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Syllabus ( NANO 621 )

   Basic information
Course title: Computational Methods in Nanoscience
Course code: NANO 621
Lecturer: Assoc. Prof. Dr. Sadiye VELİOĞLU
ECTS credits: 7.5
GTU credits: 3 (3+0+0)
Year, Semester: 1, Fall
Level of course: Second Cycle (Master's)
Type of course: Area Elective
Language of instruction: English
Mode of delivery: Face to face , Lab work
Pre- and co-requisites: none
Professional practice: No
Purpose of the course: The aim of this course is to make students comprehend the different computer methods used in nanoscience by using various computer programs and to design different systems in order to apply each method. Additionally, it is aimed to reveal the current rapid developments in this field and to examine the contributions of computational methods to various application areas.
   Learning outcomes Up

Upon successful completion of this course, students will be able to:

  1. Students who take this course will be able to model macroscopic systems on the nanoscale.

    Contribution to Program Outcomes

    1. To gain in-depth knowledge and experience about basic concepts and methods in nanoscience and nanotechnology.
    2. SKILLS
    3. Cognitive, Practical
    4. To follow the scientific publications in the field of nanotechnology and have an idea about the researches
    5. Acquire scientific knowledge.
    6. Develop an awareness of continuous learning in relation with modern technology
    7. To understand the basic principles and applications of new tools and / or software required for thesis work.
    8. Present and defence the research outcomes at seminars and conferences

    Method of assessment

    1. Written exam
    2. Homework assignment
    3. Laboratory exercise/exam
  2. Students who take this course will be able to assess the strengths and weaknesses of different types of computational methods based on application areas.

    Contribution to Program Outcomes

    1. To gain in-depth knowledge and experience about basic concepts and methods in nanoscience and nanotechnology.
    2. SKILLS
    3. Cognitive, Practical
    4. To follow the scientific publications in the field of nanotechnology and have an idea about the researches
    5. Acquire scientific knowledge.
    6. Develop an awareness of continuous learning in relation with modern technology
    7. To understand the basic principles and applications of new tools and / or software required for thesis work.
    8. Present and defence the research outcomes at seminars and conferences

    Method of assessment

    1. Written exam
    2. Homework assignment
    3. Laboratory exercise/exam
    4. Seminar/presentation
    5. Term paper
  3. Students who take this course will be able to compare the several properties of nanomaterials expressed via statistical thermodynamics with the experimental observables.

    Contribution to Program Outcomes

    1. To gain in-depth knowledge and experience about basic concepts and methods in nanoscience and nanotechnology.
    2. To manage nanotechnology-focused solutions and products commercialization processes.
    3. Acquire scientific knowledge.

    Method of assessment

    1. Homework assignment
    2. Laboratory exercise/exam
    3. Seminar/presentation
    4. Term paper
   Contents Up
Week 1: Introduction: Organization of Class.
Historical Perspective, "Computer Experiments"
Week 2: Elementary to classical statistical mechanics.
Common Statistical Ensembles.
Week 3: Molecular interactions and Force Fields
Week 4: Energy minimization methods.
Term Project Assignment
Week 5: Introduction to Molecular Dynamics.
Introduction to LAMMPS software.
Week 6: Implementation of Molecular Dynamics, Practice Course
Homework Assignment #1
Week 7: Non-equilibrium molecular dynamics (NEMD)
Week 8: Midterm Exam!!!
Week 9: Introduction to Monte Carlo. (Grand Canonical and Gibbs ensembles)
Introduction to Material Studio software.
Week 10: Examination of simulation studies published in the literature.
Week 11: Implementation of Monte Carlo, Practice Course
Homework Assignment #2
Week 12: Rare events in molecular simulation approaches.
Week 13: Rosenbluth sampling and configurational bias methods.
Week 14: Presentation of the term projects!!!
Week 15*: Overall Assessment
Week 16*: Final Exam!!!
Textbooks and materials: D. Frenkel and B. Smit, Understanding Molecular Simulation: From Algorithms to Applications, 2nd Ed., Academic Pres, 2002.
Recommended readings: 1. M. Allen and D. Tildesley, Computer Simulation of Liquids, Oxford University Press, 1989.
2. A. Satoh, Introduction to Practice of Molecular Simulation, Elsevier Press, 2011.
3. M. J. Field, A Practical Introduction to the Simulation of Molecular Systems, Cambridge University Press, 2007.
  * Between 15th and 16th weeks is there a free week for students to prepare for final exam.
Assessment Up
Method of assessment Week number Weight (%)
Mid-terms: 8 20
Other in-term studies: 0
Project: 14 20
Homework: 6, 8 20
Quiz: 0
Final exam: 16 40
  Total weight:
(%)
   Workload Up
Activity Duration (Hours per week) Total number of weeks Total hours in term
Courses (Face-to-face teaching): 3 14
Own studies outside class: 2 14
Practice, Recitation: 0 0
Homework: 4 4
Term project: 4 10
Term project presentation: 6 2
Quiz: 0 0
Own study for mid-term exam: 10 2
Mid-term: 2 1
Personal studies for final exam: 10 2
Final exam: 2 1
    Total workload:
    Total ECTS credits:
*
  * ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
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