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 che...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.展开更多
基金This work is supported by Ministry of Science and Technology of China(No.2016YFA0200602 and No.2018YFA0208603)the National Natural Science Foundation of China(No.21573204 and No.21421063)Anhui Initiative in Quantum Information Technologies,Fundamental Research Funds for the Central Universities,National Program for Support of Top-notch Young Professional,CAS Interdisciplinary Innovation Team.
基金supported by the National Natural Science Foundation of China (No.21573204 and No.21421063)Ministry of Science and Technology of China (No.2016YFA0200602)+2 种基金Anhui Initiative in Quantum Information Technologies, Fundamental Research Funds for the Central UniversitiesNational Program for Support of Top-notch Young Professional, Chinese Academy of Sciences Interdisciplinary Innovation TeamSuper Computer Center of USTC supercomputing center and CAS supercomputing center
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.
文摘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.
