清华大学材料科学与工程研究院《材料科学论坛》学术报告：Accelerating Nanomaterial Simulations Using Machine Learning Techniques
报告题目：Accelerating Nanomaterial Simulations Using Machine Learning Techniques
Accelerating Nanomaterial Simulations Using
Machine Learning Techniques
Department of Materials Engineering, Graduate School of Engineering,
The University of Tokyo, Tokyo, Japan, and
Research and Services Division of Materials Data and Integrated System,
NIMS, Tsukuba, Japan.
Keywords: Neural Network, Interatomic potential, density functional theory,
ion diffusion, thermal transport
Progress in computer and methodologies enables reliable simulations on various topics concerning structures and properties of nanomaterials and phenomena occurring in nanomaterials using first-principles methods such as density functional theory (DFT). However, some of important topics still demands practically too heavy computations, such as ion diffusion in amorphous materials, thermal transport in defective materials, atomic structures of complex interfaces, etc.
Recently, machine learning techniques have attracted much attention because of their capability to achieve computational efficiency and reliability simultaneously in nanomaterial simulations. In this talk, I will demonstrate their capability with showing our recent results on the construction and applications of interatomic potentials optimized using DFT calculation data and one of machine-learning techniques, neural network. I will mainly discuss Li ion diffusion in amorphous Li3PO4  and thermal conductivities of wuitize GaN and silicon crystals , and will also touch on the Born effective charges , atomic structure of metal/solid-electrolyte interface , and atomic energy mapping .
The works of Refs.  to  were done in collaboration with some of present/previous members in my group, K. Shimizu, T. Moriya, M. Ogura, W. Liu, W. Li, Y. Ando, E. Minamitani, and that of Ref.  with Prof. S. Han and some of members in his group, D. Yoo, K. Lee, W. Jeong. The works were supported in part by MI2I project of the Support Program for Starting Up Innovation Hub from JST, CREST-JST, PRESTO-JST and JSPS KAKENHI, Japan.
 W. Li, et al.: J. Chem. Phys., 147, 214106 (2017).
 E. Minamitani, et al.: Appl. Phys. Express, 12, 095001 (2019).
 T. Moriya: Master thesis, The University of Tokyo (2019) [in Japanese].
 K. Shimizu, et al.: in preparation.
 D. Yoo, et al.: Phys. Rev. Mater., 3, 093802 (2019).