> For the complete documentation index, see [llms.txt](https://docs.materialsproject.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.materialsproject.org/services.md).

# Services

- [MPContribs](https://docs.materialsproject.org/services/mpcontribs.md): Introduction to MP's contribution platform MPContribs
- [ML & AI applications](https://docs.materialsproject.org/services/ml-and-ai-applications.md): This section summarizes the ways in which Materials Project and its collaborators' data and tools can help with the development of new ML methods.
- [MatBench](https://docs.materialsproject.org/services/ml-and-ai-applications/matbench.md): Tools for assessing the ability of machine learning models to predict materials properties, similar to ML community standards like ImageNet
- [Matbench Discovery](https://docs.materialsproject.org/services/ml-and-ai-applications/matbench-discovery.md): With Janosh Riebesell of Alpha Lee's and Kristin Persson's groups
- [MPtrj](https://docs.materialsproject.org/services/ml-and-ai-applications/mptrj.md)
- [r2SCAN datasets](https://docs.materialsproject.org/services/ml-and-ai-applications/matpes.md): Development of robust, high fidelity datasets for training universal machine learning interatomic potentials
- [MP + LLMs (MCP)](https://docs.materialsproject.org/services/ml-and-ai-applications/mcp.md): Model context protocols for integrating Materials Project data with large lanuage models.