高效热力学数据库开发、修改和不确定性量化的ESPEI：应用于Cu-Mg
ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: application to Cu-Mg
摘要：
软件包ESPEI已开发的热力学模型参数的有效评估内的CALPHAD方法。ESPEI使用线性拟合策略基于热化学数据对单相的吉布斯能函数进行参数化，并使用相平衡数据通过马尔可夫链蒙特卡罗机器学习方法中的贝叶斯优化来细化模型参数。本文详细讨论了ESPEI中采用的方法，并使用基于分段回归的一元描述对低至0 K的Cu-Mg系统进行了演示。模型参数的不确定性被量化并传播到吉布斯能量函数。
英文摘要：
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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