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,requi...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 ma-chine 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℃).The optimized UiO-66(Ce)exhibits superior catalytic performance in hydroge-nation 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.展开更多
基金supported by the National Key R&D Program of China(Grant No.2021YFB3500700)Beijing Natural Science Foundation(Grant No.L233011)Guangdong Basic and Applied Basic Research Foundation(Grant No.2022A1515010185).
文摘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 ma-chine 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℃).The optimized UiO-66(Ce)exhibits superior catalytic performance in hydroge-nation 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.
