Exploring the Crystal Structure and Electronic Properties of γ-Al2O3: Machine Learning Drives Future Material Innovations.

Abstract

For decades, researchers have struggled to determine the precise crystal structure of γ-Al2O3 due to its atomic-level disorder and the challenges associated with obtaining high-purity, high-crystallinity γ-Al2O3 in laboratory settings. This study investigates the crystal structure and electronic properties of γ-Al2O3 coatings under the influence of an external electric field, integrating machine learning with density functional theory (DFT). A potential 160-atom supercell structure was identified from over 600,000 γ-Al2O3 configurations and confirmed through high-resolution transmission electron microscopy and selected area electron diffraction. The findings indicate that γ-Al2O3 deviates from the conventional spinel structure, suggesting that octahedral vacancies can reduce the system's energy. Under an external electric field, the material's band structure and density of states (DOS) undergo significant changes: the bandgap narrows from 3.996 to 0 eV, resulting in metallic behavior, while the projected density of states (PDOS) exhibits peak broadening and splitting of oxygen atom PDOS below the Fermi level. These alterations elucidate the variations in the electrical conductivity of alumina coatings under an electric field. These findings clarify the mechanisms of γ-Al2O3's electronic property modulation and offer insights into its covalent and ionic mixed bonding as a wide-bandgap semiconductor. This discovery is essential for understanding dielectric breakdown in insulating materials.