Machine learning-driven optimization of the output force in photo-actuated organic crystals

Abstract

Photo-actuated organic crystals that can be remotely controlled by light are gaining attention as next-generation actuator materials. In the practical application of actuator materials, the mode of deformation and the output force are important properties. Since the output force depends on the crystal properties and experimental conditions, it is necessary to explore the optimal conditions from a vast parameter space. In this study, we employed two types of machine learning for molecular design and experimental optimization to maximize the blocking force. Machine learning in molecular design led to the creation of a material pool of salicylideneamine derivatives. Bayesian optimization was used for efficient sampling from the material pool for force measurements in the real world, achieving a maximum blocking force of 37.0 mN. This method was at least 73 times more efficient than the grid search approach.