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

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.