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MCP-server exposing ML models for prediction of kpoints distance / kpoints grid for SCF DFT calculations trained on QE data for 3D crystalline inorganic materials

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Goldilocks MCP server

Provides k-point generation tools for Quantum ESPRESSO with SSSP1.3 PBEsol efficiency version of pseudo-potentials

Tools exposed:

estimate_kpoint_distance

Requires specification path to the structure file, confidence level (models are trained for levels 0.85,0.9, and 0.95), and the model (ALIGNN or RF)

Example prompt: "Can you please generate k-points spacing for structure 'path/to/BaGa4.cif', confidence level 0.95 with ALIGNN model?"

Outputs the predicted k-spacing, and the confidence interval

generate_kpoint_grid

Requires specification path to the structure file, confidence level (models are trained for levels 0.85,0.9, and 0.95), and the model (ALIGNN or RF)

Example prompt: "Can you please generate k-points grid for structure 'path/to/BaGa4.cif', confidence level 0.95 with ALIGNN model?"

Outputs the predicted kmesh, generated using the lower bound of k-spacing interval (to make sure that the probability that predicted value is in agreement with confidence level)

Installing MCP-server locally

  1. Install uv (https://docs.astral.sh/uv/getting-started/installation/)

  2. Clone repository

git clone https://github.com/stfc/goldilocks-mcp.git
cd goldilocks-mcp
  1. Create virtual environment and install dependencies
uv venv --python 3.11
source .venv/bin/activate
uv pip install -e .
  1. Install pytorch-geometric (can't be installed from pyproject.toml but is required). See details https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html
uv pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.8.0+cpu.html
uv pip install torch_geometric

Adding mcp to Claude Desktop

To add goldilocks-mcp to Claude Desktop:

  1. Open or create the Claude Desktop configuration file:

  2. If the file doesn't exist, create it with the content from claude_desktop_config.json. If it already exists, merge the goldilocks-mcp entry into the existing mcpServers object.

  3. Important: Update the path in the config file. Replace "absolute/path/to/goldilocks-mcp/goldilocks_mcp/" with the actual absolute path to the goldilocks_mcp directory in your cloned repository.

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MCP-server exposing ML models for prediction of kpoints distance / kpoints grid for SCF DFT calculations trained on QE data for 3D crystalline inorganic materials

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