Integrates with Google DeepMind's GNoME (Graph Networks for Materials Exploration) dataset, enabling access to millions of predicted stable crystal structures for materials science applications.
Uses Poetry for dependency management and package installation, simplifying the setup process for the materials science MCP server.
Built on Python for materials database access through the OPTIMADE API, enabling efficient querying and retrieval of crystal structures and their properties.
Materials MCP Project
A Model Context Protocol (MCP) server designed to interact with materials databases through the OPTIMADE API, with a specific focus on Google DeepMind's GNoME (Graph Networks for Materials Exploration) dataset. This project serves as a bridge between the OPTIMADE API and materials science applications, enabling efficient access and manipulation of crystal structure data.
Overview
The Materials MCP Project implements a Model Context Protocol server that:
- Interfaces with the OPTIMADE API to access materials databases
- Provides specialized access to the GNoME dataset, which contains millions of predicted stable crystal structures
- Enables efficient querying and retrieval of crystal structures and their properties
- Supports standardized data exchange formats for materials science applications
Features
- OPTIMADE API integration for standardized materials database access
- GNoME dataset integration for accessing predicted stable crystal structures
- RESTful API endpoints for crystal structure queries
- Support for common materials science data formats
- Efficient data caching and retrieval mechanisms
- Standardized query language support
Setup
- Ensure you have Python 3.10 or higher installed
- Create a virtual environment:
- Install dependencies using Poetry:
Project Structure
materials_mcp/
- Main package directoryapi/
- OPTIMADE API integrationgnome/
- GNoME dataset specific functionalitymodels/
- Data models and schemasserver/
- MCP server implementation
tests/
- Test directorypyproject.toml
- Project configuration and dependenciesREADME.md
- This file
Dependencies
- Python >=3.10
- optimade >=1.2.4 - For OPTIMADE API integration
- Additional dependencies will be added as needed for:
- FastAPI/Flask for the web server
- Database integration
- Data processing and analysis
- Testing and documentation
Usage
[Usage examples will be added as the project develops]
Contributing
[Contribution guidelines will be added]
License
[License information will be added]
Acknowledgments
- Google DeepMind for the GNoME dataset
- OPTIMADE consortium for the API specification
- [Other acknowledgments to be added]
This server cannot be installed
remote-capable server
The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.
A Model Context Protocol server that provides access to materials databases through the OPTIMADE API, with focus on Google DeepMind's GNoME dataset containing millions of predicted crystal structures.
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