pycalphad

Phase diagrams are computed by performing Gibbs energy minimization across temperature, pressure, and composition grids using CALPHAD thermodynamic databases. pycalphad reads Thermo-Calc style TDB files and calculates equilibrium phase assemblies, outputting phase names, compositions, and mole fractions for binary, ternary, and multicomponent systems.

Yes. pycalphad performs Gibbs energy minimization on CALPHAD databases to calculate chemical potentials, activities, Gibbs energy, enthalpy, entropy, and heat capacity across specified temperature, pressure, and composition conditions, enabling thermodynamic property mapping.

Driving force and metastability analyses quantify the thermodynamic distance between phases at given conditions. pycalphad computes these by evaluating Gibbs energy surfaces and phase equilibria, helping identify energetically favorable phases and predict phase transformations in binary through multicomponent systems.

pycalphad reads Thermo-Calc style TDB (thermodynamic database) files containing CALPHAD descriptions of phases, elements, and compounds. It handles vacancies, ordered phases, and multicomponent interactions, outputting standard thermodynamic properties: NP, X, MU, GM, HM, SM, CPM, and ACR data.

Understanding CALPHAD methodology and phase equilibrium concepts strengthens workflow design, but pycalphad provides standard APIs—Database, equilibrium, calculate, binplot—that abstract complexity. Start with binary systems and preexisting TDB databases to learn by example.

Single-point calculations return equilibrium state at one T-P-X condition; full phase diagrams sweep grids across temperature, pressure, and composition to map phase regions and boundaries. pycalphad supports both: calculate() for points and binplot() for visualization across composition and temperature ranges.