Exploiting correlations in multi-coincidence Coulomb explosion patterns for differentiating molecular structures using machine learning.

ABSTRACT: Coulomb explosion imaging (CEI) can map the real-time coordinated motion of atoms in molecules during ultrafast photochemical reactions via correlations embedded in the resulting high-dimensional data. However, this rich information remains largely underexploited due to challenges in visualizing relationships between multiple observables in multidimensional parameter space. Here, we present a new approach to CEI of polyatomic molecules, detecting up to eight ionic fragments in coincidence and leveraging machine-learning-based analysis to identify patterns and correlations. Our method yields high-dimensional, background-free momentum-space data and establishes an automated, scalable framework for extracting insightful structural information, enabling robust identification and differentiatio

SUBMITTER: Venkatachalam AS 

PROVIDER: S-EPMC12727871 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature