Molecular fingerprint and machine learning to accelerate design of high-performance homochiral metal–organic frameworks

Zhiwei Qiao*, Lifeng Li, Shuhua Li, Hong Liang, Jian Zhou, Randall Q. Snurr

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article numbere17352
JournalAIChE Journal
Volume67
Issue number10
DOIs
StatePublished - Oct 2021

Keywords

  • enantioseparation
  • machine learning
  • metal–organic framework
  • molecular fingerprint
  • molecular simulation

ASJC Scopus subject areas

  • Biotechnology
  • Environmental Engineering
  • Chemical Engineering(all)

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