# ESPEI¶

ESPEI, or Extensible Self-optimizing Phase Equilibria Infrastructure, is a tool for automated thermodynamic database development within the CALPHAD method. It uses pycalphad for calculating Gibbs free energies of thermodynamic models.

## Goals¶

1. Offer a free and open-source tool for users to develop multicomponent databases with quantified uncertainty
2. Enable development of CALPHAD-type models for Gibbs energy, thermodynamic or kinetic properties
3. Provide a platform to build and apply novel model selection and optimization methods

The implementation for ESPEI involves first fitting single-phase data by calculating parameters in thermodynamic models that are linearly described by the single-phase input data. Then Markov Chain Monte Carlo (MCMC) is used to optimize the candidate models from the single-phase fitting to multi-phase zero-phase fraction data. The benefit of this approach is the automated, simultaneous fitting for many parameters that yields uncertainty quantification, as shown in Otis and Liu High-Throughput Thermodynamic Modeling and Uncertainty Quantification for ICME. Jom 69, (2017). Single-phase and multi-phase fitting methods are described in Chapter 3 of Richard Otis’s thesis.

## History¶

The ESPEI package is based on a fork of pycalphad-fitting. The name and idea of ESPEI are originally based off of Shang, Wang, and Liu, ESPEI: Extensible, Self-optimizing Phase Equilibrium Infrastructure for Magnesium Alloys Magnes. Technol. 2010 617-622 (2010).

## Change log¶

See what’s new!

Tutorials

Developer

### Getting Help¶

For help on installing and using ESPEI, please join the PhasesResearchLab/ESPEI Gitter room.

Bugs and software issues should be reported on GitHub.

The MIT License (MIT)

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### Citing ESPEI¶

A publication is in preparation. For now, ESPEI can be cited via the following publication:

R.A. Otis, Z.-K. Liu, High-Throughput Thermodynamic Modeling and Uncertainty Quantification for ICME, JOM. 69 (2017) 886–892. doi:10.1007/s11837-017-2318-6.