xirtamesrevni_mcp_atomictoolkit

xirtamesrevni_mcp_atomictoolkit

by XirtamEsrevni
An MCP-compatible server for atomistic simulations using ASE, pymatgen, and machine learning interatomic potentials.

Atomistic Toolkit MCP Server

Overview

The Atomistic Toolkit MCP Server is an MCP-compatible server designed to provide atomistic simulation capabilities. It leverages powerful tools like ASE (Atomic Simulation Environment), pymatgen, and machine learning interatomic potentials (MLIPs) to enable advanced simulations and structure manipulations.

Note: This project is under active development, and not all features are fully functional yet.

Features

ASE Tools

  • Structure creation and manipulation: Easily create and modify atomic structures.
  • Geometry optimization: Optimize the geometry of atomic structures for better simulation results.
  • File I/O operations: Read and write structures in various file formats.

Configuration

The project is built using Python and includes a pyproject.toml file for managing dependencies and project configurations. Ensure you have the required Python environment set up before proceeding.

Usage

To use the Atomistic Toolkit MCP Server, follow these steps:
1. Clone the repository:
bash git clone https://github.com/XirtamEsrevni/mcp-atomictoolkit.git
2. Navigate to the project directory:
bash cd mcp-atomictoolkit
3. Install the necessary dependencies:
bash pip install .
4. Start the server and begin utilizing its atomistic simulation capabilities.

Resources

  • Readme: Detailed documentation on setup and usage.
  • MIT License: The project is licensed under the MIT license.

Development Status

The project is actively being developed, and contributions are welcome. Check the Activity page for the latest updates.

Community

Languages

  • Python 100.0%: The project is entirely written in Python.

Support

For issues or questions, please report them through GitHub.

Features & Capabilities

Categories
mcp_server model_context_protocol python ase pymatgen atomistic_simulation machine_learning mlip

Implementation Details

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Repository Info

XirtamEsrevni Organization

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