A computational experiment on deducing phase diagrams from spatial thermodynamic data using machine learning techniques. (September 2021)

• Record Type: Journal Article
• Authors: Tsutsui, Kazumasa; Moriguchi, Koji
• Is Part Of: Calphad. Volume 74(2021)
• Publication Date: 2021-09
• Subjects: Phase diagram -- CALPHAD -- Machine learning -- Local analysis -- Electron probe micro analyser -- Electron backscattering diffraction; Phase diagrams -- Data processing -- Periodicals; Thermochemistry -- Data processing -- Periodicals; Diagrammes de phases -- Informatique -- Périodiques; Thermochimie -- Informatique -- Périodiques; Thermodynamica; Electronic journals; 541.363
• DOI: 10.1016/j.calphad.2021.102303
• Languages: English
• ISSNs: 0364-5916

Abstract

Abstract: Derivation and discovery of physical dynamics inherent in big data is one of the most major purposes of machine learning (ML) in the field of modern natural science. In the materials science, phase diagrams are often called as "road maps" to perfectly understand the conditions for phase formation and/or transformation in any material system caused by the associated thermodynamics. In this paper, we report a numerical experiment investigating whether the underlying thermodynamics can be derived from the big data constructed of local spatial composition and phase distribution data along with the help of ML. The artificial data analysed have been created assuming a steel composition based on the calculation phase diagram (CALPHAD) thermodynamics combined with the order-statistics-based sampling model. The hypothetical procedures of data acquisition assumed in this numerical experiment are as follows; (i) obtaining local analysis data on the composition and phase distribution in the same observation area using instruments such as electron probe micro analyser (EPMA) and electron backscattering diffraction (EBSD), and (ii) training the classification model based on a ML algorithm with compositional data as input and the phase data as output. The accuracies of the reconstructed phase diagrams have been estimated for three ML algorithms, i.e. support vector machine (SVM), random forest, and multilayer perceptron (MLP). The phase diagrams predicted using SVM and MLP are found to be adequately consistent with those of the CALPHAD method. We have also investigated the regression performance of the continuous data involved in the CALPHAD thermodynamics, such as the phase fractions of body-centred cubic, face-centred cubic, and cementite phases. Compared with the ML algorithms, the CALPHAD method is found to show superior predictive performance since it is based on the sophisticated physical model.