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2024　Fundamentals of Computational Materials Science

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Academic unit or major
Undergraduate major in Materials Science and Engineering
Instructor(s)
Yamaguchi Akira  Kamiya Toshio  Hoshina Takuya     
Class Format
Lecture     
Media-enhanced courses
Day/Period(Room No.)
-
Group
-
Course number
MAT.C207
Credits
2
Academic year
2024
Offered quarter
4Q
Syllabus updated
2024/3/14
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

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, which is widely used in materials science, by making simple programs. Latest research topics of computational science-promoted material design are also introduced.

Student learning outcomes

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.
• Understand the importance of computational science for materials design.

Keywords

Quantum chemistry calculation, chemical bond, machine learning, regression analysis

Competencies that will be developed

✔ 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/Required learning

  Course schedule Required learning
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 Introduction to python fundamentals of computer and python
Class 10 Practice for programming ・How to read data from text and Excel files ・How to perform differentiation and integration by computer ・How to draw and save graphs
Class 11 Linear least square method and nonlinear optimization ・Create a program that reads data and analyzes it using the univariate linear least squares method. ・Create a program that reads the data and analyzes it using the bivariate linear least squares method. ・Load the data and create a curve fitting program using the nonlinear least squares method
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

Out-of-Class Study Time (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.

Reference books, course materials, etc.

Unspecified

Assessment criteria and methods

Achievement is evaluated by the percentage of exercises.

Related courses

  • MAT.C302 ： Spectroscopy
  • MAT.A203 ： Quantum Mechanics of Materials

Prerequisites (i.e., required knowledge, skills, courses, etc.)

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.

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