Machine learning for laser-induced electron diffraction imaging of molecular structures

TL;DR

This paper introduces a machine learning approach using convolutional neural networks to efficiently retrieve complex molecular structures from laser-induced electron diffraction data, overcoming previous computational limitations.

Contribution

The study presents a novel machine learning algorithm capable of reconstructing large molecular structures from LIED data without extensive pre-calculations or fitting procedures.

Findings

Successful structural retrieval of Fenchone molecule from LIED data

Machine learning enables imaging of larger molecules not possible with traditional methods

Method reduces computational complexity in molecular structure determination

Abstract

Ultrafast diffraction imaging is a powerful tool to retrieve the geometric structure of gas-phase molecules with combined picometre spatial and attosecond temporal resolution. However, structural retrieval becomes progressively difficult with increasing structural complexity, given that a global extremum must be found in a multi-dimensional solution space. Worse, pre-calculating many thousands of molecular configurations for all orientations becomes simply intractable. As a remedy, here, we propose a machine learning algorithm with a convolutional neural network which can be trained with a limited set of molecular configurations. We demonstrate structural retrieval of a complex and large molecule, Fenchone (C$_{10}$H$_{16}$O), from laser-induced electron diffraction (LIED) data without fitting algorithms or ab initio calculations. Retrieval of such a large molecular structure is not possible with other variants of LIED or ultrafast electron diffraction. Combining electron diffraction with machine learning presents new opportunities to image complex and larger molecules in static and time-resolved studies.