Machine-Learning Enhanced Enantioselective Single-Shot-Single-Molecule ac Stark Spectroscopy

Abstract

Enantiodiscrimination with single-molecule and single-shot resolution is fundamental for the understanding of the fate and behavior of two enantiomers in chemical reactions, biological activity, and the function of drugs. However, molecular decoherence gives rise to spectral broadening and random errors, offering major problems for most chiroptical methods in arriving at single-shot-single-molecule resolution. Here, we introduce a machine-learning strategy to solve these problems. Specifically, we focus on the task of single-shot measurement of single-molecule chirality based on enantioselective ac Stark spectroscopy. We find that, in the large-decoherence region, where the ac Stark spectroscopy without machine learning fails to distinguish molecular chirality, in contrast, the machine-learning-assisted strategy still holds a high correct rate of up to about 90%. Beyond this overwhelming superiority, the machine-learning strategy also has considerable robustness against variation of the decoherence rates between the training and testing sets.

Optical-pumping enantio-conversion of chiral mixtures in presence of tunneling between chiral states

“…In the field of medicine, acquiring homochiral medicines that offer benefits to humans is of paramount importance. Consequently, enantio-discrimination [3][4][5][6][7][8][9][10][11][12][13][14][15][16], spatial enantio-separation [17][18][19][20][21][22][23][24], enantio-specific state transfer [25][26][27][28][29][30][31][32][33][34][35][36], and enantio-conversion [37][38][39][40][41][42][43][44][45] of chiral mixtures have become crucial research topics in the chemical and biological fields.…”

“…In the past two decades, various theoretical schemes on enantio-discrimination [3][4][5][6][7][8][9][10][11][12][13][14][15][16], spatial enantio-separation [17][18][19][20][21][22][23][24], and enantio-specific state transfer [25][26][27][28][29][30][31][32][33][34][35] have been proposed based on the three-level ∆-type model of chiral molecules. This model is composed of three electromagnetic fields coupled respectively to three related electric-dipole transitions of chiral molecules.…”

“…This model is composed of three electromagnetic fields coupled respectively to three related electric-dipole transitions of chiral molecules. By taking advantage of the property of the overall phase-difference π of the three Rabi frequencies for the two enantiomers, enantio-discrimination [3][4][5][6][7][8][9][10][11][12][13][14][15][16], spatial enantio-separation [17][18][19][20][21][22][23][24], and enantio-specific state transfer [25][26][27][28][29][30][31][32][33][34][35] of chiral mixtures can be achieved. Particularly, the experiments on enantio-discrimination [46][47][48][49][50][51][52] and enantio-specific state transfer [53][54][55] have been achieved based on the three-level ∆-type model of chiral molecules.…”

Nitrile Vibrational Lifetimes as Probes of Local Electric Fields

“…Electric fields are central to many aspects of the physical sciences, including chemical reactivity, cell signaling, and more. − Over 100 years ago, Stark reported that electric fields can affect how atoms and molecules interact with light; in particular, he observed that degenerate spectral lines in hydrogen are split into multiple components in a strong electric field . This phenomenon is now called the Stark effect and provides useful information across much of the electromagnetic spectrum. − For example, electric fields almost fully break degeneracy in gas-phase microwave rotational spectroscopy, facilitating detailed spectral assignments that provide fundamental understanding of molecular structure and geometry. − The electronic Stark effect is the physical basis for sensing cellular potentials with electrochromic dyes, whose fluorescence emission spectra are influenced by the local electric field . Electric field sensing has broad-ranging applications in molecular biology, from characterizing enzyme active sites to mapping spatially heterogeneous hydration. − …”