO) to identify molecules with low hole reorganization energies. While several acquisition functions (AFs), including probability of improvement, expected improvement, and mutual information, have been proposed for use in BO, it is well established that the performance of AFs can vary depending on the data set. We evaluate the performance of AFs applied to a data set of organic semiconductor molecules and propose a novel approach that alternates the use of AFs in the BO process. Our findings conclude that alternating AFs in BO enhance the stability of the search for molecules with low reorganization energy.