Development of linearly independent descriptor generation method for sparse and interpretable modeling in materials science

In recent years, researches using techniques of machine-learning have been considerably activated in the field of materials science and we focus on research for empirical law discovery to elucidate a mechanism of physical properties of target materials. We propose linearly independent descriptor generation method for increasing the expression capability of linear regression model without generating any multicollinearity and strong near-multicollinearity which are a major problem in linear regression analysis. Our method is expected to be an essential preprocessing technique for sparse and interpretable modeling in materials science.