Using Dynasor 2.0 for Connecting Simulation to Experiment Through Correlation Functions

The dynamic properties of materials are fundamental to their thermodynamic, kinetic, optical, and transport behaviors. The dynamics are commonly probed by neutron or X-ray scattering experiments, which provide quantitative information in the form of dynamic and static structure factors. Structure factors can also be calculated from atomic-scale modeling via molecular dynamics (MD) simulations, offering a quantitative bridge between experiment and atomic-scale modeling.<br/><br/>We present the latest iteration of the dynasor package, a flexible and efficient tool for calculating correlation functions, such as static and dynamic structure factors, as well as related correlation functions from MD simulations. The package is user-friendly written in python and can thus easily be integrated into workflows for analyzing dynamics, simulating experimental spectra, and understanding phonon dynamics. Analyzing correlation functions provides detailed insights into the dynamics of a system without the need for perturbative methods. Additionally, this allows for the direct prediction of experimental spectra by weighting the functions with cross sections (or form factors) of neutrons, X-rays, or electrons. We demonstrate how dynasor can be used to analyze dynamics in various systems from MD simulations based on machine-learned potentials. For example, in the Ni-Al system, we analyze how the dynamics change between the ordered and disordered phases. Additionally, we investigate the quasi-elastic neutron scattering (QENS) spectra of the inorganic halide perovskite CsPbI3, explaining low-frequency intensity features that arise due to soft, over-damped phonon modes. These case studies highlight dynasor's capability to provide detailed insights into the dynamic behavior of materials, and how it can be used to help interpret experimental observations.