Decoding Molecular Geometries in Coulomb Explosion Imaging via Physics-Informed Deep Neural Network

TL;DR

This paper presents a physics-informed deep neural network that accurately reconstructs three-dimensional molecular geometries from Coulomb explosion imaging data, enabling detailed molecular structure analysis.

Contribution

The study introduces a novel deep learning framework that directly maps Coulomb explosion momentum patterns to molecular structures, improving reconstruction accuracy and generalizability.

Findings

High-fidelity reconstruction of CHF$_3$ molecular geometry

Effective mapping from momentum-space to position-space structures

Potential for time-resolved molecular dynamics analysis

Abstract

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 CHF$_3$ 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.