Machine-learning-assisted intelligent synthesis of UiO-66(Ce): Balancing the trade-off between structural defects and thermal stability for efficient hydrogenation of Dicyclopentadiene

Abstract

Metal-organic frameworks (MOFs), renowned for structural diversity and design flexibility, exhibit potential in catalysis. However, the pursuit of higher catalytic activity through defects often compromises stability, requiring a delicate balance. Traditional trial-and-error method for optimizing synthesis parameters within the complex chemical space is inefficient. Herein, taking the typical MOF UiO-66(Ce) as an illustrative example, a closed loop workflow is built, which integrates machine learning (ML)-assissted prediction, multi-objective optimization (MOO) and experimental preparation to synergistically optimize the defect content and thermal stability of UiO-66(Ce) for efficient hydrogenation of dicyclopentadiene (DCPD). An automatic data extraction program ensures data accuracy, establishing a high-quality database. ML is employed to explore the intricate synthesis-structure-property correlations, enabling precise delineation of pure-phase subspace and accurate predictions of properties. After two iterations, MOO model identifies optimal protocols for high defect content (>40%) and thermal stability (>300°C). The optimized UiO-66(Ce) exhibits superior catalytic performance in hydrogenation of DCPD, validating the precision and reliability of our methodology. This ML-assisted approach offers a valuable paradigm for solving the trade-off riddle in materials field.