Computational Approaches to Model X-ray Photon Correlation Spectroscopy from Molecular Dynamics

TL;DR

This paper introduces a computational framework that models X-ray photon correlation spectroscopy (XPCS) and speckle visibility spectroscopy (XSVS) data directly from molecular dynamics simulations, enhancing interpretation of experimental results.

Contribution

It compares two computational methods for simulating X-ray scattering from MD data, improving efficiency and accuracy in modeling dynamic processes.

Findings

The fast Fourier transform method is more efficient for computing scattering intensities.

The simulated speckle patterns accurately reproduce experimental properties.

The framework links atomic-scale dynamics to macroscopic XPCS and XSVS measurements.

Abstract

X-ray photon correlation spectroscopy (XPCS) allows for the resolution of dynamic processes within a material across a wide range of length and time scales. X-ray speckle visibility spectroscopy (XSVS) is a related method that uses a single diffraction pattern to probe ultrafast dynamics. Interpretation of the XPCS and XSVS data in terms of underlying physical processes is necessary to establish the connection between the macroscopic responses and the microstructural dynamics. To aid the interpretation of the XPCS and XSVS data, we present a computational framework to model these experiments by computing the X-ray scattering intensity directly from the atomic positions obtained from molecular dynamics (MD) simulations. We compare the efficiency and accuracy of two alternative computational methods: the direct method computing the intensity at each diffraction vector separately, and a method based on fast Fourier transform that computes the intensities at all diffraction vectors at once. The computed X-ray speckle patterns capture the density fluctuations over a range of length and time scales and are shown to reproduce the known properties and relations of experimental XPCS and XSVS for liquids.