"""
Define citations for ESPEI
"""

ESPEI_CITATION = "﻿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, (2019). http://arxiv.org/abs/1902.01269."

ESPEI_BIBTEX = """@article{Bocklund2019ESPEI,
abstract = {The software package ESPEI has been developed for efficient evaluation of thermodynamic model parameters within the CALPHAD method. ESPEI uses a linear fitting strategy to parameterize Gibbs energy functions of single phases based on their thermochemical data and refine the model parameters using phase equilibrium data through Bayesian optimization within a Markov Chain Monte Carlo machine learning approach. In this paper, the methodologies employed in ESPEI are discussed in detail and demonstrated for the Cu-Mg system down to 0 K using unary descriptions based on segmented regression. The model parameter uncertainties are quantified and propagated to the Gibbs energy functions.},
archivePrefix = {arXiv},
arxivId = {1902.01269},
author = {Bocklund, Brandon and Otis, Richard and Egorov, Aleksei and Obaied, Abdulmonem and Roslyakova, Irina and Liu, Zi-Kui},
eprint = {1902.01269},
month = {feb},
title = {{ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: application to Cu-Mg}},
url = {http://arxiv.org/abs/1902.01269},
year = {2019}
}
"""


"""
Define citations for ESPEI
"""

ESPEI_CITATION = "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, (2019). http://arxiv.org/abs/1902.01269."

ESPEI_BIBTEX = """@article{Bocklund2019ESPEI,
abstract = {The software package ESPEI has been developed for efficient evaluation of thermodynamic model parameters within the CALPHAD method. ESPEI uses a linear fitting strategy to parameterize Gibbs energy functions of single phases based on their thermochemical data and refine the model parameters using phase equilibrium data through Bayesian optimization within a Markov Chain Monte Carlo machine learning approach. In this paper, the methodologies employed in ESPEI are discussed in detail and demonstrated for the Cu-Mg system down to 0 K using unary descriptions based on segmented regression. The model parameter uncertainties are quantified and propagated to the Gibbs energy functions.},
archivePrefix = {arXiv},
arxivId = {1902.01269},
author = {Bocklund, Brandon and Otis, Richard and Egorov, Aleksei and Obaied, Abdulmonem and Roslyakova, Irina and Liu, Zi-Kui},
eprint = {1902.01269},
month = {feb},
title = {{ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: application to Cu-Mg}},
url = {http://arxiv.org/abs/1902.01269},
year = {2019}
}
"""


Remove non-breaking space unicode character from citation