Communications Physics (Nov 2023)

Applying Bayesian inference and deterministic anisotropy to retrieve the molecular structure ∣Ψ(R)∣2 distribution from gas-phase diffraction experiments

  • Kareem Hegazy,
  • Varun Makhija,
  • Phil Bucksbaum,
  • Jeff Corbett,
  • James Cryan,
  • Nick Hartmann,
  • Markus Ilchen,
  • Keith Jobe,
  • Renkai Li,
  • Igor Makasyuk,
  • Xiaozhe Shen,
  • Xijie Wang,
  • Stephen Weathersby,
  • Jie Yang,
  • Ryan Coffee

DOI
https://doi.org/10.1038/s42005-023-01420-9
Journal volume & issue
Vol. 6, no. 1
pp. 1 – 17

Abstract

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Abstract Currently, our general approach to retrieving molecular structures from ultrafast gas-phase diffraction heavily relies on complex ab initio electronic or vibrational excited state simulations to make conclusive interpretations. Without such simulations, inverting this measurement for the structural probability distribution is typically intractable. This creates a so-called inverse problem. Here we address this inverse problem by developing a broadly applicable method that approximates the molecular frame structure ∣Ψ(R, t)∣2 distribution independent of these complex simulations. We retrieve the vibronic ground state ∣Ψ(R)∣2 for both simulated stretched NO2 and measured N2O. From measured N2O, we observe 40 mÅ coordinate-space resolution from 3.75 Å−1 reciprocal space range and poor signal-to-noise, a 50X improvement over traditional Fourier transform methods. In simulated NO2 diffraction experiments, typical to high signal-to-noise levels predict 100–1000X resolution improvements, down to 0.1 mÅ. By directly measuring the width of ∣Ψ(R)∣2, we open ultrafast gas-phase diffraction capabilities to measurements beyond current analysis approaches. This method has the potential to effectively turn gas-phase ultrafast diffraction into a discovery-oriented technique to probe systems that are prohibitively difficult to simulate.