Gallium oxide crystal complexity tamed by machine learning

Gallium oxide crystal complexity tamed by machine learning
Atomic structure of γ-Ga2O3. a) Schematic representation of the crystal structure with inequivalent Ga positions given numbers (1,3) for tetrahedral Td and (2,4) for octahedral Oh ordination. b,c) Atomic resolution image of γ-Ga2O3 crystallized on a sapphire substrate. b) High-resolution phase contrast image along the [110] projection. The inset shows an expanded view as well as an image simulation. The latter is overlaid with an atomic model (red atoms are oxygen, green, and blue are fourfold and sixfold coordinated Ga atoms. c) STEM high-angle annular dark-field image of the same area. The bright atoms correspond to Ga. An atomic model is overlaid to the image. The image pattern fluctuates between a single periodicity and a double periodicity along the (111) planes of the structure. The inset shows details of the microscopy image that correspond to an occupation resembling that of the β-structure in the <132 > projection (double periodicity, left inset) and to an occupation of the γ-structure along the <110 > projection (single periodicity, right inset). Figure 1a was prepared using the VESTA software package. Credit: Advanced Materials (2022). DOI: 10.1002/adma.202204217

Researchers at the University of Liverpool, the University of Bristol, University College London (UCL), and Diamond Light Source have developed new understanding of gallium oxide by combining a machine-learning theoretical approach with experimental results.

In a paper published in the journal Advanced Materials, researchers used a combination of theoretical approaches and machine learning techniques to identify the key characteristics of gallium , a material that has promising applications in power electronics and solar-blind photodetectors.

Gallium oxide presents a particular challenge across synthesis, characterization, and theory due to its inherent disorder and resulting —electronic structure relationship.

It has five different phases or crystal structures, known as alpha, beta, gamma, delta and epsilon. The gamma phase was first suspected to exist in 1939, but it remained largely elusive until 2013 when more details of its structure were found using neutron diffraction. It has four inequivalent gallium lattice sites that are partially occupied in an inherently disordered structure, so that in spite of its deceptively simple cubic symmetry, it is in fact immensely complex. The enormous number of possible crystal structures makes conventional theoretical approaches impossible.

Lead author of the study, Dr. Laura Ratcliff from University of Bristol's Center for Computational Chemistry, said, "To address the challenge of developing a robust atomistic model, first principles calculations are combined with machine learning to screen almost one million possible structures in 160-atom cells. The predicted low energy configurations provide a good description of the experimental data, whilst clear deviations are found for the higher energy configurations, confirming that these are not a realistic description of the disorder in gamma-gallium oxide."

Dr. Anna Regoutz of the Department of Chemistry at UCL said, "Our data from the Diamond Light Source and from collaborators around the world was crucial for validating the theoretical findings."

Tim Veal, Professor of Materials Physics at the University of Liverpool, said, "This detailed understanding of the influence of structural disorder on the electronic structure of gamma-gallium oxide is crucial to provide a firm knowledge base for this and other disordered materials. This enables furthering optimization and implementation across different applications of this and related materials."

Dr. Leanne Jones, a Ph.D. student from the University of Liverpool's Department of Physics and the Stephenson Institute for Renewable Energy who worked on the study, said, "This research addresses a gap in our understanding of this material and will help gamma-gallium oxide to reach its potential in applications."

Explore further

MOCVD tool to advance gallium-oxide semiconductor research

More information: Laura E. Ratcliff et al, Tackling Disorder in γ‐Ga 2 O 3, Advanced Materials (2022). DOI: 10.1002/adma.202204217
Journal information: Advanced Materials

Citation: Gallium oxide crystal complexity tamed by machine learning (2022, August 17) retrieved 6 October 2022 from
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.

Feedback to editors