gnnepcsaft_mcp_server
GNNEPCSAFT MCP Server facilitates advanced thermodynamic calculations using Graph Neural Networks to estimate ePC-SAFT parameters, providing predictions for properties like density and vapor pressure. It is suitable for pure components, mixtures, and integrates with LLMs for chemistry applications.
GNNEPCSAFT MCP Server
GNNEPCSAFT MCP Server is designed for enabling communication and context management between large language models (LLMs) and clients for advanced thermodynamic calculations. It leverages Graph Neural Networks (GNNs) to estimate ePC-SAFT parameters, facilitating predictions like density and vapor pressure for any molecule without experimental data. Key features include supporting pure components and mixtures, automatic data collection from PubChem, and deployment for thermodynamics-aware LLMs.
Key Features
- Estimate ePC-SAFT parameters using GNNs
- Calculate density, vapor pressure, enthalpy of vaporization, and critical points
- Support for pure components and mixtures
- Automatic data collection from PubChem for any molecule
Use Cases
- Predicting thermodynamic properties for new or existing molecules
- Running property calculations for mixtures
- Integrating with LLMs for chemistry and materials science applications
- Automating data collection and property estimation in pipelines