# Molecular rotation movie filmed with high-harmonic generation

## TL;DR

This paper demonstrates that machine learning combined with angle-resolved high-harmonic spectroscopy can fully reconstruct molecular rotational dynamics in space and time, advancing molecular imaging techniques.

## Contribution

It introduces a novel approach integrating machine learning with HHS to decode complex molecular rotational dynamics from harmonic signals.

## Key findings

- Successful reconstruction of molecular rotational wave packets.
- Good agreement between experimental data and simulations.
- Enhanced understanding of molecular rotation in space and time.

## Abstract

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.