Seminar & Colloquium
[세미나: 3월 30일(수), 오전 9시 30분] Brookhaven National Lab, 최상국 박사
TITLE
Toward correlated quantum materials design: ab initio methodology development and AI-based algorithm acceleration
SPEAKER
Brookhaven National Lab, 최상국 박사
EDUCATION
- Jun. 2004 B.S. Materials Science & Engineering, Seoul National University, Seoul, South Korea
- Jun. 2006 M.S. Materials Science & Engineering, Seoul National University, Seoul, South Korea
- Dec. 2013 Ph.D. Physics, University of California at Berkeley, CA, USA
PROFESSIONAL EXPERIENCES
- Oct. 2019~Present Associate Computational Scientist, Brookhaven National Lab, Upton, NY, USA
- Jan. 2017~Sep. 2019 Assistant Computational Scientist, Brookhaven National Lab, Upton, NY, USA
- Jan. 2016~Dec. 2016 Research Associates, Brookhaven National Lab, Upton, NY, USA, Advisers: Prof. Gabriel Kotliar
- Jan. 2014~ Dec. 2015 Postdoctoral Associates, Department of Physics and Astronomy, Rutgers University, Piscataway, NJ, USA, Advisers: Prof. Gabriel Kotliar and Prof. Kristjan Haule
Thesis: First-Principles Calculations and Model Hamiltonian Approaches to Electronic and Optical Properties of Defects, Interfaces and Nanostructures
Adviser: Prof. Steven G. Louie
| Date | Wednesday, March 30th, 2022
| Time | 09:30 ~
| Venue | 온라인 강의 (https://snu-ac-kr.zoom.us/j/95675187812)
회의 ID: 956 7518 7812
[Abstract]
Quantum information science is a surging frontier of physical science. By creating quantum states and utilizing them as quantum bits (qubits), it promises vastly improved performance over what we have achieved during the 20th century.
Quantum materials are a class of materials of which properties can be explained by only quantum physics. When their quantum nature is due to electron-electron interaction, quantum materials give rise to a rich tableau of novel physics. These so-called correlated quantum materials can be utilized as “semiconductors” for quantum information science.
However, understanding correlated quantum materials properties is one of the grand challenges in the field of quantum materials. Correlated quantum materials preclude simple explanations and computationally simple methods based on Landau’s Fermi liquid theory, such as density functional theory.
In this talk, I'll introduce the first principles+DMFT approaches, especially LQSGW+DMFT[1,2]. I will also show several interesting physics found in correlated quantum material including infinite-layer nickelate [3,4]. I'll also present how reinforcement machine learning can accelerate the ab initio methodology. Lastly, I'll discuss the potential of the first principles+DMFT approaches for data-driven topological qubit materials design.
[1] S. Choi+, P. Semon, B. Kang, A. Kutepov, and G. Kotliar, Comp. Phys. Comm. 244, 277 (2019); 10.1016/j.cpc.2019.07.003
[2] S. Choi, A. Kutepov, K. Haule, M. van Schilfgaarde, and G. Kotliar+, npj Quantum Materials 1, 16001 (2016); 10.1038/npjquantmats.2016.1
[3] S. Ryee, P. Semon, M. J. Han+, and S. Choi+ , Phys. Rev. Lett. 126, 206401 (2021);
[4] B. Kang, C. Melnick, P. Semon, S. Ryee, M. J. Han, G. Kotliar, and S. Choi†, arXiv:2007.14610
| Host | Prof. In-Suk Choi (880-1712)