In this study, we employed two types of machine learning in the molecular design and experimental optimization of photomechanical crystals, successfully maximizing the blocking force (Figure ). First, the least absolute shrinkage and selection operator (LASSO) regression was used for molecular design of photomechanical crystals with different Young's moduli. Crystals of several compounds were prepared based on the molecular design, creating a material pool with varying Young's moduli and crystal sizes. Then, we used Bayesian optimization for efficient sampling from the material pool to maximize the output force. This finding will not only broaden the applicability of photomechanical molecular crystal, but also be helpful to optimize other properties of functional crystals.