From ab-initio to scattering experiments using neuroevolution potentials

Machine-learned interaction potentials have in recent years emerged as an appealing alternative to traditional methods for obtaining forces for molecular dynamics simulations, combining the computational efficiency of semi-empiricial potentials with the accuracy of ab-inito methods. In particular, Neuroevolution Potential (NEP) models, as implemented in the GPUMD package, are highly accurate and computationally efficient, enabling large scale MD simulations with system sizes up to millions of atoms with ab-initio level accuracy. In this work, we present a Python workflow for constructing and sampling NEPs using the `calorine` package, and how the resulting trajectories can be analysed with the `dynasor` package to predict observables from scattering experiments. We focus on our recent work on predicting inelastic neutron scattering spectra (INS) for crystalline benzene as an example system, but the approach is readily extendable to other systems.