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

FS Meng and S Shinzato and K Matsubara and JP Du and PJ Yu and S Ogata, JOM, 77, 8101-8117 (2025).

DOI: 10.1007/s11837-025-07721-4

A neural network interatomic potential (NNIP) has been developed for the ternary system of alpha\documentclass12ptminimal \usepackageamsmath \usepackagewasysym \usepackageamsfonts \usepackageamssymb \usepackageamsbsy \usepackagemathrsfs \usepackageupgreek \setlength\oddsidemargin-69pt \begindocument$$\alpha $$\enddocument-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\documentclass12ptminimal \usepackageamsmath \usepackagewasysym \usepackageamsfonts \usepackageamssymb \usepackageamsbsy \usepackagemathrsfs \usepackageupgreek \setlength\oddsidemargin-69pt \begindocument$$\alpha $$\enddocument-Fe both with and without H, showing that hydrogen enhances defect accumulation during plastic deformation, which may eventually lead to material failure.

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