# Accelerating Materials Discovery with Data-Driven Tools

> **Harness the power of data science to explore, analyze, and visualize materials properties like never before.**  
> Using cutting-edge tools like **pymatgen**, **ASE**, and **matminer**, this tutorial series will guide you step by step through the process of **querying**, **visualizing**, and **analyzing** materials data from world-class databases.

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Modern **materials science** is undergoing a **data revolution**. Thanks to advances in **high-throughput density functional theory (DFT)**, thousands of materials are simulated and characterized daily, resulting in **massive datasets** that hold the key to discovering new functional compounds.

However, extracting insights from these datasets is **not trivial**. That is where tools like:

- **[Materials Project](https://materialsproject.org/)** → a comprehensive database with millions of computed materials properties
- **[pymatgen](https://pymatgen.org/)** → a Python library for analyzing structures and properties
- **[ASE](https://wiki.fysik.dtu.dk/ase/)** → for interactive 3D visualization of atomic structures
- **[matminer](https://hackingmaterials.lbl.gov/matminer/)** → for feature extraction, dataset access, and materials data mining

help researchers, engineers, and students **accelerate materials discovery** for technologies like **semiconductors**, **photovoltaics**, **energy storage**, **catalysis**, **clean fuels**, and **batteries**.

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## What You will Learn

By following this tutorial series, you will learn how to:

- ✅ **Access** and query materials properties using the **Materials Project API**.
- ✅ **Visualize** atomic structures in 3D using **pymatgen** and **ASE**.
- ✅ **Analyze** and compare materials based on structural, electronic, and thermodynamic properties.
- ✅ **Mine data** from curated datasets using **matminer**.
- ✅ Gain practical, **hands-on experience** with data-driven materials science.

Each tutorial is designed to be **beginner-friendly** yet **practically useful**, combining **clear explanations** with **real-world examples**.

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## Tutorial Roadmap

| Tutorial | Description | Tools Covered |
|---------|------------|---------------|
| **1. Accessing Materials Data** | Learn to query the **Materials Project** database and retrieve materials properties for analysis. | `pymatgen`, Materials Project API |
| **2. Visualizing Atomic Structures** | Explore and visualize 3D structures interactively using **ASE** and **pymatgen**. | `ASE`, `pymatgen` |
| **3. Querying and Comparing Materials** | Perform filtered searches and compare candidate materials based on their properties. | `pymatgen`, `pandas` |
| **4. Data Mining with Matminer** | Work with curated datasets, perform filtering, feature extraction, and statistical analysis. | `matminer`, `seaborn`, `pandas` |

Each tutorial builds on the previous one, so we recommend working through them in order.

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## Who Is This For?

This tutorial series is designed for:

- **Students** in materials science, chemistry, and physics.
- **Researchers** exploring new compounds and their properties.
- **Engineers** seeking to integrate data-driven workflows into Research and Development.
- **Developers** curious about materials informatics.

### **Prerequisites**
- Curiosity and enthusiasm for **materials science**.
- No prior experience with DFT, databases, or visualization tools required — we will guide you through it!

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## Running the tutorial in Google Colab

You do not need to install anything locally to follow this tutorial. All notebooks can be opened and executed directly in [Google Colab](https://colab.research.google.com/).

### 1. Open the Notebook in Colab
- Navigate the tutorial website.
- At the top of each section, click the rocket icon and then **launch in colab**, as shown in the picture:
<img src="./_static/images/intro/launch_colab.jpeg" width=700 style=margin:auto/>

### 2. Install Required Packages

Each notebook specifies the packages it needs. Usually, you can run the first cell to install them. For example:

```python
!pip install pymatgen matminer seaborn
```

### 3. Run the Notebook Cells

- Press **Shift+enter** (or click the ▶ button) to execute a cell.

- Run the cells in orer, top to bottom.

- If something fails (e.g., missing package), check the installation cell again.

### 4. Saving Your Progress
- To save a copy of the notebook with your edits: File → Save a copy in Drive

- To download locally: File → Download → .ipynb
    Or you can download it directly from this page by clicking the download icon at the top right.

## Contents

```{tableofcontents}
```