Cu Diffusion in Amorphous Ta2O5 Studied with a Simplified Neural Network Potential-Reference-Cited by-同舟云学术

Cu Diffusion in Amorphous Ta2O5 Studied with a Simplified Neural Network Potential

Published:2017-10-15 Issue:10 Volume:86 Page:104004
ISSN:0031-9015
Container-title:Journal of the Physical Society of Japan
language:en
Short-container-title:J. Phys. Soc. Jpn.

Author:

Li Wenwen¹,Ando Yasunobu²,Watanabe Satoshi¹³

Affiliation:

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

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

Publisher

Physical Society of Japan

Subject

General Physics and Astronomy

Link

https://journals.jps.jp/doi/pdf/10.7566/JPSJ.86.104004

Cited by 28 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Diffusion-facilitated transport of self-driven particles in polycrystalline structures;Physical Review Research;2024-08-08

2. Applications of machine‐learning interatomic potentials for modeling ceramics, glass, and electrolytes: A review;Journal of the American Ceramic Society;2024-06-09

3. Isolation of current–voltage characteristics for each layer of a two-layer dielectric using the example of Al–Al2O3–Ta2O5–Ni diodes with different tantalum oxide thicknesses;Journal of Materials Science: Materials in Electronics;2023-11

4. Deep neural network potential for simulating hydrogen blistering in tungsten;Physical Review Materials;2023-09-06

5. Accelerating Non-Empirical Structure Determination of Ziegler–Natta Catalysts with a High-Dimensional Neural Network Potential;The Journal of Physical Chemistry C;2023-06-09