用扩散Transformer模型从离子动量分布重建分子结构，精度达半根化学键长度。
Generative Modeling Enables Molecular Structure Retrieval from Coulomb Explosion Imaging
- 基于扩散Transformer网络，从离子动量分布逆推分子几何
- 平均绝对误差低于1个玻尔半径，即典型化学键的一半
- 适合研究飞秒化学中复杂分子结构动态，可拓展至多原子体系
在真实空间和时间中捕捉分子在化学反应过程中的结构变化，是理解并最终控制飞秒化学的关键挑战。库仑爆炸成像（Coulomb explosion imaging）为此提供了重要途径，尤其得益于近年来高重复率X射线自由电子激光源的发展。该技术通过分析分子快速库仑爆炸产生的离子动量分布，推断分子结构信息。然而，从这些分布中重构分子结构属于高度非线性的逆问题，对于超过几个原子的分子仍无解。本文采用基于扩散的Transformer神经网络，成功实现对未知分子几何结构的重建，平均绝对误差低于1个玻尔半径（Bohr radius），约为典型化学键长度的一半。
原文摘要 · Abstract (English)
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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