A machine learning-based interatomicpotential for Fe using marginalized graph kernels

Machine learning frameworks have been proposed lately to significantly reduce the computational cost of methods that require density functional theory (DFT) data without compromising excessively on the accuracy. Systems such as iron (Fe) have been extensively investigated because of its variety of applications, but the complexity of the material due to the several polymorphic transitions within it has made it a challenging task. We developed and characterized several Gaussian Process Regression (GPR) machine learning models for non-spin-polarized Fe at high pressure trained with DFT molecular dynamics (MD) data. The marginalized graph kernel is used to compute the similarity between pairs of graphs that represent distinct atomic configurations generated by the MD simulations and GPR predictions of the energy are also based on this similarity. The best single-volume models have prediction errors (RMSE) below 10 meV/atom achieved with several hundred atomic configurations with 128-atom supercells.