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Zikai Hao, Haifeng Lv, Dayong Wang, Xiaojun Wu. High-performance Chemical Information Database towards Accelerating Discovery of Metal–Organic Frameworks for Gas Adsorption with Machine Learning[J]. Chinese Journal of Chemical Physics .
Citation: Zikai Hao, Haifeng Lv, Dayong Wang, Xiaojun Wu. High-performance Chemical Information Database towards Accelerating Discovery of Metal–Organic Frameworks for Gas Adsorption with Machine Learning[J]. Chinese Journal of Chemical Physics .

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

  • Received Date: 2021-04-29
  • Accepted Date: 2021-05-13
  • Rev Recd Date: 2021-05-07
  • Available Online: 2021-05-26
  • 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 160,000 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 realizes fast and efficient keyword searching against millions of records and provides real-time recommendations for similar structures. Combining machine learning method and materials database, we developed an adsorption model to determine the adsorption capacitor of MOFs 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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    通讯作者: 陈斌, bchen63@163.com
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      沈阳化工大学材料科学与工程学院 沈阳 110142

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