Issue 5, 2023

Active learning for efficient navigation of multi-component gas adsorption landscapes in a MOF

Abstract

In recent decades, metal–organic frameworks (MOFs) have gained recognition for their potential in multicomponent gas separations. Though molecular simulations have revealed structure–property relationships of MOF–adsorbate systems, they can be computationally expensive and there is a need for surrogate models that can predict the adsorption data faster. In this work, an active learning (AL) protocol is introduced that can predict multicomponent gas adsorption in a MOF for a range of thermodynamic conditions. This methodology is applied to build a model for the adsorption of three different gas mixtures (CO2–CH4, Xe–Kr, and H2S–CO2) in the MOF Cu-BTC. A Gaussian process regression (GPR) model is used to fit the data as well to leverage its predicted uncertainty to drive the learning. The training data is generated using grand-canonical Monte Carlo (GCMC) simulations as points are iteratively added to the model to minimize the predicted uncertainty. Also, a criteria which captures the perceived performance of the GPs is introduced to terminate the AL process when the perceived accuracy threshold is met. The three systems are tested for a pressure–mole fraction (PX), and a pressure–mole fraction–temperature (PXT) feature space. It is demonstrated that AL one only needs a fraction of the data from simulations to build a reliable surrogate model for predicting mixture adsorption. Further, the final GP fit from AL outperforms ideal adsorbed solution theory predictions.

Graphical abstract: Active learning for efficient navigation of multi-component gas adsorption landscapes in a MOF

Supplementary files

Article information

Article type
Paper
Submitted
06 Jun 2023
Accepted
23 Aug 2023
First published
24 Aug 2023
This article is Open Access
Creative Commons BY license

Digital Discovery, 2023,2, 1506-1521

Active learning for efficient navigation of multi-component gas adsorption landscapes in a MOF

K. Mukherjee, E. Osaro and Y. J. Colón, Digital Discovery, 2023, 2, 1506 DOI: 10.1039/D3DD00106G

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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