민승규 교수 사진

UNIST화학과

들뜬상태 현상 이론/전산화학 연구실

민승규 교수 (Seung Kyu Min)

연구실 소개

이 연구실은 광-물질 상호작용에 따른 전하·엑시톤·폴라론 전달과 복사·비복사 붕괴 등 분자 과정을 연구합니다. 전자와 원자핵의 상관관계를 다루기 위해 정확한 인수분해에 기반한 혼합 양자-고전 동역학 방법을 개발하고, 광화학 반응의 들뜬상태 분자동역학을 연구합니다. 또한 들뜬상태 포텐셜 에너지 표면 계산을 위한 반경험적 및 머신러닝 양자화학 방법을 개발합니다.

AI가 연구실 홈페이지를 바탕으로 생성한 연구실 소개예요

연구실 홈페이지의 소개

To understand various interesting phenomena such as charge transfer, exciton transfer, polaron transfer, and (non)radiative decays in photovoltaics, photochemistry, polaron chemistry, and photocatalysts, it is extremely crucial to understand molecular processes with light-matter interactions. The most accurate description for these processes would be the fully quantum mechanical dynamics by solving the time-dependent Schrodinger equation or the quantum Liouville equation, which is possible only for the small degrees of freedom. One of the most promising approaches is the so-called mixed quantum-classical dynamics which deals with the electronic and nuclear degrees of freedom based on quantum mechanics and classical mechanics, respectively. In this case, the most crucial part is to understand the correlation between electrons and nuclei. My group investigates and develops various mixed quantum-classical approaches based on the exact factorization which can deal with the exact electron-nuclear correlation. One of the codes developed from my group is "pyUNIxMD" which can perform excited state molecular dynamics simulations based on various mixed quantum-classical algorithm. "pyUNIxMD" is under the active development. Various novel approaches to describe photochemical reactions in molecular systems and extended systems are soon to be updated. One of the most important to simulate excited state molecular dynamics is the efficient and accurate quantum chemical calculations for electronic excited states. Since the quantum chemical calculation is the most time-consuming part, the development of efficient quantum chemical methods is really demanding. My research group tries to develop semi-empirical approaches and machine learning approaches to calculate potential energy surface

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