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

Area of Science:

• Physical Chemistry
• Chemical Physics
• Molecular Dynamics

Background:

• Coulomb explosion imaging (CEI) offers insights into ultrafast molecular dynamics.
• Analyzing multidimensional data from CEI presents visualization and interpretation challenges.
• Current methods often fail to fully exploit the rich information in CEI data.

Purpose of the Study:

• To develop an advanced CEI approach for polyatomic molecules.
• To leverage machine learning for pattern recognition in complex CEI data.
• To enable robust molecular structure identification and differentiation.

Main Methods:

• Detecting up to eight ionic fragments in coincidence.
• Applying machine-learning-based analysis to identify patterns and correlations.
• Generating high-dimensional, background-free momentum-space data.

Main Results:

• Established an automated, scalable framework for extracting structural information.
• Successfully imaged and distinguished dichloroethylene isomers.
• Demonstrated potential for identifying weak reaction channels and minority species.

Conclusions:

• The new CEI method enhances the analysis of ultrafast structural dynamics.
• It enables channel-specific analysis and disentangling of mixed reaction pathways.
• This approach has broad applications in molecular imaging and chemical dynamics research.