High-Performance Chemical Information Database towards Accelerating Discovery of Metal-Organic Frameworks for Gas Adsorption with Machine Learning  

作　　者：Zi-kai Hao Hai-feng Lv Da-yong Wang Xiao-jun Wu 

机构地区：[1]Hefei National Laboratory for Physical Science at the Microscale,School of Chemistry and Materials Sciences,CAS Key Laboratory of Materials for Energy Conversion,and CAS Center for Excellence in Nanoscience,University of Science and Technology of China,Hefei 230026,China [2]Synergetic Innovation Center of Quantum Information&Quantum Physics,University of Science and Technology of China,Hefei 230026,China

出　　处：《Chinese Journal of Chemical Physics》2021年第4期436-442,I0003,共8页化学物理学报（英文）

基　　金：This work was supported by the National Natu-ral Science Foundation of China(No.21573204 and No.21421063),Fundamental Research Funds for the Central Universities,National Program for Support of Top-notch Young Professional,CAS Interdisciplinary Innovation Team,and Super Computer Center of USTCSCC and SCCAS.

摘　　要：Chemical structure searching based on databases and machine learning has at-tracted great attention recently for fast screening materials with target func-tionalities.To this end,we estab-lished a high-performance chemical struc-ture database based on MYSQL engines,named MYDB.More than 160000 metal-organic frameworks(MOFs)have been collected and stored by using new retrieval algorithms for effcient searching and recom-mendation.The evaluations results show that MYDB could realize fast and effcient key-word searching against millions of records and provide real-time recommendations for similar structures.Combining machine learning method and materials database,we developed an adsorption model to determine the adsorption capacitor of metal-organic frameworks to-ward argon and hydrogen under certain conditions.We expect that MYDB together with the developed machine learning techniques could support large-scale,low-cost,and highly convenient structural research towards accelerating discovery of materials with target func-tionalities in the eld of computational materials research.

关 键 词：Chemical informatics DATABASE Search engine Machine learning Gas ab-sorption 

分 类 号：TQ424[化学工程]