Formation of three-dimensional dislocation networks in α-iron twist grain boundaries: Insights from first-principles neural network interatomic potentials

FS Meng and JH Li and S Shinzato and K Matsubara and WT Geng and S Ogata, COMPUTATIONAL MATERIALS SCIENCE, 253, 113812 (2025).

DOI: 10.1016/j.commatsci.2025.113812

We conducted a systematic analysis of the atomic structure and energy of (001), (110), and (111) twist grain boundaries (TWGBs) in a-iron using a recently developed neural network interatomic potential (NNIP). This study showcases typical dislocation networks within TWGBs that exhibit small twist angles. Notably, we observed a three-dimensional (3D) dislocation network in (111) twist grain boundaries, primarily composed of 2 < 111 > dislocations-structures unattainable using previously proposed empirical potentials, hence unreported 1 in earlier studies. The novel 3D dislocation network was further validated through several approaches, including principal component analysis (PCA), an NNIP ensemble model, and cross-validation with other machine learning interatomic potentials designed for a-iron. This breakthrough offers a new perspective on the properties of twist grain boundaries, potentially impacting our understanding of their strength, toughness, and mobility.

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