2024 Faculty Courses School of Science Department of Physics Graduate major in Physics
Special Topics in Physics XI
- Academic unit or major
- Graduate major in Physics
- Instructor(s)
- Takashi Miyake
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - Intensive
- Class
- -
- Course Code
- PHY.P587
- Number of credits
- 100
- Course offered
- 2024
- Offered quarter
- 2Q
- Syllabus updated
- Mar 14, 2025
- Language
- Japanese
Syllabus
Course overview and goals
First-principles calculation is a powerful tool for materials research. Topics of this course include (1) the basic theory of first-principles calculation, (2) understanding of material properties based on first-principles electronic structure calculation, and (3) materials informatics.
Students will learn how to understand and predict properties of real materials.
Course description and aims
By the end of this course, students will be able to explain (i) basic theory of first-principles calculation, and (ii) electronic structures of several real materials, such as permanent magnet.
Keywords
computational physics, machine learning, electron correlation, magnetism
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
The contents will be explained through lectures.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | First-principles calculation | Understand density functional theory. |
| Class 2 | Band structure of materials | Understand electronic structures of several materials. |
| Class 3 | Electronic states of correlated materials | Derivation of first-principles low-energy model and its applications |
| Class 4 | Permanent magnet | Understand rare-earth magnets based on electronic states. |
| Class 5 | Computational materials design | Crystal structure prediction, materials exploration by high-throughput calculation |
| Class 6 | Machine learning | Understand basic techniques of AI (regression, classification, clustering). |
| Class 7 | Materials informatics | Data-driven materials research |
Study advice (preparation and review)
Textbook(s)
None
Reference books, course materials, etc.
Course materials are provided during class.
Evaluation methods and criteria
Students’ course scores are based on attendance and assignments.
Related courses
- PHY.C340 ： Basic Solid State Physics
- PHY.C341 ： Condensed Matter Physics I
- PHY.C342 ： Condensed Matter Physics II
Prerequisites
Knowledge of basic solid state physics.