In this investigation, the Q-learning algorithm was capped at a maximum of 10 episodes. We evaluated two reward metrics: the difference in MRE between consecutive states, and the difference in R 2 values between consecutive states. Interestingly, the MRE reward metric demonstrated superior performance compared to the R 2 reward metric. Consequently, we primarily focus on the MRE metric in this paper, while presenting results for the R 2 metric in the supporting information (SI). The determination of the optimal hyperparameter combination relied on the achieved final MRE for the MRE reward metric and the final R 2 values for the R 2 reward metric. In this section, rewards are based on MRE as calculated between predicted adsorption and actual adsorption data, rather than the R 2 score. The reward (𝑟) is calculated as the difference in MRE between states t and t+1. Once the reward has been calculated, it is fed into Eq. 1 to construct the Q-table.