Revealing hydrogen dynamics and embrittlement resistance in Cu-modified Al-Sc alloys using machine learning potential

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Revealing hydrogen dynamics and embrittlement resistance in Cu-modified Al-Sc alloys using machine learning potential

Journal Article (2026)

Author(s)

Yucheng Ji (University of Science and Technology Beijing)

Xiaoqian Fu (University of Science and Technology Beijing)

Poulumi Dey (TU Delft - Team Poulumi Dey)

Chaofang Dong (University of Science and Technology Beijing)

Research Group

Team Poulumi Dey

DOI related publication

https://doi.org/10.1016/j.matlet.2026.140170

Hydrogen Precipitate Molecular dynamics Al alloy Machine learning potential

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https://resolver.tudelft.nl/uuid:baf34b40-6026-4915-82d6-6b956258697f

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Publication Year

2026

Language

English

Research Group

Team Poulumi Dey

Volume number

409

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Abstract

Employing a machine learning potential tailored for the AlScCuH system, this study elucidates the dynamic behavior of atomic H in AlScCu alloys. Cu-doped Al3Sc precipitate exhibits a pronounced ability to trap H, thereby diminishing H concentration at critical regions, for instance in front of a crack tip. Such H localization at the crack tip was found to lower both critical stress and threshold strain, which results in significant degradation of the alloy's mechanical properties.

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File under embargo until 29-07-2026