Generative Modeling Enables Molecular Structure Retrieval from Coulomb Explosion Imaging

• URL: http://arxiv.org/abs/2511.00179v1
• Date: Fri, 31 Oct 2025 18:33:40 GMT
• Title: Generative Modeling Enables Molecular Structure Retrieval from Coulomb Explosion Imaging
• Authors: Xiang Li, Till Jahnke, Rebecca Boll, Jiaqi Han, Minkai Xu, Michael Meyer, Maria Novella Piancastelli, Daniel Rolles, Artem Rudenko, Florian Trinter, Thomas J. A. Wolf, Jana B. Thayer, James P. Cryan, Stefano Ermon, Phay J. Ho,
• Abstract summary: We use a diffusion-based Transformer neural network to reconstruct unknown geometries from ion-momentum distributions.<n>We show that the network reconstructs unknown molecular geometries from ion-momentum distributions with a mean absolute error below one Bohr radius.
• Score: 46.04582001144512
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Capturing the structural changes that molecules undergo during chemical reactions in real space and time is a long-standing dream and an essential prerequisite for understanding and ultimately controlling femtochemistry. A key approach to tackle this challenging task is Coulomb explosion imaging, which benefited decisively from recently emerging high-repetition-rate X-ray free-electron laser sources. With this technique, information on the molecular structure is inferred from the momentum distributions of the ions produced by the rapid Coulomb explosion of molecules. Retrieving molecular structures from these distributions poses a highly non-linear inverse problem that remains unsolved for molecules consisting of more than a few atoms. Here, we address this challenge using a diffusion-based Transformer neural network. We show that the network reconstructs unknown molecular geometries from ion-momentum distributions with a mean absolute error below one Bohr radius, which is half the length of a typical chemical bond.

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