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index.qmd
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---
title: "ESPEI Documentation"
---
\part{Introduction}
ESPEI, or Extensible Self-optimizing Phase Equilibria Infrastructure, is
a tool for creating Calphad databases and evaluating the uncertainty of
Calphad models. The purpose of ESPEI is to be both a user tool for
fitting state-of-the-art Calphad-type models and to be a research
platform for developing methods for fitting and uncertainty
quantification. ESPEI uses [pycalphad](http://pycalphad.org) for the
thermodynamic backend and supports fitting adjustable parameters for any
pycalphad model.
ESPEI is developed in the open on
[GitHub](https://github.com/PhasesResearchLab/ESPEI). The project is led
by Brandon Bocklund, who is currently a postdoctoral researcher at
Lawrence Livermore National Laboratory. Brandon developed ESPEI while
completing his Ph.D. under Zi-Kui Liu at The Pennsylvania State
University. See the project\'s [History](#history) for more details.
# What does ESPEI do?
## Parameter generation
ESPEI can be used to generate model parameters for Calphad models of the
Gibbs energy that follow the temperature-dependent power series
expansion of the Gibbs energy within the compound energy formalism (CEF)
for endmembers and for binary and ternary Redlich-Kister interaction
parameters with Muggianu extrapolation. This parameter generation step
augments the Calphad modeler by providing tools for data-driven model
selection, rather than relying on a modeler\'s intuition alone. Model
generation is based on a linear regression of enthalpy, entropy, and
heat capacity data (see [non-equilibrium thermochemical data](reference/dataset_schema.qmd#non_equilibrium_thermochemical_data)), using the corrected Akiake Information Criterion (AICc) to
prevent overfitting.
## Optimization and uncertainty quantification
ESPEI can optimize and quantify the uncertainty of Calphad model
parameters to thermochemical and
[phase boundary data](reference/dataset_schema.qmd#phase_boundary_data). Optimization and uncertainty quantification is performed
using a Bayesian ensemble Markov Chain Monte Carlo (MCMC) method. Any
Calphad database can be used, including databases generated by ESPEI or
starting from an existing Calphad database.
ESPEI supports all models supported by pycalphad. User-developed models
that are compatible with pycalphad can be used without making any
modifications to ESPEI\'s code. Performing Bayesian parameter estimation
for arbitrary multicomponent thermodynamic data is supported.
```{python}
#| echo: false
#| warning: false
#| fig-cap: "The Cu-Mg phase diagram plotted from a database created with and optimized by ESPEI. See the [Cu-Mg Example](tutorials/cu-mg-example/cu-mg-example.qmd) to try it yourself!"
from pycalphad import Database, binplot, variables as v
from espei.datasets import load_datasets, recursive_glob
from espei.plot import dataplot
datasets = load_datasets(recursive_glob("tutorials/cu-mg-example/input-data"))
dbf = Database("tutorials/cu-mg-example/Cu-Mg-mcmc.tdb")
comps = ["CU", "MG", "VA"]
phases = list(dbf.phases.keys())
conds = {v.P: 101325, v.T: (500, 1500, 10), v.X("MG"): (0, 1, 0.02)}
# plot the phase diagram and data
ax = binplot(dbf, comps, phases, conds)
dataplot(comps, phases, conds, datasets, ax=ax)
fig = ax.figure
fig.show()
```
# History
The name ESPEI and early concept were developed by
[@shang2010] under the supervision of Zi-Kui
Liu. After developing [pycalphad](http://pycalphad.org), Richard Otis
and Zi-Kui Liu reimagined the concept and wrote
[pycalphad-fitting](https://github.com/richardotis/pycalphad-fitting)
(used in [@otis2016] and
[@otis2017]), which formed the nucleus for the
present version of ESPEI [@bocklund2019].
Details on the implementation of ESPEI can be found in the following
publications:
- B. Bocklund *et al.*, MRS Communications 9(2) (2019) 1--10.
[doi:10.1557/mrc.2019.59](https://doi.org/10.1557/mrc.2019.59).
- B. Bocklund, Ph.D. Dissertation (Chapter 3), The Pennsylvania State
University (2021), <https://etda.libraries.psu.edu/catalog/21192bjb54>
- B. Bocklund *et al.*, Calphad 86 (2024) 102720.
[doi:10.1016/j.calphad.2024.102720](https://doi.org/10.1016/j.calphad.2024.102720)
We are thankful for the financial support provided to Brandon during his
Ph.D. by the Computational Materials Education and Training (CoMET) NSF
Research Traineeship (grant number DGE-1449785) and from a NASA Space
Technology Research Fellowship (NSTRF, grant number 80NSSC18K1168).
# Getting Help
For help on installing and using ESPEI, please join the
[PhasesResearchLab/ESPEI Gitter
room](https://gitter.im/PhasesResearchLab/ESPEI).
Bugs and software issues should be reported on the [GitHub issue
tracker](https://github.com/PhasesResearchLab/ESPEI/issues).
# Citing ESPEI
If you use ESPEI for work presented in a publication, we ask that you
cite the following publication:
B. Bocklund, R. Otis, A. Egorov, A. Obaied, I. Roslyakova, Z.-K. Liu,
ESPEI for efficient thermodynamic database development,
modification, and uncertainty quantification: application to Cu--Mg,
MRS Commun. (2019) 1--10. [doi:10.1557/mrc.2019.59](https://doi.org/10.1557/mrc.2019.59).
<!-- -->
@article{Bocklund2019ESPEI,
archivePrefix = {arXiv},
arxivId = {1902.01269},
author = {Bocklund, Brandon and Otis, Richard and Egorov, Aleksei and Obaied, Abdulmonem and Roslyakova, Irina and Liu, Zi-Kui},
doi = {10.1557/mrc.2019.59},
eprint = {1902.01269},
issn = {2159-6859},
journal = {MRS Communications},
month = {jun},
pages = {1--10},
title = {{ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: application to Cu–Mg}},
year = {2019}
}
# Changelog
See [what\'s new](CHANGES.html)!
# License
ESPEI is MIT licensed.
The MIT License (MIT)
Copyright (c) 2015 Richard Otis
Copyright (c) 2017 Brandon Bocklund
Copyright (c) 2018 Materials Genome Foundation
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.