> For the complete documentation index, see [llms.txt](https://ai-materials-and-chemistry.gitbook.io/foundry/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ai-materials-and-chemistry.gitbook.io/foundry/getting-started/readme.md).

# Introduction

<div align="center"><img src="/files/-MfZQiggG4qV-OyuWO5U" 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="/pages/ju3AYOFchMWpP6pUqDDX">Installation</a></li><li><a href="/pages/kn1c3afzutnYjk4SfCh6">Quick Start</a></li></ul> | <p><strong>User Guide</strong></p><ul><li><a href="/pages/PwR4h7jMy7IAwbrBcTy8">Searching</a></li><li><a href="/pages/TtaEXuGGHY6NPWeDlceX">Loading Data</a></li><li><a href="/pages/Eawrh0H9g7cITJNG60Ml">ML Frameworks</a></li></ul> | <p><strong>Features</strong></p><ul><li><a href="/pages/Y3WWjg7mdygiFxOVSlop">CLI</a></li><li><a href="/pages/U9PUhaAs60zH5sAjwVev">MCP Server</a></li><li><a href="/pages/1B9JJKhDkSJYmwFXLFvC">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/)