Direct inverse analysis based on Gaussian mixture regression for multiple objective variables in material design. (November 2020)

• Record Type: Journal Article
• Authors: Shimizu, Naoto; Kaneko, Hiromasa
• Is Part Of: Materials & design. Volume 196(2020)
• Publication Date: 2020-11
• Subjects: Material design -- Multiple objective variables -- Experimental conditions -- Direct inverse analysis -- Gaussian mixture regression; Materials -- Periodicals; Engineering design -- Periodicals; Matériaux -- Périodiques; Conception technique -- Périodiques; Electronic journals; 620.11
• DOI: 10.1016/j.matdes.2020.109168
• Languages: English
• ISSNs: 0264-1275

Abstract

Abstract: In research and development of highly functional materials, new materials and compounds are required to achieve target values of multiple properties and activities Y. Because regression methods that construct a single model for each Y variable cannot consider the relationships between the Y variables, the search for candidates for materials that satisfy all of the target Y values cannot be efficiently performed. Furthermore, pseudo-inverse analysis of models, where promising candidates are selected based on the Y values predicted by substituting a large number of candidates for explanatory variables X, such as experimental conditions and molecular descriptors, into models, cannot search for candidates with desired Y values from X candidates. Therefore, in this study, we focused on Gaussian mixture regression (GMR), which can simultaneously handle multiple Y variables. GMR can simultaneously predict multiple Y variables while considering the relationships between the Y variables, and it can also directly predict X variables by substituting the values of multiple Y variables. We used numerical simulation data in which the Y variables were nonlinearly correlated and nonlinear relationships existed between X and Y, and verified the predictive ability of the GMR model and the advantages of direct inverse analysis of the GMR model. In addition, we analyzed a dataset of thermoelectric conversion materials and searched for new high-performance thermoelectric conversion materials. Graphical abstract: Unlabelled Image Highlights: The aim is design of highly functional materials with multiple objective variables Y. Gaussian mixture regression (GMR), which can simultaneously handle multiple Y variables, is focused on. GMR can also predict the values of explanatory variables directly from Y values, which means direct inverse analysis. The proposed method is evaluated using numerical simulation data and thermoelectric conversion materials.