Amorphous alumina (a-AlOx), which plays important roles in several technological fields, shows a wide variation of density and composition. However, their influences on the properties of a-AlOx have rarely been investigated from a theoretical perspective. In this study, high-dimensional neural network potentials were constructed to generate a series of atomic structures of a-AlOx with different densities (2.6 g/cm3–3.3 g/cm3) and O/Al ratios (1.0–1.75). The structural, vibrational, mechanical, and thermal properties of the a-AlOx models were investigated, as well as the Li and Cu diffusion behavior in the models. The results showed that density and composition had different degrees of effects on the different properties. The structural and vibrational properties were strongly affected by composition, whereas the mechanical properties were mainly determined by density. The thermal conductivity was affected by both the density and composition of a-AlOx. However, the effects on the Li and Cu diffusion behavior were relatively unclear.
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