JP
Lab Info

Dept. of Theoretical Nanotechnology
(Minamitani Lab.)

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# Computational Materials Science # Density functional theory # Topological Data Analysis # Machine Learning

In the Department of Theoretical Nanotechnology, we strive to understand the properties of materials at the nanoscale and employ computer simulations and physical theories to discover methods to enhance their properties. Additionally, we are also developing new simulation techniques using data science approaches, including machine learning. Recent research topics include the study of amorphous material properties using topological data analysis, the development of high-efficiency simulation methods using machine learning potentials, and theoretical research on spintronics. We also actively collaborate with other theoretical and experimental groups.

Staff

Emi Minamitani ｜Professor
Research Map
Degree：
Ph.D in Engineering, (2010, Osaka University)
Career：
September 2022 ~ Present: Professor, Nano-Technology Center for SANKEN, Osaka University
April 2019 ~ September 2022: Associate Professor, Institute for Molecular Science
September 2022 ~ March 2023: Professor (Concurrently), Institute for Molecular Science
October 2015 ~ March 2019: Lecturer, Department of Materials Engineering, Graduate School of Engineering, University of Tokyo
December 2013 ~ September 2015: Assistant Professor, Department of Materials Engineering, Graduate School of Engineering, University of Tokyo
April 2011 ~ December　2013: Special Postdoctoral Researcher, RIKEN
April 2010 ~ March 2011: JSPS (Japan Society for the Promotion of Science) Research Fellow, Graduate School of Engineering, Osaka University
Atsuo Shitade Associate Professor Research Map
Nguyen Thi Phuong Thao Assistant Professor Research Map
Kumpei SHIRAISHI Assistant Professor Research Map
Left) The structure of amorphous Si and the predicted thermal conductivity results using topological data analysis. Right) Points with a strong correlation to thermal conductivity and the corresponding nano-scale structure.
Persistent homology analysis of amorphous carbon and a machine learning model for energy prediction using convolutional neural networks with that as input.

Current Research Topics

  • Persistent homology-based descriptor for machine-learning potential of amorphous structures

E. Minamitani, I. Obayashi, K. Shimizu, S. Watanabe, J. Chem. Phys. 159, 084101 August 22 2023

 

  • Topological descriptor of thermal conductivity in amorphous Si

Emi Minamitani, Takuma Shiga, Makoto Kashiwagi, Ippei Obayashi, J. Chem. Phys. 156, 244502, June 23 2022

 

  • Ab initio analysis for the initial process of Joule heating in semiconductor

Emi Minamitani, Phys. Rev. B, Phys. Rev. B 104, 085202, 16 August 2021

 

  • Spin-orbital Yu-Shiba-Rusinov states in single Kondo molecular magnet, Hui-Nan Xia, Emi Minamitani, Rok Žitko, Zhen-Yu Liu, Xin Liao, Min Cai, Zi-Heng Ling, Wen-Hao Zhang, Svetlana Klyatskaya, Mario Ruben, Ying-Shuang Fu, Nat. Commun. 13, 6388, 27 October 2022
Lab Info

Dept. of Theoretical Nanotechnology
(Minamitani Lab.)

website