Imaging a complex molecular structure with laser-induced electron diffraction and machine learning

Imaging a molecular structure with electron or X-ray diffraction relies on finding a global extremum in a multi-dimensional solution space [1]. Laser-induced electron diffraction (LIED) [2] is a powerful laser-based method that images the structure of a single gas-phase molecule with combined sub-atomic picometre and atto-to-femtosecond spatio-temporal resolution [3]. In LIED, the structural information of the molecule is extracted from the coherently scattered electron wave packet, driven by an intense laser field after photoionization. However, retrieving the molecular geometry from a diffraction pattern becomes progressively difficult with increasing molecular structure and is a general challenge for any diffraction-based imaging technique. A machine learning (ML)-based approach is tailored to overcome this limitation since it achieves pattern matching in a complex solution space with high precision. We demonstrate the accurate retrieval of the three-dimensional structure of the chiral molecule Fenchone (C₁₀H₁₆O) by implementing LIED in combination with an ML algorithm [4]. Our results show that ML-LIED provides new opportunities to determine the structure of large and complex molecules.

[1] A. Sanchez-Gonzalez et al., Nat. Commun. 8, 15461 (2017).

[2] T. Zuo et al., Chem. Phys. Lett. 259, 313 (1996).

[3] A. Sanchez et al., Nat. Commun. 12, 1520 (2021).

[4] X. Liu et al., Comm. Chem. 4, 154 (2021).