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Related Experiment Video

Updated: May 5, 2026

Coulomb Explosion Imaging as a Tool to Distinguish Between Stereoisomers

Coulomb Explosion Imaging as a Tool to Distinguish Between Stereoisomers

Published on: August 18, 2017

Generative modeling enables molecular structure retrieval from Coulomb explosion imaging.

Xiang Li¹, Till Jahnke^2,3, Rebecca Boll²

¹Linac Coherent Light Source, SLAC National Accelerator Laboratory, Menlo Park, CA, USA. xiangli@slac.stanford.edu.

Nature Communications

|March 2, 2026

Summary

This summary is machine-generated.

Researchers developed a Transformer neural network to reconstruct molecular structures from ion momentum data. This breakthrough advances the understanding of femtochemistry by solving a complex inverse problem for larger molecules.

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Area of Science:

Molecular dynamics
Chemical physics
Computational chemistry

Background:

Understanding molecular structural dynamics is crucial for controlling chemical reactions.
Coulomb explosion imaging using X-ray free-electron lasers (XFELs) offers insights into molecular structures.
Retrieving molecular structures from ion momentum distributions is a complex, unsolved inverse problem for larger molecules.

Purpose of the Study:

To develop a novel computational method for reconstructing molecular geometries from ion momentum distributions.
To address the challenge of solving the inverse problem for molecules beyond a few atoms.

Main Methods:

Utilized a diffusion-based Transformer neural network architecture.
Applied the network to analyze ion momentum distributions obtained from Coulomb explosion imaging.

Main Results:

The neural network successfully reconstructed unknown molecular geometries.
Achieved a mean absolute error below one Bohr radius in structural reconstruction.
Demonstrated a significant advancement in solving the inverse problem for larger molecular systems.

Conclusions:

The developed diffusion-based Transformer network provides an effective solution for retrieving molecular structures from Coulomb explosion data.
This method paves the way for deeper insights into femtochemistry and molecular dynamics.
Offers a powerful tool for analyzing complex molecular reactions in real-time.