Beyond the BET Analysis: The Surface Area Prediction of Nanoporous Materials Using a Machine Learning Method
Journal
Journal of Physical Chemistry Letters
Journal Volume
11
Journal Issue
14
Pages
5412-5417
Date Issued
2020
Author(s)
Abstract
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. ? 2020 American Chemical Society.
Subjects
Forecasting
Metal-Organic Frameworks
Monolayers
Organometallics
Porous materials
Predictive analytics
BET analysis
Brunauer-Emmett-Teller method
Data-driven approach
Large surface area
Machine learning methods
Metalorganic frameworks (MOFs)
Nano-porous materials
Structural characterization
Machine learning
Type
journal article