The overall schematic of the reinforcement learning framework is depicted in Fig. , which comprises two key components: an agent and an environment. The agent, which serves as the generator, takes action by creating a data representation of a new MOF structure. At the same time, the predictor evaluates the action as the environment by predicting the property of the new MOF representation. Based on the prediction, a reward is returned to update the agent for generating MOF structures to maximize the reward. The objective of reinforcement learning is to find the weights of the agent that maximize the expected return obtained from the environment. The details of the framework are provided in the Methods section.