An auto‐configurable machine learning framework to optimize and predict catalysts for CO2 to light olefins process
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DOI: 10.1002/aic.18437
Abstract: This study proposed an auto‐configurable machine learning framework based on the differential evolution algorithm (AutoML‐DE) driven by hybrid data for the screening and discovery of promising CO2 to light olefins (CO2TLO) catalysts candidates. The hybrid… read more here.
Keywords: light olefins; machine learning; configurable machine; learning framework ... See more keywords