3 papers
cond-mat.mtrl-sci2026
A nuclear-quantum-corrected machine-learning potential reveals quantum-enhanced hydrogen segregation at general grain boundaries in alpha-iron
Kazuma Ito
Atomistic descriptions of hydrogen diffusion and trapping at defects are essential for understanding hydrogen embrittlement. As the lightest solute in metals, hydrogen exhibits nuc…
physics.comp-ph2026
Machine-learned atomistic simulations reveal the basis of hydrogen-induced crack-plane transition in alpha-Fe
Jiaqin Xu, Zhiqiang Zhao, Kazuma Ito +4
Hydrogen-related fracture in body-centered cubic Fe and ferritic steels often appears as transgranular quasi-cleavage rather than purely intergranular failure, especially at low to…
cond-mat.mtrl-sci2025
Fast and accurate Fe-H machine-learning interatomic potential for elucidating hydrogen embrittlement mechanisms
Kazuma Ito
Understanding the mechanisms of hydrogen embrittlement (HE) is essential for advancing next-generation high-strength steels, thereby motivating the development of highly accurate m…