Beyond the BET Analysis: The Surface Area Prediction of Nanoporous Materials Using a Machine Learning Method

Surface areas of porous materials such as metal–organic frameworks (MOFs) are commonly characterized using the Brunauer–Emmett–Teller (BET) method. However, it has been shown that the BET method does not always provide an accurate surface area estimation, especially for large-surface area MOFs. In this work, we propose, for the first time, a data-driven approach to accurately predict the surface area of MOFs. Machine learning is employed to train models based on adsorption isotherm features of more than 300 diverse structures to predict a benchmark measure of the surface area known as the true monolayer area. We demonstrate that the ML-based methods can predict true monolayer areas significantly better than the BET method, showing great promise for their potential as a more accurate alternative to the BET method in the structural characterization of porous materials.