Computational Materials Science

Course Code:

MSN 304

Course Period:

Spring

Course Type:

Core

Credits:

3

Theoric:

3

Practice:

0

Laboratory Hour:

0

ECTS:

6

Prerequisite Courses:

Linear Algebra

Course Language:

English

Course Coordinator:

Taner Akbay

Courses given by:

Taner Akbay

Course Objectives:

In this course, students will be introduced to modern computational methods used in materials modelling and simulation in various length and time scales ranging from atomic to continuum level.

Course Content:

Introductıon to numerical methods for data analyses and visualisation as well as solving linear/nonlinear algebraic/differential equations using Python as a scripting language will be covered. Hands on lessons of basics of Python programming together with specific packages developed for implementation of discretisation techniques (e.g. finite differences) for numerically solving (partial-) differential equations arising from heat conduction and mass diffusion in engineering materials. Examples of atomistic and molecular simulation techniques to become familiar with analyses for determining structure property relationship of materials in different length scales.

Course Methodology:

1: Lecture, 2: Question-Answer, 3: Discussion, 9: Simulation, 12: Case Study

Course Evaluation Methods:

A: Written exam, B: Multiple-choice exam C: Take-home quiz, D: Experiment report, E: Homework, F: Project, G: Presentation by student, H: …

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Course Learning Outcomes

Learning Outcomes	Programme Learning Outcomes	Teaching Methods	Assessment Methods
1) Gains basic understanding of the numerical methods used in engineering applications.	1a, 1b, 2a, 2b	1, 2, 3	A, E
2) Develops capacity for independent integration and application of computational methods in materials modelling and simulation in order to effectively analyse structure property relationship in engineering materials.	1a, 1b, 2a, 2b, 4a	1, 2, 3	A, E
3) Learns how to program in Python scripting language	2a, 2b, 4a	1, 2, 3	A, E

Course Flow

COURSE CONTENT
Week	Topics	Study Materials
1	Introduction to Materials Modelling – The Interdisciplinary Glue	Lecture notes
2	Variables, Statements, Functions in Python	Lecture notes
3	Data Processing and Visualisation	Lecture notes
4	Regression Analysis (The Least Squares Method)	Lecture notes
5	Solving Linear and Nonlinear Equations	Lecture notes
6	Solving Ordinary Differential Equations	Lecture notes
7	Midterm I	-
8	Solving Partial Differential Equations	Lecture notes
9	Solving Heat Transport Equations in Solids	Lecture notes
10	Solving Mass Transport Equations in Solids	Lecture notes
11	Midterm II	-
12	Atomistic Simulation Methods in Materials Modelling	Lecture notes
13	Classical Force Field Methods in Materials Modelling	Lecture notes
14	Multi-scale Techniques for Computational Materials Modelling	Lecture notes
15	Wrap-up	-

Recommended Sources

RECOMMENDED SOURCES

Textbook

Computational Materials Science – An Introduction, J. G. Lee

Additional

Resources

Python Programming for Engineers and Scientists, I. Turk, C. I. Emre

Material Sharing

MATERIAL SHARING
Documents	Shareable
Assignments	Shareable
Exams	Not shareable

Assessment

ASSESSMENT
IN-TERM STUDIES	NUMBER	PERCENTAGE
Homework	2	10
Midterm Exam	2	20
Final Exam	1	40
Total		100
CONTRIBUTION OF FINAL EXAMINATION TO OVERALL GRADE		40
CONTRIBUTION OF IN-TERM STUDIES TO OVERALL GRADE		60
Total		100

Course’s Contribution to Program

COURSE'S CONTRIBUTION TO PROGRAMME
No	Program Learning Outcomes	Contribution
No	Program Learning Outcomes	1	2	3	4	5
1a	Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline,					X
1b	Ability to use theoretical and applied knowledge in these areas in complex engineering problems.					X
2a	Ability to identify, formulate, and solve complex engineering problems,					X
2b	Ability to select and apply proper analysis and modeling methods for this purpose.					X
3a	Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result,
3b	Ability to apply modern design methods for this purpose.
4a	Ability to devise, select and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice.					X
4b	Ability to employ information technologies effectively.
5a	Ability to design experiments for investigating complex engineering problems or discipline specific research questions,
5b	Ability to conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions.
6a	Ability to work efficiently in intra-disciplinary teams,
6b	Ability to work efficiently in multi-disciplinary teams,
6c	Ability to work individually.
7a	Ability to communicate effectively in Turkish, both orally and in writing,
7b	Knowledge of a minimum of one foreign language,
7c	Ability to write effective reports and comprehend written reports, prepare design and production reports,
7d	Ability to make effective presentations,
7e	Ability to give and receive clear and intelligible instructions.
8a	Recognition of the need for lifelong learning, ability to access information, ability to follow developments in science and technology,
8b	Ability to continue to educate him/herself.
9a	Consciousness to behave according to ethical principles and professional and ethical responsibility.
9b	Knowledge on standards used in engineering practice.
10a	Knowledge about business life practices such as project management, risk management, change management.
10b	Awareness in entrepreneurship and innovation.
10c	Knowledge about sustainable development.
11a	Knowledge about the global and social effects of engineering practices on health, environment, and safety,
11b	Knowledge about contemporary issues of the century reflected into the field of engineering.
11c	Awareness of the legal consequences of engineering solutions.

ECTS

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Course Duration (Excluding exam weeks: 13xTotal course hours/week)	13	3	39
Hours for off-the-classroom study (Pre-study, practice, review/week)	13	6	78
Hours for off-the-classroom study (Midterm exam/week)	2	7	14
Midterm Exam	2	1.5	3
Hours for off-the-classroom study (Final exam/week)	1	10	10
Final Exam	1	2	2
Total Work Load			146
Total Work Load / 25 (h)			5.84
ECTS Credit of the Course			6

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