Tackling Disorder in $\gamma$-Ga$_2$O$_3$
Laura E. Ratcliff, Takayoshi Oshima, Felix Nippert, Benjamin M., Janzen, Elias Kluth, R\"udiger Goldhahn, Martin Feneberg, Piero Mazzolini,, Oliver Bierwagen, Charlotte Wouters, Musbah Nofal, Martin Albrecht, Jack E., N. Swallow, Leanne A. H. Jones, Pardeep K. Thakur

TL;DR
This paper combines density functional theory and machine learning to model the complex disordered structure of $eta$-Ga$_2$O$_3$, validated by various spectroscopic experiments, advancing understanding of its electronic properties for electronic applications.
Contribution
It introduces a novel atomistic model of $eta$-Ga$_2$O$_3$ using machine learning and DFT, validated by experimental spectroscopy, to better understand its disordered structure and electronic behavior.
Findings
Strong absorption onset at 5.1 eV at room temperature
Good agreement between theoretical and experimental excitation energies
Advances in modeling complex disordered oxides
Abstract
GaO and its polymorphs are attracting increasing attention. The rich structural space of polymorphic oxide systems such as GaO offers potential for electronic structure engineering, which is of particular interest for a range of applications, such as power electronics. -GaO presents a particular challenge across synthesis, characterisation, and theory due to its inherent disorder and resulting complex structure -- electronic structure relationship. Here, density functional theory is used in combination with a machine learning approach to screen nearly one million potential structures, thereby developing a robust atomistic model of the -phase. Theoretical results are compared with surface and bulk sensitive soft and hard X-ray photoelectron spectroscopy, X-ray absorption spectroscopy, spectroscopic ellipsometry, and photoluminescence excitation…
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Taxonomy
TopicsGa2O3 and related materials · Machine Learning in Materials Science · Advanced Photocatalysis Techniques
