Robust Machine Learning Models for Predicting High CO2 Working Capacity and CO2/H2 Selectivity of Gas Adsorption in Metal Organic Frameworks for Precombustion Carbon Capture

This work is devoted to the development of quantitative structure–property relationship (QSPR) models using machine learning to predict CO2 working capacity and CO2/H2 selectivity for precombustion carbon capture using a topologically diverse database of hypothetical metal–organic framework (MOF) structures (358 400 MOFs, 1166 network topologies). Such a diversity of the networks topology is much higher than previously used (