Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis-Reference-Cited by-同舟云学术

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Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis

Published:2023-04 Issue: Volume:49 Page:101802
ISSN:1748-0132
Container-title:Nano Today
language:en
Short-container-title:Nano Today

Author:

Lin Jing,Liu Zhimeng,Guo Yujie,Wang Shulin,Tao Zhang,Xue Xiangdong,Li Rushuo,Feng Shihao,Wang Linmeng,Liu Jiangtao,Gao Hongyi,Wang Ge,Su Yanjing

Publisher

Elsevier BV

Subject

Pharmaceutical Science,General Materials Science,Biomedical Engineering,Bioengineering,Biotechnology

Reference268 articles.

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2. The Reticular Chemistry Structure Resource (RCSR) database of, and symbols for, crystal nets;O’Keeffe;Acc. Chem. Res.,2008

3. Hybrid porous solids: past, present, future;Férey;Chem. Soc. Rev.,2008

4. Soft porous crystals;Horike;Nat. Chem.,2009

5. Recent advances in metal-organic-framework-based catalysts for thermocatalytic selective oxidation of organic substances;Wu;Chem. Catal.,2022

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