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Molecular fingerprint and machine learning to accelerate design of high-performance homochiral metal–organic frameworks |
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| Authors: | Zhiwei Qiao Lifeng Li Shuhua Li Hong Liang Jian Zhou Randall Q Snurr |
| Affiliation: | 1. Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou, China;2. Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou, China
Contribution: Data curation, Methodology, Visualization, Writing - original draft;3. Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou, China Contribution: Resources, Supervision, Writing - original draft;4. Guangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou, China Contribution: Funding acquisition, ?Investigation, Resources, Supervision, Writing - original draft;5. School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou, China;6. Department of Chemical & Biological Engineering, Northwestern University, Evanston, Illinois, USA |
| Abstract: | Computational screening was employed to calculate the enantioseparation capabilities of 45 functionalized homochiral metal–organic frameworks (FHMOFs), and machine learning (ML) and molecular fingerprint (MF) techniques were used to find new FHMOFs with high performance. With increasing temperature, the enantioselectivities for (R,S)-1,3-dimethyl-1,2-propadiene are improved. The “glove effect” in the chiral pockets was proposed to explain the correlations between the steric effect of functional groups and performance of FHMOFs. Moreover, the neighborhood component analysis and RDKit/MACCS MFs show the highest predictive effect on enantioselectivities among the four ML classification algorithms with nine MFs that were tested. Based on the importance of MF, 85 new FHMOFs were designed, and a newly designed FHMOF, NO2-NHOH-FHMOF, with high similarity to the optimal MFs achieved improved chiral separation performance, with enantioselectivities of 85%. The design principles and new chiral pockets obtained by ML and MFs could facilitate the development of new materials for chiral separation. |
| Keywords: | enantioseparation machine learning metal–organic framework molecular fingerprint molecular simulation |