Machine Learning-Driven Optimization of Output Force in Photo-Actuated Organic Crystals

Photo-actuated organic crystals, which can be remotely controlled by light, are gaining attention as next-generation actuator materials. In the practical application of actuator materials, not only the mode of deformation but also the output force is an important property. Since the output force depends on both 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, successfully maximizing 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, achieving a maximum blocking force of 37.0 mN. It was estimated that this method was at least 73 times more efficient than the grid search approach.