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.