Unifying Mixed Gas Adsorption in Molecular Sieve Membranes and MOFs using Machine Learning
This work addresses the problem of predicting complex adsorption processes for gas mixtures in materials like molecular sieves and MOFs, which is incremental as it unifies existing approaches but introduces a new method for a known bottleneck.
The authors tackled the challenge of predicting gas adsorption isotherms for both pure and binary mixtures across diverse materials like molecular sieves and MOFs, using a unified machine learning model based on physical properties, achieving accurate predictions with good accuracy for CO2 uptake in CALF-20.
Recent machine learning models to accurately obtain gas adsorption isotherms focus on polymers or metal-organic frameworks (MOFs) separately. The difficulty in creating a unified model that can predict the adsorption trends in both types of adsorbents is challenging, owing to the diversity in their chemical structures. Moreover, models trained only on single gas adsorption data are incapable of predicting adsorption isotherms for binary gas mixtures. In this work, we address these problems using feature vectors comprising only the physical properties of the gas mixtures and adsorbents. Our model is trained on adsorption isotherms of both single and binary mixed gases inside carbon molecular sieving membrane (CMSM), together with data available from CoRE MOF database. The trained models are capable of accurately predicting the adsorption trends in both classes of materials, for both pure and binary components. ML architecture designed for one class of material, is not suitable for predicting the other class, even after proper training, signifying that the model must be trained jointly for proper predictions and transferability. The model is used to predict with good accuracy the CO2 uptake inside CALF-20 framework. This work opens up a new avenue for predicting complex adsorption processes for gas mixtures in a wide range of materials.