Kyung Hee University
Investigating restricted symmetry systems, chiral magnetism, and AI-driven materials science.
We study the magnetism and magnetic dynamics in restricted symmetry systems. Restricted symmetry systems include spatially confined systems (nanostructures), low-dimensional systems with limited translational symmetry, and systems without inversion symmetry featuring asymmetric exchange interaction (Dzyaloshinskii-Moriya interaction).
Rich magnetic phenomena occur in these systems due to enhanced interactions such as high-order anisotropies and dipolar interactions. We investigate how these behaviors are affected by external fields, electric currents, and temperature, with applications in spintronics.
Our research methodology combines Monte Carlo methods, Langevin dynamics, and Landau-Lifshitz dynamics. Recently, we have integrated Deep Learning technology to advance magnetic materials research. We actively collaborate with the Physics Department at Fudan University (China) and the SPLEEM group at LBNL (USA).
| 회차 | 내용 | 비고 |
|---|---|---|
| 1-1 | 파이썬과 딥러닝을 위한 컴퓨터 환경설정 | |
| 1-2 | 파이썬 기초문법 | |
| 2-1 | 자성모델 파이썬으로 구현하기-1 (이징모델 이론) | |
| 2-2 | 자성모델 파이썬으로 구현하기-2 (몬테카를로 시뮬레이션) | |
| 3-1 | 딥러닝 기초 이론 | |
| 3-2 | 딥러닝 기초과정 실습 (심층신경망) | |
| 4-1 | 딥러닝 중급과정 실습-1 (오토인코더) | |
| 4-2 | 딥러닝 중급과정 실습-2 (변분 오토인코더) | |
| 5-1 | 직접 만든 데이터로 딥러닝 학습하기 | |
| 5-2 | 하이젠버그자성모델 전산모사 및 이론 (Antisymmetric exchange) | |
| 6-1 | 논문 따라하기 1 (Interpolation using Autoencoder) | |
| 6-2 | 논문 따라하기 2 (Indexing topological numbers) |
2013 - Present