Unifying Mixed Gas Adsorption in Molecular Sieve Membranes and MOFs using Machine Learning

TL;DR

This paper develops a machine learning model that accurately predicts gas adsorption isotherms for both polymers and MOFs, including binary mixtures, by using physical properties as features, unifying previous separate models.

Contribution

The study introduces a joint ML model trained on diverse materials and gas mixtures, enabling accurate predictions across different adsorbent classes and mixture types.

Findings

Model accurately predicts adsorption trends for pure and binary gases.

Training on combined data improves transferability across materials.

Successful prediction of CO2 uptake in CALF-20 framework.

Abstract

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.