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

作　　者：Jing Lin Tao Ban Tian Li Ye Sun Shenglan Zhou Rushuo Li Yanjing Su Jitti Kasemchainan Hongyi Gao Lei Shi Ge Wang 

机构地区：[1]Beijing Key Laboratory of Function Materials for Molecule&Structure Construction,Beijing Advanced Innovation Center for Materials Genome Engineering,School of Materials Science and Engineering,University of Science and Technology Beijing,Beijing,China [2]Beijing Advanced Innovation Center for Big Data and Brain Computing,School of Computer Science and Engineering,Beihang University,Beijing,China [3]Beijing Advanced Innovation Center for Materials Genome Engineering,Institute for Advanced Materials and Technology,School of Materials Science and Engineering,University of Science and Technology Beijing,Beijing,China [4]Department of Chemical Technology,Chulalongkorn University,Bangkok,Thailand

出　　处：《Materials Genome Engineering Advances》2024年第3期96-106,共11页材料基因工程前沿（英文）

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).

摘　　要：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.

关 键 词：defect content machine learning metal-organic frameworks multi-objective optimization thermal stability 

分 类 号：TG14[一般工业技术—材料科学与工程]