Optimizing the prediction of adsorption in MOFs through an RL framework offers a compelling approach. Unlike conventional models, RL provides a dynamic method for selecting the training dataset, which is vital in scenarios where the dataset size is vast. Compared to other models, RL adaptively navigates through extensive datasets, iteratively identifying training points. Recently, RL has been used as a tool for the inverse design of MOFs for direct air capture of carbon dioxide .