Wenwen Li^1*, Yasunodu Ando^1*, Satoshi Watanabe^2,3*

1 Research Center for Computational Design of Advanced Functional Materials, National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki 305-8568, Japan

2 Department of Materials Engineering, The University of Tokyo, Bunkyo, Tokyo 113-8656, Japan

3 Center for Materials Research by Information Integration, Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Tsukuba, Ibaraki 305-0047, Japan

* Corresponding authors emails: wenwenli@preferred.jp, yasunobu.ando@aist.go.jp, watanabe@cello.t.u-tokyo.ac.jp

DOI10.24435/materialscloud:y1-zd [version v1]

Publication date: Aug 03, 2020

How to cite this record

Wenwen Li, Yasunodu Ando, Satoshi Watanabe, Effects of the density and composition on the properties of amorphous alumina: a high dimensional neural network potential study, Materials Cloud Archive 2020.89 (2020), https://doi.org/10.24435/materialscloud:y1-zd

Description

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.

Materials Cloud sections using this data

Files

File name	Size	Description
train.xyzdat.tar.gz MD5md5:a915abb5f8e33888414091d8be060a73	45.2 MiB	The DFT (VASP) calculation results of reference structures that were used for the training of neural network potential.
additional.xyzdat.tar.gz MD5md5:f22892aec7e63c302229ef4cd75c26ab	132.0 MiB	Additional reference DFT calculations.

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External references

Keywords

amorphous aluminum oxide neural network potential