"Vapor" matching MCP servers:

Enables AI assistants to perform complex chemical engineering calculations including thermodynamic modeling, equation of state calculations, phase equilibrium computations, and vapor-liquid equilibrium analysis. Built on the MoziChem framework to bridge AI language models with specialized chemical engineering tools.

Enables estimation of PC-SAFT pure-component parameters and prediction of properties like density and vapor pressure for any molecule or mixture using Graph Neural Networks.

Enables AI agents to look up thermophysical properties — density, viscosity, thermal conductivity, speed of sound, enthalpy, entropy, Cv/Cp and phase — for roughly 76 reference fluids at a given temperature and pressure, or along the liquid/vapor saturation curve, plus fluid listing and name resolution. Each returned value is accompanied by its unit, and out-of-range conditions surface as clear errors.

Computes thermophysical fluid properties for ~124 pure fluids from any two independent state variables, returning density, enthalpy, entropy, viscosity, thermal conductivity, specific heat, vapor quality and phase. It also lists the exact canonical fluid names the build recognizes, so agents can query valid states anywhere inside each fluid's equation-of-state range.