The predictor provides the estimated target properties, allowing for the calculation of the rewards. As illustrated in Fig. , the architecture based on the Transformer encoder takes different embedding layers of topologies, metal clusters, and vocabulary of SELFIES. By adding a simple dense layer to the class token at the first position, which is a learnable embedding layer, the predictor can predict the desired target properties. The performance of the predictor was evaluated by measuring the mean absolute errors (MAE) of CO 2 heat of adsorption and CO 2 /H 2 O selectivity, which were found to be 2.87 kJ/mol and 0.64, respectively (see Supplementary Fig. ). It should be emphasized that the reward functions of the reinforcement learning framework were assigned based on the predicted targets of the predictor.