Functional Output Regression for Machine Learning in Materials Science.

ABSTRACT: In recent years, there has been a rapid growth in the use of machine learning in material science. Conventionally, a trained predictive model describes a scalar output variable, such as thermodynamic, electronic, or mechanical properties, as a function of input descriptors that vectorize the compositional or structural features of any given material, such as molecules, chemical compositions, or crystalline systems. In machine learning of material data, on the other hand, the output variable is often given as a function. For example, when predicting the optical absorption spectrum of a molecule, the output variable is a spectral function defined in the wavelength domain. Alternatively, in predicting the microstructure of a polymer nanocomposite, the output variable is given as an image from

SUBMITTER: Iwayama M 

PROVIDER: S-EPMC9597664 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature