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We will give a talk on the recent achievements and future plans in the computational molecular science research team. In particular, we will introduce materials design of hole-transporting materials (HTMs) for perovskite solar cells. In this study, the efficient search of optimum HTMs was achieved by applying machine learning techniques. We employed the deep neural network to predict the power conversion efficiency of perovskite solar cells with HTMs by utilizing molecular descriptors as input features. We also employed the Gaussian process regression to evaluate the acquisition function in Bayesian optimization and implement uncertainty and reliability to the prediction model. Discrete particle swarm optimization was applied to tackle the optimization problem in the vast chemical space. In addition, we will introduce the future development of the solar-cell simulator based on the dynamic Monte Carlo approach with the first-principles calculation.