# 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. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.materialsproject.org/services.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.