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

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 (C₁₀H₁₆O), from laser-induced electron diffraction (LIED) data

SUBMITTER: Liu X 

PROVIDER: S-EPMC9814146 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature