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

The Journal of Physical Chemistry Letters
Archit DatarLi-Chiang Lin

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.

References

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Citations

Mar 2, 2021·The Journal of Physical Chemistry Letters·Eun Hyun Cho, Li-Chiang Lin
Apr 30, 2021·Journal of Chemical Information and Modeling·Cigdem AltintasRamazan Yildirim
Jul 16, 2021·Journal of Chemical Information and Modeling·Zachary J BaumQiongqiong Zhou
Nov 6, 2020·The Journal of Physical Chemistry Letters·Oleg V Prezhdo
Nov 19, 2021·Journal of Chemical Information and Modeling·Prosun HalderJayant K Singh
Dec 17, 2021·Journal of the American Chemical Society·Rishabh JainJeffrey D Rimer

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