Prediction of $\textrm{CO}_2$ Adsorption in Nano-Pores with Graph Neural Networks

• URL: http://arxiv.org/abs/2209.07567v1
• Date: Mon, 22 Aug 2022 04:22:21 GMT
• Title: Prediction of $\textrm{CO}_2$ Adsorption in Nano-Pores with Graph Neural Networks
• Authors: Guojing Cong, Anshul Gupta, Rodrigo Neumann, Maira de Bayser, Mathias Steiner, Breannd\'an \'O Conch\'uir
• Abstract summary: Our model is based solely on standard structural input files containing atomistic descriptions of the adsorbent material candidates. We construct novel methodological extensions to match the prediction accuracy of classical machine learning models. Our approach can be more broadly applied to optimize gas capture processes at industrial scale.
• Score: 2.6424064030995957
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We investigate the graph-based convolutional neural network approach for predicting and ranking gas adsorption properties of crystalline Metal-Organic Framework (MOF) adsorbents for application in post-combustion capture of $\textrm{CO}_2$. Our model is based solely on standard structural input files containing atomistic descriptions of the adsorbent material candidates. We construct novel methodological extensions to match the prediction accuracy of classical machine learning models that were built with hundreds of features at much higher computational cost. Our approach can be more broadly applied to optimize gas capture processes at industrial scale.

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