Cho E.H.Deng X.Zou C.Lin L.-C.LI-CHIANG LIN2022-05-242022-05-242020https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097746427&doi=10.1021%2facs.jpcc.0c09073&partnerID=40&md5=f85c98b6a2f3071fd98487a8d24c0049https://scholars.lib.ntu.edu.tw/handle/123456789/611458Nanoporous materials, such as metal-organic frameworks (MOFs), have shown great potential as adsorbents for separations in a wide variety of energy- or environment-related applications. One promising application is sour gas sweetening; a raw natural gas contains small amounts of H2S that can be detrimental to the efficient utilization of the energy source. However, the large database of nanoporous materials has made the discovery of optimum materials significantly demanding. While molecular simulations can play a complementary role in facilitating the materials search, their brute-force utilization still requires a vast amount of computational resources. In this study, we incorporate a machine learning algorithm with structural and chemistry descriptors as inputs for efficient screening. Specifically, the random forest regressor, which can also be useful for elucidating structure-property relationships, is employed. For reliable predictions with machine learning, the choices of features play considerably important roles. In addition to commonly adopted geometrical and chemical features, we propose and incorporate a set of newly designed features for training the model. These new features represent preferential binding sites of open-metal sites and dense framework atoms on the pore surface. Our analysis shows that the inclusion of the newly designed features greatly improves the machine learning performance. Our work can pave the way for the future design of nanoporous materials for sour gas sweetening. These newly designed features can also be used for the development of machine learning models for other applications, especially those involving molecules with strong dipole and/or quadruple moments, such as carbon capture. ? 2020 American Chemical Society.Binding sitesDecision treesHydrogen sulfideLearning algorithmsMetal-Organic FrameworksNanoporesOrganometallicsPlasma interactionsPorous materialsSour gasTuring machinesComputational resourcesComputational studiesMachine learning modelsMetalorganic frameworks (MOFs)Molecular simulationsNano-porous materialsSour gas sweeteningsStructure property relationshipsMachine learningjournal article10.1021/acs.jpcc.0c090732-s2.0-85097746427