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Introduction to Materials Chemistry

From chemical bonding to electronic structure, thermodynamics, and computational chemistry. A comprehensive guide to understanding materials from a chemical perspective and practicing with Python.

About This Series

The Introduction to Materials Chemistry series provides a systematic approach to learning materials science from a chemical perspective. Starting from the fundamentals of chemical bonding, progressing through molecular orbital theory, crystal field theory, and thermodynamics, it culminates in practical electronic structure calculations using Python.

A key feature of this series is the integration of theory and computation. After learning the fundamental theory in each chapter, you will gain practical understanding through Python-based calculation examples and exercises. You will master computational methods essential for modern materials chemistry research, including DFT (Density Functional Theory), molecular orbital calculations, and thermodynamic calculations.

The target audience ranges from undergraduate students to graduate students and researchers in materials science and chemistry. If you have basic chemistry knowledge and understand Python's basic syntax, you can fully utilize the content of this series.

Learning Contents

Learning Flow

flowchart TD A[Fundamentals of Chemical Bonding
Chapter 1] --> B[Molecular Orbital Theory
Chapter 2] B --> C[Crystal Field Theory
Chapter 3] B --> D[Thermodynamics and Phase Equilibria
Chapter 4] C --> E[Python Practice
Chapter 5] D --> E A1[Ionic Bonding
Covalent Bonding
Metallic Bonding] --> A A2[Intermolecular Forces
Hydrogen Bonding] --> A B1[LCAO Method
Hückel Theory] --> B B2[Band Theory
DOS] --> B C1[d-Orbital Splitting
CFT/LFT] --> C C2[Jahn-Teller Effect
Magnetism] --> C D1[Gibbs Free Energy
Chemical Potential] --> D D2[Phase Diagrams
CALPHAD] --> D E1[DFT Calculations
ASE+GPAW] --> E E2[pycalphad
Materials Project] --> E E3[Machine Learning
Workflows] --> E style A fill:#f093fb,stroke:#f5576c,stroke-width:3px,color:#fff style B fill:#f093fb,stroke:#f5576c,stroke-width:3px,color:#fff style C fill:#f093fb,stroke:#f5576c,stroke-width:3px,color:#fff style D fill:#f093fb,stroke:#f5576c,stroke-width:3px,color:#fff style E fill:#f5576c,stroke:#f093fb,stroke-width:3px,color:#fff

Frequently Asked Questions (FAQ)

What is the difference between materials chemistry and physical chemistry?

Materials chemistry is a discipline that focuses on the chemical properties and structure of materials. While physical chemistry deals with the physical principles of chemical phenomena, materials chemistry studies the chemical bonding, electronic structure, and thermodynamic behavior of solid materials (metals, ceramics, semiconductors, polymers, etc.) from a practical perspective. This series applies fundamental theories of physical chemistry in the context of materials science.

Do I need deep knowledge of quantum chemistry to learn DFT (Density Functional Theory)?

Practical use of DFT is possible if you understand basic quantum mechanics (wave functions, Schrödinger equation, concept of orbitals). Chapter 5 of this series briefly explains the theoretical background of DFT before proceeding to computational practice using ASE+GPAW. The emphasis is on being able to execute calculations and interpret results rather than complete theoretical understanding.

How are molecular orbital theory and band theory related?

Molecular orbital (MO) theory describes electronic states within molecules, while band theory describes electronic states in solids (infinitely periodic systems). Band theory can be understood as an extension of molecular orbital theory to periodic boundary conditions through tight-binding approximation or LCAO method. Chapter 2 clearly demonstrates this continuity and calculates the transition from H₂ molecular MO to band structure using Python.

What is the difference between Crystal Field Theory (CFT) and Ligand Field Theory (LFT)?

Crystal Field Theory (CFT) treats ligands as point charges and explains d-orbital splitting solely through electrostatic interactions. Ligand Field Theory (LFT) incorporates molecular orbital theory concepts into CFT and considers covalent bonding between metal and ligands. CFT is concise and educational, but LFT enables more accurate predictions. Chapter 3 compares both theories and clarifies their applicable ranges.

What is the CALPHAD method? Why is it important for materials design?

The CALPHAD (CALculation of PHAse Diagrams) method is a technique for calculating phase diagrams of multicomponent systems using thermodynamic databases. By combining experimental data with model parameters, it can predict phase equilibria in composition and temperature ranges where experiments are difficult. It is an indispensable tool for alloy design, process optimization, and new materials exploration. Chapter 4 teaches practical phase diagram calculations using pycalphad.

What are the benefits of using the Materials Project API?

Materials Project is one of the world's largest open databases providing DFT calculation data for over 150,000 inorganic materials. Using the API allows easy retrieval of data such as crystal structures, energies, band gaps, and elastic constants, which can be utilized for pre-experimental screening and as training data for machine learning. Chapter 5 practices materials exploration and machine learning applications using the API.

Can the types of chemical bonds (ionic, covalent, metallic) be clearly distinguished?

In actual materials, pure ionic, covalent, or metallic bonds are rare, and most exhibit mixed properties (continuum of ionicity and covalency). For example, ZnS is an ionic crystal with strong covalent character, and Si-Ge alloys are covalent metals. Chapter 1 teaches classification of bonding character based on electronegativity differences and Pauling's bonding theory, and quantifies bonding character using Python.

Is Hückel theory still useful today? How do you differentiate its use from DFT?

Hückel theory is very useful for qualitative understanding of π-conjugated molecular systems. With extremely low computational cost and intuitive physical interpretation, it is suitable for educational purposes and initial screening. On the other hand, DFT offers high quantitative accuracy and is applicable to complex systems. Chapter 2 compares Hückel theory and DFT using benzene and understands the advantages and disadvantages of each.

What kind of phenomenon is the Jahn-Teller effect?

The Jahn-Teller effect is a phenomenon where molecules or complexes with degenerate electronic states lower their symmetry through structural distortion, stabilizing their energy. For example, octahedral Cu²⁺ complexes (d⁹ configuration) elongate in the axial direction, causing the degenerate e_g orbitals to split. This effect significantly influences the structure, magnetism, and optical properties of transition metal compounds. Chapter 3 calculates the relationship between distortion and energy using Python.

How is machine learning applied to materials chemistry?

Machine learning is widely applied to materials property prediction, new materials exploration, and process optimization. It is particularly effective for surrogate modeling of DFT calculations (fast prediction), pattern extraction from big data like Materials Project, and optimization of experimental conditions. Chapter 5 builds a band gap prediction model using scikit-learn, practicing from feature engineering to prediction accuracy evaluation.

Recommended Resources

  • Python Environment: Anaconda (Python 3.9 or higher), JupyterLab/Notebook
  • Essential Libraries: numpy, scipy, matplotlib, pandas
  • Electronic Structure Calculations: ASE (Atomic Simulation Environment), GPAW, PySCF
  • Materials Databases: pymatgen, mp-api (Materials Project API)
  • Thermodynamic Calculations: pycalphad
  • Visualization: py3Dmol (3D visualization of molecular and crystal structures)
  • Machine Learning: scikit-learn, matminer (materials descriptor library)
  • Recommended Textbooks:
    • Atkins, P., de Paula, J. "Physical Chemistry" (standard textbook for physical chemistry)
    • Shriver, D.F., Atkins, P.W. "Inorganic Chemistry" (classic in inorganic chemistry)
    • Ashcroft, N.W., Mermin, N.D. "Solid State Physics" (classic in solid state physics)
    • Martin, R.M. "Electronic Structure" (comprehensive explanation of electronic structure theory)
  • Online Resources:

Learning Objectives of This Series

Upon completion of this series, you will acquire the following skills:

  • Theoretically understand the types and characteristics of chemical bonds and calculate bond energies using Python
  • Understand molecular orbital theory and band theory in a unified manner and implement LCAO method and Hückel theory
  • Explain d-orbital splitting in transition metal complexes using crystal field theory and predict magnetic and optical properties
  • Understand Gibbs free energy, chemical potential, and phase diagrams, and perform calculations using pycalphad
  • Understand DFT calculation fundamentals and execute crystal structure optimization and band structure calculations using ASE+GPAW
  • Utilize Materials Project API and practice data-driven materials exploration
  • Apply machine learning to materials property prediction and build and evaluate prediction models

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