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

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.

Keywords

• defect content
• machine learning
• metal‐organic frameworks
• multi‐objective optimization
• thermal stability