Grain-boundary-induced Fe2CrNi-like ordering in austenitic stainless steels revealed by DFT-based Ising optimization
Predicting segregation behavior in concentrated multicomponent systems remains challenging due to strong solute–solute interactions and vast configuration space. Here, we show that in austenitic Fe0.8 − xCr0.2Nix (x = 0.075–0.15) stainless steels, segregation at a low-energy Σ5(210) grain boundary (GB) gives rise to pronounced chemical ordering that closely resembles the bulk ground-state Fe2CrNi phase. Using a density-functional-theory (DFT)-based Ising optimization framework that combines factorization-machine surrogate modeling with an Ising machine, we efficiently explore the vast configurational space of chemically disordered GB structures. The optimized configurations consistently exhibit strong Cr enrichment and composition-dependent Ni segregation near the GB together with a highly ordered Cr–Ni arrangement, indicating incipient phase formation at the interface. Analysis of the spin-polarized local density of states (LDOS) reveals that the site-dependent energetics are governed by spin splitting of Cr d states and hybridization-driven redistribution of Ni d states, both of which reduce the LDOS at the Fermi level and stabilize specific atomic configurations. These results demonstrate that GBs in concentrated alloys can act as structural and chemical templates for bulk-like ordering, bridging the gap between segregation and chemical ordering. The present work highlights the capability of DFT-driven Ising optimization to uncover emergent ordering phenomena in complex alloys and provides new insight into interface-mediated phase stability.
Authors
- Ai Koizumi (ORCID: https://orcid.org/0000-0001-8426-4908)
- Ryo Tamura (ORCID: https://orcid.org/0000-0002-0349-358X)
- W. T. Geng (ORCID: https://orcid.org/0000-0002-9838-5644)
Institutions
- Zhejiang Normal University (CN)
- University of Tsukuba (JP)
- National Institute for Materials Science (JP)
Publication Details
- Journal
- Journal of Applied Physics
- Published
- 2026-09-22
- DOI
- https://doi.org/10.1063/5.0349407
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00