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.
Gas Adsorption and Diffusion Behaviors in Interfacial Systems Composed of a Polymer of Intrinsic Microporosity and Amorphous Silica: A Molecular Simulation Study
COFAP: A Universal Framework for COFs Adsorption Prediction through Designed Multi-Modal Extraction and Cross-Modal Synergy
College of Science, College of Information and Electrical Engineering（科学学院，信息与电气工程学院）；China Agricultural University（中国农业大学）；Qingdao Institute of Software, College of Computer Science and Technology（软件研究所，计算机科学与技术学院）；China University of Petroleum (East China)（中国石油大学（华东））；Weifang university（潍坊大学）；College of Environmental Science and Engineering（环境科学与工程学院）；North China Electric Power University（华北电力大学）；College of Science（科学学院）
Influence of Ultramicroporosity and Surface Chemistry on Dynamic CO2 Capture in Activated Carbons
Eindhoven University of Technology（埃因霍温理工大学）；IMDEA Energy Institute（IMDEA能源研究所）；Instituto de Catálisis y Petroleoquímica (ICP), CSIC（西班牙国家研究委员会催化与石油化工研究所）
