Chiral Stacking Identification of Two-Dimensional Triclinic Crystals Enabled by Machine Learning

Abstract

Chiral materials possess broken inversion and mirror symmetry and show great potential in the application of next-generation optic, electronic, and spintronic devices. Two-dimensional (2D) chiral crystals have planar chirality, which is nonsuperimposable on their 2D enantiomers by any rotation about the axis perpendicular to the substrate. The degree of freedom to construct vertical stacking of 2D monolayer enantiomers offers the possibility of chiral manipulation for designed properties by creating multilayers with either a racemic or enantiomerically pure stacking order. However, the rapid recognition of the relative proportion of two enantiomers becomes demanding due to the complexity of stacking orders of 2D chiral crystals. Here, we report the unambiguous identification of racemic and enantiomerically pure stackings for layered ReSe2 and ReS2 using circular polarized Raman spectroscopy. The chiral Raman response is successfully manipulated by the enantiomer proportion, and the stacking orders of multilayer ReSe2 and ReS2 can be completely clarified with the help of second harmonic generation and scanning transmission electron microscopy measurements. Finally, we trained an artificial intelligent Spectra Classification Assistant to predict the chirality and the complete crystallographic structures of multilayer ReSe2 from a single circular polarized Raman spectrum with the accuracy reaching 0.9417 ± 0.0059.