A machine learning framework for accelerating the development of highly efficient methanol synthesis catalysts

Abstract

Converting CO₂ with green hydrogen to methanol as a carbon-neutral liquid fuel is a promising route for the long-term storage and distribution of intermittent renewable energy. Nevertheless, attaining highly efficient methanol synthesis catalysts from the vast composition space remains a significant challenge. Here we present a machine learning framework for accelerating the development of high space-time yield (STY) methanol synthesis catalysts. A database of methanol synthesis catalysts has been compiled, consisting of catalyst composition, preparation parameters, structural characteristics, reaction conditions and their corresponding catalytic performance. A methodology for constructing catalyst features based on the intrinsic physicochemical properties of the catalyst components has been developed, which significantly reduced the data dimensionality and enhanced the efficiency of machine learning operations. Two high-precision machine learning prediction models for the activities and product selectivity of catalysts were trained and obtained. Using this machine learning framework, an efficient search was achieved within the catalyst composition space, leading to the successful identification of high STY multi-element oxide methanol synthesis catalysts. Notably, the CuZnAlTi catalyst achieved high STYs of 0.49 and 0.65 g_MeOH/(g_catalyst h) for CO₂ and CO hydrogenation to methanol at 250 °C, respectively, and the STY was further increased to 2.63 g_MeOH/(g_catalyst h) in CO and CO₂ co-hydrogenation.