  Characterizing chirality is highly important for applications in the
pharmaceutical industry, as well as in the study of dynamical chemical and
biological systems. However, this task has remained challenging, especially due
to the ongoing increasing complexity and size of the molecular structure of
drugs and active compounds. In particular, large molecules with many active
chirality centers are today ubiquitous, but remain difficult to structurally
analyze due to their high number of stereoisomers. Here we theoretically
explore the sensitivity of high harmonic generation (HHG) to the chirality of
molecules with a varying number of active chiral centers. We find that HHG
driven by bi-chromatic non-collinear lasers is a sensitive probe for the
stereo-configuration of a chiral molecule. We first show through calculations
(from benchmark chiral molecules with up to three chirality centers) that the
HHG spectrum is imprinted with information about the handedness of each
chirality center in the driven molecule. Next, we show that using both
classical- and deep-learning-based reconstruction algorithms, the composition
of an unknown mixture of stereoisomers can be reconstructed with high fidelity
by a single-shot HHG measurement. Our work illustrates how the combination of
non-linear optics and machine learning might open routes for ultra-sensitive
sensing in chiral systems.
