Materials & Design (Nov 2020)
Direct inverse analysis based on Gaussian mixture regression for multiple objective variables in material design
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.
