Optimizing the prediction of adsorption in metal-organic frameworks leveraging Q-learning

The application of machine learning (ML) techniques in materials science has revolutionized the pace and scope of materials research and design. In the case of metal-organic frameworks (MOFs), a promising class of materials due to their tunable properties and versatile applications in gas adsorption and separation, ML has helped survey the vast material space. This study explores the integration of reinforcement learning (RL), specifically Q-learning, within an active learning (AL) context, combined with Gaussian processes (GPs) for predictive modeling of adsorption in MOFs. We demonstrate the effectiveness of the RL-driven framework in guiding the selection of training data points and optimizing predictive model performance for methane and carbon dioxide adsorption, using two different reward metrics. Our results highlight the integration of RL as an AL method for adsorption predictions in MFs, and how it compares to a previously implemented AL scheme.