Work

Fast and accurate Fe–H machine-learning interatomic potential for elucidating hydrogen embrittlement mechanisms

Year: 2026

Type: article

Source: International Journal of Hydrogen Energy

DOI: 10.1016/j.ijhydene.2026.155600

Author: Kazuma Ito

Institution: Nippon Steel (Japan)

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7161811406

FWCI: 6.694

Cites: 100

Cited by: 6

Related to: 10