Extensive study on the α-Fe grain boundaries using molecular dynamics simulations using a deep learning potential

Abstract

Grain boundaries (GBs) critically influence the mechanical properties of polycrystalline metallic materials, yet systematic atomic-level understanding of GB structures and deformation mechanisms in α-Fe remains limited. In this study, we employ deep learning-based molecular dynamics simulations to investigate the atomic structures, grain boundary energies, and tensile deformation behaviors of 40 symmetric tilt GBs (STGBs) in α-Fe with [100] and [110] rotation axes. The relaxed GB structures exhibit strong misorientation dependence, with low-angle boundaries characterized by dislocation walls and high-angle boundaries showing diverse atomic arrangements. Calculated GB energies range from 1.003–1.336 J m−2 for [100] STGBs and from 0.281–1.482 J m−2 for [110] STGBs, with deep energy cusps at special orientations such as the Σ3 (1 1¯ 2¯)[110] coherent twin boundary (0.281 J m−2). Tensile simulations reveal that the Σ3 boundary exhibits exceptional strength and ductility due to its ordered structure and delocalized slip activation, while high-energy GBs fail in a brittle manner. Deformation mechanism analysis demonstrates that ductile GBs accommodate plasticity through {110}< 111> and {112}< 111> slip systems propagating from the interface into grain interiors. This work provides atomic-scale insights into GB structure–property relationships in α-Fe and offers theoretical guidance for grain boundary engineering of bcc iron-based alloys.