# ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: application to Cu-Mg

## TL;DR

ESPEI is a software tool that streamlines thermodynamic database development by combining linear fitting and Bayesian optimization, demonstrated on the Cu-Mg system to quantify and propagate uncertainties in Gibbs energy models.

## Contribution

The paper introduces ESPEI, a novel software package that integrates linear fitting and Bayesian methods for efficient thermodynamic modeling and uncertainty quantification.

## Key findings

- Successful application to Cu-Mg system down to 0 K
- Quantification and propagation of model parameter uncertainties
- Demonstration of Bayesian optimization in thermodynamic modeling

## 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.

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Source: https://tomesphere.com/paper/1902.01269