A Neural Network Interatomic Potential for the Ternary α-Fe-C-H System: Toward Million-Atom Simulations of Hydrogen Embrittlement in Steel

作者：Fan-Shun Meng, Shuhei Shinzato, Kazuki Matsubara, Jun-Ping Du, Peijun Yu, Shigenobu Ogata · 发表于：JOM · 年份：2025 · DOI：10.1007/s11837-025-07721-4 · 被引用次数：6 · 研究领域：Hydrogen embrittlement and corrosion behaviors in metals、Microstructure and Mechanical Properties of Steels、Nuclear Materials and Properties

Abstract A neural network interatomic potential (NNIP) has been developed for the ternary system of $$\alpha $$ α -iron, carbon, and hydrogen to clarify the degradation behavior of Fe-C steels in hydrogen-rich environments. The NNIP was trained on an extensive reference database generated from spin-polarized density functional theory (DFT) calculations. It demonstrates remarkable performance in various scenarios relevant to Fe and Fe-C systems under hydrogen, including the diffusion kinetics of H and C in Fe and their thermodynamic interactions with iron vacancies, grain boundaries, screw dislocations, cementite, and cementite–ferrite interfaces. Using this NNIP, we conducted large-scale (one-million-atom) molecular dynamics (MD) simulations of uniaxial tensile tests on C-containing $$\alpha $$ α -Fe both with and without H, showing that hydrogen enhances defect accumulation during plastic deformation, which may eventually lead to material failure.