COURSE UNIT TITLE

: COMPUTATIONAL NANOSCIENCE - II

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
NNE 5052 COMPUTATIONAL NANOSCIENCE - II ELECTIVE 3 0 0 7

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ÜMIT AKINCI

Offered to

Nanoscience and Nanoengineering
Nanoscience and Nanoengineering
Nanoscience and Nanoengineering

Course Objective

The aim of the course is to introduce students to the most commonly used methods in the theoretical examination of systems covered by nanoscience and nanotechnology.

In order to realize the desired nanosystems, mechanical, optical, electronic, magnetic properties should be determined theoretically. Numerical methods and simulation methods serve this purpose is one of the most powerful tools.

Upon successful completion of the course, students will learn the basics of the molecular dynamic and Monte Carlo simulations, with these methods they will have knowledge about various nanotechnological applications. Students who successfully complete the course will be able to determine the various properties of nanomaterials and learn how to use the molecular dynamic and Monte Carlo simulations.

Learning Outcomes of the Course Unit

1   To have a general idea about computational methods in nanoscience
2   To have an idea about the need for MD and MC methods and to learn the basics of these methods
3   To be able to apply MD simulations to simple systems
4   To introduce the use of MD simulations in nanotechnology
5   To be able to apply MC simulations to simple systems
6   To learn the use of MC simulations in nanotechnology

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction: The place and historical development of molecular dynamics (MD) and Monte Carlo (MC) methods in computer simulations
2 Fundamentals of MD: Equations of motion, interaction and potential functions, integration methods
3 Basic applications of MD: simple molecule simulations
4 Basic applications of MD: simulation of gas systems
5 MD simulation of nanoclusters
6 MD simulation of nanotubes
7 Determination of mechanical properties of nanosystems by MD
8 Determination of thermal properties of nanosystems by MD
9 Midterm 1
10 Introduction to MC simulation: Random processes, random numbers, probability distributions
11 Device simulations
12 Determination of magnetic properties of nanoparticles
13 Presentations of projects
14 Presentations of projects

Recomended or Required Reading

Textbook(s): :
D. C. Rapaport, the art of molecular dynamıcs sımulatıon, cambridge university press, 2004

Supplementary Book(s):
Lichang Wang, Molecular Dynamıcs -Theoretıcal Developments And Applıcatıons In Nanotechnology And Energy, Intech Press, 2012

Planned Learning Activities and Teaching Methods

Lecture, problem solving, project realization

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 PRJ PROJECT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE *0.25 + PRJ *0.45 +FIN *0.30
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE *0.25 +PRJ *0.45 +RST *0.30


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

umit.akinci@deu.edu.tr/ Tınaztepe kampüsü öğretim üyeleri binası /223

Office Hours

Wednesday 13.00-15.00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 9 3 27
Tutorials 2 3 6
Preparations before/after weekly lectures 11 4 44
Preparation for midterm exam 1 20 20
Preparation for final exam 1 20 20
Preparing presentations 1 50 50
Midterm 1 3 3
Final 1 3 3
TOTAL WORKLOAD (hours) 173

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.153454444144534
LO.254442412122354
LO.354342212113344
LO.453355311112343
LO.553355311112343
LO.653354444124535