In the supporting information (SI), we show the results of using an R 2 reward metric in the sequence used in this paper. First, we applied the RL framework to CH4 both in Cu-BTC and IRMOF-1 and carried out sensitivity analysis to determine the optimal Q-learning parameters to be used in the case of the CoRE MOFs. For the R 2 reward metric, we set a criterion for the RL agent execution where the algorithm ceased its iterations once the final R 2 between the GP-predicted values and the ground truth adsorption reached or surpassed 0.99.