Effects of the density and composition on the properties of amorphous alumina: a high dimensional neural network potential study

Amorphous alumina (a-AlOx), which plays important roles in several technological fields, shows wide variation of the 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 (NN) potentials were constructed to generate a series of atomic structures of a-AlOx with different densities (2.6–3.3 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 behaviour in the models. The results showed that the density and composition had different degrees of effects on the different properties. The structural and vibrational properties were strongly affected by the composition, whereas the mechanical properties were mainly determined by the density. The thermal conductivity was affected by both the density and composition of a-AlOx. However, the effects on the Li and Cu diffusion behaviour were relatively unclear.

非晶氧化铝（a-AlOx）在诸多技术领域中具有关键应用价值，但其密度与组分存在显著差异。然而，此前鲜有从理论层面探究二者对非晶氧化铝性能的影响机制。本研究构建了高维神经网络势（NN势），以此生成一系列具备不同密度（2.6~3.3 g/cm³）与O/Al原子比（1.0~1.75）的非晶氧化铝原子结构模型。随后，本研究对该系列模型的结构、振动、力学与热学性能，以及模型内锂（Li）和铜（Cu）的扩散行为开展了系统探究。结果显示，密度与组分对不同性能的影响程度各不相同：结构与振动性能主要受组分调控，力学性能则主要由密度决定，而非晶氧化铝的热导率同时受密度与组分的共同影响。但二者对锂、铜扩散行为的影响仍相对不明晰。