Implementation of an extensible property modeling framework in ESPEI with applications to molar volume and elastic stiffness models

IF 1.9 3区 材料科学 Q4 CHEMISTRY, PHYSICAL
Brandon Bocklund, Aurélien Perron, Joseph T. McKeown, Kaila M. Bertsch
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引用次数: 0

Abstract

Property models are becoming more widely adopted by commercial Calphad databases, but they are not nearly as common in non-commercial or traditional academic Calphad databases. A primary driver is that user-friendly Calphad modeling tools that support property models are not widely available. Here we present new property modeling capabilities that have been implemented in ESPEI (the Extensible, Self-optimizing Phase Equilibrium Infrastructure). These capabilities include both generating property model parameters from data and improvements to the algorithmic selection of the most appropriate model from a series of candidates. Two illustrative examples are given that use ESPEI to fit different property models. First, we generate molar volume model parameters for Group IV, V, and VI refractory BCC alloys based on the model by Lu et al. (2005). Second, we demonstrate the extensibility of ESPEI’s property modeling capabilities by implementing a custom PyCalphad model for BCC elastic stiffness parameters to generate and compare parameters to the ones assessed by Marker et al. (2018) using the same data. Property models generated by ESPEI can be used in PyCalphad or further optimized with uncertainty quantification using ESPEI.

在 ESPEI 中实施可扩展的属性建模框架,并应用于摩尔体积和弹性刚度模型
商业 Calphad 数据库越来越广泛地采用属性模型,但在非商业或传统的学术 Calphad 数据库中却并不常见。一个主要原因是支持属性模型的用户友好型 Calphad 建模工具并不普及。在此,我们介绍 ESPEI(可扩展、自优化相平衡基础架构)中实现的新属性建模功能。这些功能包括从数据中生成属性模型参数,以及改进从一系列候选模型中选择最合适模型的算法。下面给出两个使用 ESPEI 拟合不同性质模型的示例。首先,我们根据 Lu 等人(2005 年)的模型生成了第四、五和六组难熔 BCC 合金的摩尔体积模型参数。其次,我们通过实施自定义 PyCalphad 模型来生成 BCC 弹性刚度参数,并与 Marker 等人(2018 年)使用相同数据评估的参数进行比较,从而展示了 ESPEI 属性建模功能的可扩展性。ESPEI生成的属性模型可用于PyCalphad,或通过ESPEI的不确定性量化进一步优化。
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来源期刊
CiteScore
4.00
自引率
16.70%
发文量
94
审稿时长
2.5 months
期刊介绍: The design of industrial processes requires reliable thermodynamic data. CALPHAD (Computer Coupling of Phase Diagrams and Thermochemistry) aims to promote computational thermodynamics through development of models to represent thermodynamic properties for various phases which permit prediction of properties of multicomponent systems from those of binary and ternary subsystems, critical assessment of data and their incorporation into self-consistent databases, development of software to optimize and derive thermodynamic parameters and the development and use of databanks for calculations to improve understanding of various industrial and technological processes. This work is disseminated through the CALPHAD journal and its annual conference.
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