Computational Methods in Materials Science (NANO*3600)
Code and section: NANO*3600*01
Term: Fall 2016
Instructor: Alexandros Gezerlis
Monday, Wednesday, Friday
8:30 am – 9:20 am
CRSC 116 (later in the semester we’ll move to MacN 415)
11:30 pm – 2:20 pm
R. H. Landau, M. J. Paez, and C. C. Bordeianu, Computational Physics: Problem Solving with Python (3rd edition, Wiley, 2015)
- Note 1: I’ve made special arrangements with the publisher for us to use a custom volume (ISBN: 9783527807512) consisting of only selected chapters, at a considerable discount. This custom volume can be purchased at the University bookstore.
- Note 2: In response to my request to buy the book so it can be placed on reserve, the University of Guelph Library has purchased the ebook version, which you can access online through the library website.
- Mark Newman, Computational Physics (Rev. ed., CreateSpace, 2013)
- Morten Hjorth-Jensen, Computational physics online lecture notes
Online resources on Unix and Python
- Matt Might, Survival guide for Unix newbies
- Michael Stonebank, UNIX Tutorial for Beginners
- The Python Tutorial
- Allen B. Downey, Think Python
- Mark Newman, Python programming for physicists
This is a first course on what is known as computational science or scientific computing. We will focus on the interplay between science problem, mathematical formulation, and computational implementation. Previous exposure to programming is not required but will certainly be beneficial. We will cover the essentials of the following subjects:
- POSIX shell basics
- Programming in Python
- Errors and uncertainties
- Monte Carlo calculations
- Differentiation and integration
- Dealing with matrices
- Solving algebraic equations
- Data fitting
- Ordinary differential equations
- Molecular dynamics simulations
|Midterm exam (TBD)||20%|
|Final exam (December 10th, 8:30 am)||40%|
If the final exam mark is greater than that of the midterm, the midterm will be dropped and the final exam mark will be weighted as 60% of the final mark.
Since one learns programming by doing, the lab is an integral part of this course. Thus, 5% of the overall grade will be based on lab attendance and performance.
- Thursdays, 9:30 am—11:30 am, in my office (MacN 219)
- Feel free to approach me before and after class to discuss scientific computing
- Alternative arrangements can be made by appointment (firstname.lastname@example.org)
Course policy regarding grading
Each homework assignment should be handed in on the due date before the lecture begins. There will be a significant penalty for late assignments, unless special arrangements are made ahead of time.
Course policy regarding use of electronic devices and recording of lectures
Electronic recording of classes is expressly forbidden without consent of the instructor. When recordings are permitted they are solely for the use of the authorized student and may not be reproduced, or transmitted to others, without the express written consent of the instructor.
When you find yourself unable to meet an in-course requirement because of illness or compassionate reasons, please advise the course instructor in writing, with your name, id#, and e-mail contact.
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Please note: Whether or not a student intended to commit academic misconduct is not relevant for a finding of guilt. Hurried or careless submission of assignments does not excuse students from responsibility for verifying the academic integrity of their work before submitting it. Students who are in any doubt as to whether an action on their part could be construed as an academic offence should consult with a faculty member or faculty advisor.
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Course Evaluation Information
Please refer to the Course and Instructor Evaluation Website
The last date to drop one-semester courses, without academic penalty, is November 4, 2016. For regulations and procedures for Dropping Courses, see the Undergraduate Calendar.