Cheminformatics of Functional Nanoporous Materials

Owing to their superior catalytic, adsorption, and separation properties, nanoporous materials, such as zeolites, have been widely used in the fields of energy, environment, and sustainable chemistry. However, the reaction processes, crystallization mechanisms, and structure-property relationship of nanoporous materials remain unclear.  Different from traditional chemical theories and experimentation, cheminformatics utilizes computer simulations and machine learning techniques to extract the hidden chemical information behind a large amount of experimental and computational data, which provides an alternative solution to forementioned problems. 

 Our group utilizes advanced cheminformatics techniques (such as molecular simulations, quantum chemistry, database, machine learning, etc.) to study several key scientific issues in the development of novel functional nanoporous materials, including: 

1. High-throughput structure prediction of nanoporous materials

2. High-throughput screening of nanoporous materials according to their properties

3. Database development for nanoporous materials

4. Machine learning of the structure-property relationship and the reaction/formation mechanisms of nanoporous materials

Selected publications：

1.  J. Phys. Chem. Lett. 2019, 10, 1411-1415. DOI: (10.1021/acs.jpclett.9b00136)

2.  ACS Nano 2018, 12, 4096. DOI: (10.1021/acsnano.8b02625)

3.  Chem 2017, 3, 928. (DOI: 10.1016/j.chempr.2017.10.009)

4.  Chem. Sci. 2016, 7, 3472. (DOI: 10.1039/c6sc00123h)

5.  Nat. Commun. 2015, 6, 8328. (DOI: 10.1038/ncomms9328)

6.  Chem. Rev. 2014, 114, 7268. (DOI: 10.1021/cr500010r)

7.  Angew. Chem. Int. Ed. 2013, 52, 1673. (DOI: 10.1002/anie.201206340)

8.  Angew. Chem. Int. Ed. 2008, 47, 4401. (DOI: 10.1002/anie.200705175)