Series Overview
This series is a comprehensive 4-chapter educational content designed for those learning about materials databases—the most critical resources in materials science—from scratch, to those seeking to develop practical skills progressively.
Materials databases are vast repositories of systematically accumulated knowledge, containing DFT calculation results and experimental data. Major databases used by researchers worldwide, such as Materials Project (140k materials), AFLOW (3.5M structures), and OQMD (1M materials), consolidate decades of accumulated material property data.
Chapter Contents
Chapter 1: Complete Overview of Materials Databases
Difficulty: Beginner | Reading Time: 20-25 minutes | Code Examples: 10
Learn the characteristics of the four major materials databases (MP, AFLOW, OQMD, JARVIS) and gain the ability to select the appropriate database according to research objectives. Acquire practical skills from obtaining Materials Project API keys to basic data retrieval.
- Comparison of the four major databases
- API authentication and access methods
- Basics of data retrieval
- History of materials databases
Chapter 2: Complete Guide to Materials Project
Difficulty: Beginner to Intermediate | Reading Time: 30-35 minutes | Code Examples: 18
Aim for complete mastery of pymatgen and MPRester API. Progressively acquire practical skills including advanced query techniques, batch downloads, and data visualization.
- pymatgen fundamentals
- MPRester API details
- Advanced query techniques
- Batch downloads
- Data visualization
Chapter 3: Database Integration and Workflow
Difficulty: Intermediate | Reading Time: 20-25 minutes | Code Examples: 12
Integrate multiple databases and construct data cleaning, missing value handling, and automated update pipelines. Learn the importance of data quality management through practical case studies.
- Integration of multiple databases
- Data cleaning
- Missing value handling
- Automated update pipelines
Chapter 4: Building Custom Databases
Difficulty: Intermediate | Reading Time: 15-20 minutes | Code Examples: 10
Learn how to structure and publish experimental data, from SQLite to PostgreSQL. Practice everything from schema design, CRUD operations, and backup strategies to data publication on Zenodo and DOI acquisition.
- Database design fundamentals
- Local DB with SQLite
- PostgreSQL/MySQL
- Backup strategies
- Data publication and DOI acquisition
How to Proceed with Learning
For Beginners: Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 (all chapters recommended)
For Intermediate Learners: Chapter 2 (advanced queries) → Chapter 3 → Chapter 4
For Specific Skill Enhancement: Select only the chapters you need
Prerequisites
- Python fundamentals (variables, functions, lists, dictionaries)
- Basic pandas operations (recommended)