# Introduction

<div align="center"><img src="https://385940188-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MW6A6E0L1zra4m3voL_%2Fsync%2F1aa23e12013ce5b3e26a41208016c078ffac28ad.png?generation=1616695021634698&#x26;alt=media" alt="Foundry" width="400"></div>

**Foundry-ML** is a Python library that simplifies access to machine learning-ready datasets in materials science and chemistry.

## Features

* **Search & Discover** - Find datasets by keyword or browse the catalog
* **Rich Metadata** - Understand datasets before downloading with detailed schemas
* **Easy Loading** - Get data in Python, PyTorch, or TensorFlow format
* **Automatic Caching** - Fast subsequent access after first download
* **Publishing** - Share your own datasets with the community
* **AI Integration** - MCP server for AI assistant access
* **CLI** - Terminal-based workflows

## Quick Example

```python
from foundry import Foundry

# Connect
f = Foundry()

# Search for datasets
results = f.search("band gap", limit=5)

# Load a dataset
dataset = results.iloc[0].FoundryDataset
X, y = dataset.get_as_dict()['train']

# Get citation for your paper
print(dataset.get_citation())
```

## Installation

```bash
pip install foundry-ml
```

For cloud environments (Colab, remote Jupyter):

```python
f = Foundry(no_browser=True, no_local_server=True)
```

## What's Next?

| <p><strong>Getting Started</strong></p><ul><li><a href="installation">Installation</a></li><li><a href="quickstart">Quick Start</a></li></ul> | <p><strong>User Guide</strong></p><ul><li><a href="../user-guide/searching">Searching</a></li><li><a href="../user-guide/loading-data">Loading Data</a></li><li><a href="../user-guide/ml-frameworks">ML Frameworks</a></li></ul> | <p><strong>Features</strong></p><ul><li><a href="../features/cli">CLI</a></li><li><a href="../features/mcp-server">MCP Server</a></li><li><a href="../features/huggingface">HuggingFace</a></li></ul> |
| --------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |

## Project Support

This work was supported by the National Science Foundation under NSF Award Number: 1931306 "Collaborative Research: Framework: Machine Learning Materials Innovation Infrastructure".

Foundry brings together components from:

* [Materials Data Facility (MDF)](https://materialsdatafacility.org)
* [Data and Learning Hub for Science (DLHub)](https://www.dlhub.org)
* [MAST-ML](https://mastmldocs.readthedocs.io/)