2025 (Current Year) Faculty Courses School of Materials and Chemical Technology Undergraduate major in Materials Science and Engineering
Fundamentals of Computational Materials Science
- Academic unit or major
- Undergraduate major in Materials Science and Engineering
- Instructor(s)
- Akira Yamaguchi / Toshio Kamiya / Takuya Hoshina / /
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Tue (S7-201、情報ネットワーク演習室1,2) / 5-6 Fri (S7-201、情報ネットワーク演習室1,2)
- Class
- -
- Course Code
- MAT.C207
- Number of credits
- 200
- Course offered
- 2025
- Offered quarter
- 4Q
- Syllabus updated
- Mar 19, 2025
- Language
- Japanese
Syllabus
Course overview and goals
This course provides the fundamental knowledge on the computational material science. In the early stage, students learn how to calculate molecular structures and electronic structures by quantum chemistry program and understand the chemical bonding. In the middle stage, the fundamental principles of regression analyses and the differences among those methods are explained. To demonstrate the regression analysis, data-analyses software equipped with Graphical User Interface (GUI) is used for dataset of ceramic materials and their properties. In the final stage, the students experience programing language using python and generative AI, which are widely used in materials science, by making simple programs. Latest research topics of computational science-promoted material design are also introduced.
Course description and aims
This lecture aims to learn the following knowledge on the quantum chemistry and computational science for understanding the ceramics materials and their properties.
• Learn about the fundamentals of quantum calculation and practical procedures.
• Learn about the meaning of the electric structure from practical calculations on various molecules and their analyses.
• Understand the potential and usefulness of the quantum calculation.
• Learn about the fundamental principles of regression analyses and the differences among those methods.
• Learn about the role and importance of data processing and parameter setting.
• Learn about programing language and use of generative AI.
• Understand the importance of computational science for materials design.
Keywords
Quantum chemistry calculation, chemical bond, machine learning, regression analysis
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
The course is divided into 3 parts. In each lecture, some exercises are given, and the students carry out and submit them to the lecturer.
In the early stage, basic knowledge on the chemical bonds is provided. The students are requested to use quantum chemistry calculation software to calculate structure of molecules within lecture time.
In the middle stage, fundamentals on regression analyses are explained. The students use data-analyses software to demonstrate regression analyses for a dataset of ceramics material and their properties.
In the final stage, basic knowledge on generic programing language is provided.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Introduction to quantum chemistry calculation |
Schrodinger equation, atomic orbital, eigen value (energy), N-bodies problem, Hartree-Fock approximation, Linear combination of atomic orbital coefficient |
| Class 2 | Calculation of atom and molecule by ab-initio method |
molecular orbital, basic function, Slater function and Gauss function, hybridization, lone pair electrons |
| Class 3 | Coordination of atoms in materials: Geometry optimization |
geometry optimization, initial geometry, vibrational motion, vibration energy, vibration mode, infrared absorption, Raman scattering |
| Class 4 | Vibration mode, atomic charge, dipole moment |
Mulliken charge density, dipole moment |
| Class 5 | Comparison between calculation and observation |
orbital energy, work function, photoelectron spectroscopy |
| Class 6 | Regression analysis: linear regression |
liner regression, least squares method, covariance, coefficient of determination |
| Class 7 | Regression analysis: multi-variable linear regression |
multiple regression analysis, least absolute shrinkage and selection operator regression, Ridge regression |
| Class 8 | Regression analysis: non linear |
nonparametric regression, support vector regression |
| Class 9 | Experiencing Programming – Python and Generative AI |
Setting up the development environment |
| Class 10 | Programming Exercises I: Linear Least Squares Method |
Python syntax |
| Class 11 | Programming Exercises II: Nonlinear Least Squares Method and Matrix Calculations |
Python programming exercises: |
| Class 12 | Special lecture ① |
Lecture about recent research promoted by materials informatics① |
| Class 13 | Special lecture ② |
Lecture about recent research promoted by materials informatics② |
| Class 14 | Special lecture ③ |
Lecture about recent research promoted by materials informatics③ |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
The documents distributed in lecture.
The lecture materials including python programs are available from the following web.
http://d2mate.mdxes.iir.isct.ac.jp/D2MatE/D2MatE_programs.html?page=fcms
Reference books, course materials, etc.
Unspecified
Evaluation methods and criteria
Achievement is evaluated by the percentage of exercises.
Related courses
- MAT.C302 ： Spectroscopy
- MAT.A203 ： Quantum Mechanics of Materials
Prerequisites
The students are requested to have taken the lectures on quantum chemistry and experimental class for materials.
Other
The number of students is limited due to the capacity of device.