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<p>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 differentiation of molecular structures. We demonstrate the method by imaging and distinguishing dichloroethylene isomers, showcasing its potential for broader applications in molecular imaging. Our results pave the way for channel-specific analysis of ultrafast structural dynamics in chemically relevant systems, particularly for disentangling mixed reaction pathways and detecting contributions from weak channels and minority species.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月12日

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  • Anbu Selvam Venkatachalam,
  • Loren Greenman,
  • Joshua Stallbaumer,
  • Artem Rudenko,
  • Daniel Rolles,
  • Huynh Van Sa Lam

摘要

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 differentiation of molecular structures. We demonstrate the method by imaging and distinguishing dichloroethylene isomers, showcasing its potential for broader applications in molecular imaging. Our results pave the way for channel-specific analysis of ultrafast structural dynamics in chemically relevant systems, particularly for disentangling mixed reaction pathways and detecting contributions from weak channels and minority species.