Nanomaterials Introduction Series
Learning Nanomaterial Science through Python Practice
About This Series
This series is an educational content that systematically covers Nanomaterials from fundamentals to practical data analysis and materials design. You will learn property prediction and materials design methods using machine learning and data-driven approaches for representative nanomaterials such as carbon nanotubes, graphene, quantum dots, and metal nanoparticles.
Target Audience
- Undergraduate and graduate students (Engineering, Materials Science, Chemistry, Physics)
- Beginners in nanomaterials research
- Researchers and engineers who want to apply machine learning to materials development
- Those considering a career in nanotechnology
Learning Objectives
Upon completing this series, you will acquire the following skills:
✅ Understanding basic principles of nanomaterials and size effects
✅ Knowledge of nanoparticle synthesis and characterization techniques
✅ Nanomaterial data analysis and visualization with Python
✅ Prediction of nanomaterial properties using machine learning
✅ Practical nanomaterial design using Bayesian optimization
✅ Problem-solving skills through real-world application cases
Prerequisites
- Required: High school mathematics (statistics, calculus), Python basics
- Recommended: University-level physics and chemistry (year 1-2), linear algebra, basic concepts of machine learning
Series Structure
📘 Chapter 1: Introduction to Nanomaterials
Reading Time: 20-25 min | Level: Beginner
Learn about the definition of nanomaterials, size effects, quantum effects, and classification (0D to 3D nanomaterials). Understand the characteristics and application fields of representative nanomaterials such as carbon nanotubes, graphene, quantum dots, and metal nanoparticles.
Learning Content:
- Definition of nanoscale and size effects
- Quantum effects and quantum confinement effects
- Dimensional classification of nanomaterials
- Application fields (energy, electronics, medicine, environment)
📗 Chapter 2: Fundamental Principles of Nanomaterials
Reading Time: 25-30 min | Level: Beginner to Intermediate
Learn about nanomaterial synthesis methods, characterization techniques, size-dependent properties, and surface/interface effects. Understand measurement techniques such as TEM, SEM, XRD, and UV-Vis, as well as the mechanisms of property emergence unique to nanomaterials.
Learning Content:
- Bottom-up and top-down synthesis methods
- Characterization techniques (TEM, SEM, XRD, UV-Vis, Raman)
- Size-dependent properties (melting point depression, optical properties, magnetic properties)
- Surface area/volume ratio and surface energy
💻 Chapter 3: Python Practical Tutorial
Reading Time: 30-40 min | Level: Intermediate
Practice nanomaterial data analysis and visualization using Python, property prediction using machine learning, and materials design using Bayesian optimization. Through 30-35 executable code examples, you will acquire techniques that can be used in actual nanomaterials research.
Learning Content:
- Analysis and visualization of nanoparticle size distributions
- Prediction of optical properties (plasmon resonance, quantum dot emission)
- Property prediction using five types of regression models
- Nanomaterial design using Bayesian optimization
- Analysis of molecular dynamics (MD) data
- Prediction interpretation using SHAP analysis
Libraries Used:
pandas, numpy, matplotlib, seaborn, scikit-learn,
lightgbm, scipy, scikit-optimize, shap
🏭 Chapter 4: Real-World Applications and Careers
Reading Time: 20-25 min | Level: Intermediate
Learn about actual success stories in nanomaterials research through five case studies. Understand the problem-solving process through practical application cases of carbon nanotube composite materials, quantum dots, gold nanoparticle catalysts, graphene, and nanomedicines.
Case Studies:
1. Mechanical Property Optimization of Carbon Nanotube (CNT) Composite Materials
2. Emission Wavelength Control of Quantum Dots
3. Activity Prediction of Gold Nanoparticle Catalysts
4. Electrical Property Control of Graphene
5. Design of Nanomedicines (Drug Delivery)
Career Information:
- Academia vs Industry
- Job types and salaries in the nanomaterials field
- Required skill sets
How to Study
Recommended Learning Path
Study Method
-
Chapter 1-2 (Fundamentals): First, understand the concepts thoroughly
- Basic principles of nanomaterials and size effects
- Synthesis methods and characterization techniques -
Chapter 3 (Practice): Learn by doing
- Execute all code examples yourself
- Change parameters and observe behavior
- Work on exercises -
Chapter 4 (Applications): Think about application to real problems
- Apply case studies to your own research
- Concretize your career plan
Environment Setup
The following environment is required for Chapter 3 practice:
Recommended Environment:
- Python 3.8 or higher
- Jupyter Notebook or Google Colab
- Main libraries: pandas, scikit-learn, lightgbm, scipy, scikit-optimize
Installation instructions are explained in detail in Chapter 3.
Series Features
🎯 Practice-Oriented
You can acquire techniques that can be used in actual nanomaterials research through 30-35 executable Python code examples.
📊 Data-Driven Approach
Learn the latest methods of nanomaterial design using machine learning and Bayesian optimization.
🔬 Real Application Cases
Understand the flow of actual research and development projects through five detailed case studies.
🌐 Learn in English
Technical terms are provided with both English and Japanese, building a foundation for reading international literature.
Related Series
This site also publishes the following series:
- - AI/ML application to materials science in general
- Introduction to Process Informatics - Chemical process optimization and digital twins
References and Resources
Main Textbooks
-
Cao, G. & Wang, Y. (2011). Nanostructures and Nanomaterials: Synthesis, Properties, and Applications (2nd ed.). World Scientific. DOI: 10.1142/7885
-
Pokropivny, V. V. & Skorokhod, V. V. (2007). Classification of nanostructures by dimensionality and concept of surface forms engineering in nanomaterial science. Materials Science and Engineering: C, 27(5-8), 990-993. DOI: 10.1016/j.msec.2006.09.023
-
Roduner, E. (2006). Size matters: why nanomaterials are different. Chemical Society Reviews, 35(7), 583-592. DOI: 10.1039/B502142C
Online Resources
- MIT OpenCourseWare: Nanomaterials courses
- Coursera: Nanotechnology specialization
- Materials Project: Nanomaterials database (https://materialsproject.org)
Feedback and Questions
For questions and feedback regarding this series, please contact:
Dr. Yusuke Hashimoto
Institute of Multidisciplinary Research for Advanced Materials (IMRAM)
Tohoku University
Email: yusuke.hashimoto.b8@tohoku.ac.jp
License
This content is published under Creative Commons Attribution 4.0 International License.
Free use for educational and research purposes is welcome. Please use the following format for citations:
Yusuke Hashimoto (2025) 'Nanomaterials Introduction Series v1.0' Tohoku University
https://yusukehashimotolab.github.io/wp/knowledge/nm-introduction/
Last Updated: October 16, 2025
Version: 1.0