Large-scale atomistic study of plasticity in amorphous gallium oxide with a machine-learning potential
Jiahui Zhang, Junlei Zhao, Jesper Byggm\"astar, Erkka J. Frankberg,, Antti Kuronen

TL;DR
This study uses machine learning to perform large-scale atomistic simulations, revealing the structural and mechanical properties of amorphous gallium oxide, including its glass transition, density, and plastic behavior, with implications for its resistance to shear banding.
Contribution
It introduces a machine-learning interatomic potential for large-scale simulations of amorphous Ga2O3, providing new insights into its structure and mechanical response.
Findings
Amorphous Ga2O3 exhibits a distinct glass transition at 1234-1348 K.
Structural properties of a-Ga2O3 are similar to amorphous alumina.
a-Ga2O3 shows high plasticity and potentially higher resistance to shear banding.
Abstract
Compared to the widely investigated crystalline polymorphs of gallium oxide (Ga2O3), knowledge about its amorphous state is still limited. With the help of a machine-learning interatomic potential, we conducted large-scale atomistic simulations to investigate the glass transition and mechanical behavior of amorphous Ga2O3 (a-Ga2O3). During the quenching simulations, amorphization of gallium oxide melt is observed at ultrahigh cooling rates, including a distinct glass transition. The final densities at room temperature have up to 4% variance compared to experiments. The glass transition temperature is evaluated to range from 1234 K to 1348 K at different cooling rates. Structural analysis of the amorphous structure shows evident similarities in structural properties between a-Ga2O3 and amorphous alumina (a-Al2O3), such as radial distribution function, coordination distribution, and bond…
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Taxonomy
TopicsMachine Learning in Materials Science
