Local force field of thermally displaced atoms in unstable bcc iron from machine learning

A dataset of energy versus atomic thermal displacements was created from density functional theory molecular dynamics simulations of non-spin-polarized body-centered cubic iron at pressures centered at 7 GPa and temperatures centered at 1700K, and Gaussian process regression was used to make predictions based on the similarity between atomic displacements as determined by a graph kernel. Force versus displacement relationships were computed at randomly selected timesteps of the simulations for all atoms in the supercell in the directions of their first-, second-, and third- nearest-neighbors. The atoms experience a generally restorative force in the direction of their first- and third-nearest neighbors, but are unstable in the direction of their second-nearest neighbors. The predicted dynamics are consistent with a martensitic phase transition to face-centered cubic, which is the thermodynamically stable phase of iron at the investigated temperature and pressure conditions.