Determining the absolute configuration of gas-phase molecules in position-space has long been a fundamental challenge in molecular physics. While strong-field-induced Coulomb explosion imaging (CEI) has emerged as a powerful tool for probing molecular stereochemistry in momentum-space, reconstructing the original three-dimensional structure of polyatomic molecules remains a long-standing challenge due to the inherent complexity of multidimensional inversion. Here, we introduce a deep learning framework that bridges this gap by directly recovering position-space molecular structures from Coulomb explosion momentum patterns. Our approach combines CEI simulations with a neural network trained to establish the mapping between momentum-space Newton plots and real-space geometries. The trained model demonstrates high fidelity in reconstructing the structure of CHF3 from experimental CEI data. This generalizable framework can not only be extended to other molecular systems but also opens avenues for time-resolved structural analysis of molecular dynamics.
Cite
@article{arxiv.2512.16559,
title = {Decoding Molecular Geometries in Coulomb Explosion Imaging via Physics-Informed Deep Neural Network},
author = {Xingyu Guo and Enliang Wang and Wenguang Wu and Zhaopeng Xing and Tuo Liu and Chunkai Xu and Xu Shan and Artem Rudenko and Daniel Rolles and Jing Chen and Xiangjun Chen},
journal= {arXiv preprint arXiv:2512.16559},
year = {2025}
}