  Direct imaging of molecular dynamics is a long-standing goal in physics and
chemistry. As an emerging tool, high-harmonic spectroscopy (HHS) enables
accessing molecular dynamics on femtosecond to attosecond time scales. However,
decoding information from the harmonic signals is usually painstaking due to
the coherent nature of high-harmonic generation (HHG). Here we show that this
obstacle can be effectively overcome by exploiting machine learning in HHS.
Combining the machine learning with an angle-resolved HHS method, we
demonstrate that the rich dynamics of molecular rotational wave packet is fully
reconstructed from the angular distributions of HHG measured at various time
delays of the probe pulse. The experimental retrievals are in good agreement
with the numerical simulations. These findings provide a comprehensive picture
of molecular rotation in space and time which will facilitate the development
of related researches on molecular dynamics imaging.
