• Citation: K. Ito (2026), "Fast and accurate Fe-H machine-learning interatomic potential for elucidating hydrogen embrittlement mechanisms", International Journal of Hydrogen Energy 242, 155600. DOI: 10.1016/j.ijhydene.2026.155600.
    Abstract: Highly accurate machine-learning interatomic potentials (MLIPs) for the Fe-H system are essential for elucidating hydrogen embrittlement (HE), yet the high computational cost of existing MLIPs limits their applicability in practical, large-scale simulations. In this study, we develop a new MLIP within the Performant Implementation of the Atomic Cluster Expansion framework, trained on a comprehensive HE-related dataset generated via a concurrent-learning strategy. The potential achieves density functional theory-level accuracy for lattice defects in α-Fe, including vacancies, surfaces, grain boundaries, and both screw and edge dislocations, as well as their interactions with hydrogen. Furthermore, extrapolation-grade analysis demonstrates that it reliably captures atomic configurations associated with hydrogen-induced grain boundary fracture formed during uniaxial tensile deformation of hydrogen-segregated nanopolycrystals. Despite its high accuracy, the computational cost is only several tens of times that of empirical potentials and over an order of magnitude lower than existing Fe-H MLIPs, enabling efficient HE simulations.

  • LAMMPS pair_style pace (2026--Ito-K--Fe-H--LAMMPS--ipr1)
    See Computed Properties
    Notes: This file was provided by Kazuma Ito on May 21, 2026.
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Date Created: October 5, 2010 | Last updated: May 22, 2026