Datar A.Chung Y.G.Lin L.-C.LI-CHIANG LIN2022-05-242022-05-242020https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088266400&doi=10.1021%2facs.jpclett.0c01518&partnerID=40&md5=df526192a231a8d36943aeae3b91fd55https://scholars.lib.ntu.edu.tw/handle/123456789/611462Surface 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. ? 2020 American Chemical Society.ForecastingMetal-Organic FrameworksMonolayersOrganometallicsPorous materialsPredictive analyticsBET analysisBrunauer-Emmett-Teller methodData-driven approachLarge surface areaMachine learning methodsMetalorganic frameworks (MOFs)Nano-porous materialsStructural characterizationMachine learningjournal article10.1021/acs.jpclett.0c015182-s2.0-85088266400