[기계시스템학부] Dr. Jongmin Seo from Stanford University, Design thinking : Creative confident and Radical Collaboration
- 전체 학생
- 전체 학년/전체 성별
- 전체 학과
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학습 전공능력연계
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mechasys@sookmyung.ac.kr
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02-2077-7862
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온라인(ZOOM)
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핵심역량 지수
나의 역량 지수
세부내용
Invited Seminar
Department of Mechanical Systems Engineering
Design Thinking:
Creative Confidence and Radical Collaboration
날짜 2020년12월15일 화요일
시각 12시 -13시
장소 Zoom (Meeting ID: 857 2684 1207)
신청 Snoway 비교과 프로그램 (마일리지 1000점)
대상 공개강의 (타과생 환영합니다)
연사 서종민 박사
약력 Present Engineering Research Associate in Mechanical Engineering, Stanford University
2019 Postdoc in Pediatrics, Stanford University
2016 PhD in Mechanical Engineering, Stanford University
2012 MS in Mechanical Engineering, Stanford University
2010 BS in Mechanical Engineering, Seoul National University
Using the generalized Langevin equation (GLE) is a promising approach to build coarse-grained (CG) models of molecular systems since the GLE model often leads to more accurate thermodynamic and kinetic predictions than Brownian dynamics or Langevin models by including a more sophisticated friction with memory. The GLE approach has been used for CG coordinates such as the center of mass of a group of atoms with pairwise decomposition and for a single CG coordinate. We present a GLE approach when CG coordinates are multiple generalized coordinates, defined, in general, as nonlinear functions of microscopic atomic coordinates. The CG model for multiple generalized coordinates is described by the multidimensional GLE from the Mori-Zwanzig formalism, which includes an exact memory matrix. We first present a method to compute the memory matrix in a multidimensional GLE using trajectories of a full system. Then, in order to reduce the computational cost of computing the multidimensional friction with memory, we introduce a method that maps the GLE to an extended Markovian system. In addition, we study the effect of using a non-constant mass matrix in the CG model. In particular, we include mass-dependent terms in the mean force. We used the proposed CG model to describe the conformational motion of a solvated alanine dipeptide system, with two dihedral angles as the CG coordinates. We showed that the CG model can accurately reproduce two important kinetic quantities: the velocity autocorrelation function and the distribution of first passage times.
