高分子模擬實作
Polymer Modeling
| 節 | 週二 |
|---|---|
7 15:30–16:20 | 高分子模擬實作 CS105（光復） 3 節連堂 |
8 16:30–17:20 | |
9 17:30–18:20 |
* 根據陽明交大上課時間表所列
本課程專為化學系研究生設計，介紹高分子模擬的核心方法與實作技巧。課程由統計力學基礎出發，依序建立蒙地卡羅（Monte Carlo）與分子動力學（Molecular Dynamics）的理論架構，並透過 Python 與 Gromacs 的實作練習，讓學生具備獨立執行、分析高分子模擬的能力。課程後半段聚焦高分子聚合反應的動力學預測，銜接理論方法與實際高分子化學問題。 This course is designed for graduate students in chemistry and introduces the core methods and practical techniques of polymer simulation. Starting from the foundations of statistical mechanics, the course builds up the theoretical framework of Monte Carlo (MC) and molecular dynamics (MD) methods, and through hands-on practice with Python and Gromacs, equips students with the ability to independently run and analyze polymer simulations. The latter half of the course focuses on predicting the kinetics of polymerization reactions, connecting theoretical methods to real polymer chemistry problems.
建議修過大學部物理化學或統計熱力學相關課程；具備基礎程式設計概念者尤佳。課程第 3–4 週會複習 Python 基礎，無先備 Python 經驗亦可修習。 Completion of an undergraduate physical chemistry or statistical thermodynamics course is recommended. Prior programming experience is helpful but not required — Weeks 3–4 provide a Python refresher for students without a programming background.
無備註
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期中考 (Midterm Exam) 20% 期末專題 (Final Project) 80%
教師未提供此項資料
| 週次 | 主題 |
|---|---|
| 第 1 週 | 導論與統計力學(Introduction & Statistical Mechanics) 為什麼要模擬高分子？多尺度模擬全景圖；Boltzmann 分布 Why simulate polymers? Multiscale simulation landscape; Boltzmann distribution |
| 第 2 週 | Monte Carlo Metropolis 演算法與 detailed balance Metropolis algorithm and detailed balance |
| 第 3 週 | Basic Python I 語法、資料結構、numpy 基礎 Syntax, data structures, numpy basics |
| 第 4 週 | Basic Python II 實作簡單 MC 模擬，呼應第 2 週內容 Implementing a simple MC simulation, tying back to Week 2 |
| 第 5 週 | MC-Ensemble 系綜理論在 Monte Carlo 模擬中的應用 Ensemble theory applied to MC simulation |
| 第 6 週 | MD-Verlet Verlet 演算法 Verlet algorithm |
| 第 7 週 | MD-Ensemble Nosé-Hoover chain 與 Parrinello-Rahman barostat Nosé-Hoover chain and Parrinello-Rahman barostat |
| 第 8 週 | MD-analysis 擴散係數與自由能計算 Calculation of diffusion constant and free energy |
| 第 9 週 | MC for Polymers CBMC、Rosenbluth 抽樣；迴轉半徑、持久長度、端對端距離 CBMC, Rosenbluth sampling; radius of gyration, persistence length, end-to-end distance |
| 第 10 週 | Midterm Exam |
| 第 11 週 | Gromacs-submit 力場選擇與參數化（含粗粒化簡介）；準備送出 Gromacs 模擬工作所需檔案 Force field selection/parameterization (incl. intro to coarse-graining); preparing files to submit a Gromacs job |
| 第 12 週 | Gromacs-analysis 從上週模擬中萃取關心的參數 Extracting parameters of interest from last week's simulation |
| 第 13 週 | 高分子聚合導論(Introduction to Polymerization) 基礎聚合機制與化學，呼應第一週的動機 Basic polymerization mechanisms and chemistry, echoing the Week 1 motivation |
| 第 14 週 | ODE 用常微分方程預測反應 Predicting reactions using ordinary differential equations |
| 第 15 週 | Kinetic Monte Carlo 用動力學蒙地卡羅預測反應 Predicting reactions using kinetic Monte Carlo |
| 第 16 週 | 期末專題(Final Project) 期末報告與展示 Final presentation and submission |
Understanding Molecular Simulation, Daan Frenkel and Berend Smit, 2002, Elsevier Inc. Polymer Physics, Michael Rubinstein and Ralph H. Colby, 2003, Oxford Unviersity Press
- 地點
- EO 713
- 時間
- Tue 10:00-12:00
- 聯絡方式
- fswang@nycu.edu.tw