2026-04-28 アルゴンヌ国立研究所（ANL）
Schematic of MOLEXA reconstructing molecular structure from Coulomb explosion imaging data. Ion momentum patterns generated by intense X-ray pulses are analyzed by a generative AI model to infer molecular geometry and its uncertainty. (Image by Stacy Huang.)
＜関連情報＞
- https://www.anl.gov/article/ai-rebuilds-molecules-from-exploding-fragments
- https://www.nature.com/articles/s41467-026-70160-5
生成モデリングにより、クーロン爆発イメージングから分子構造を抽出できる Generative modeling enables molecular structure retrieval from Coulomb explosion imaging
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
Nature Communications Published:03 March 2026
DOI:https://doi.org/10.1038/s41467-026-70160-5
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 has 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 nonlinear 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.
