High-Performance Chemical Information Database towards Accelerating Discovery of Metal-Organic Frameworks for Gas Adsorption with Machine Learning |
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Authors: | Zi-kai Hao Hai-feng Lv Da-yong Wang Xiao-jun Wu |
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Institution: | 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 Synergetic Innovation Center of Quantum Information & Quantum Physics,University of Science and Technology of China,Hefei 230026,China |
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Abstract: | Chemical structure searching based on databases and machine learning has attracted great attention recently for fast screening materials with target functionalities. To this end, we established a high-performance chemical structure 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 efficient searching and recommendation. The evaluations results show that MYDB could realize fast and efficient keyword 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 toward 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 functionalities in the field of computational materials research. |
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Keywords: | Chemical informatics Database Search engine Machine learning Gas absorption |
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