Prediction by Convolutional Neural Networks of CO2/N2 Selectivity in Porous Carbons from N2 Adsorption Isotherm at 77 K

Porous carbons are an important class of porous materials with many applications, including gas separation. An N2 adsorption isotherm at 77 K is the most widely used approach to characterize porosity. Conventionally, textual properties such as surface area and pore volumes are derived from the N2 adsorption isotherm at 77 K by fitting it to adsorption theory and then correlating it to gas separation performance (uptake and selectivity). Here the N2 isotherm at 77 K was used directly as input (representing feature descriptors for the porosity) to train convolutional neural networks to predict gas separation performance (using CO2 /N2 as a test case) for porous carbons. The porosity space for porous carbons was explored for higher CO2 /N2 selectivity. Porous carbons with a bimodal pore-size distribution of well-separated mesopores (3-7 nm) and micropores (<2 nm) were found to be most promising. This work will be useful in guiding experimental research of porous carbons with the desired porosity for gas separation and other applications.