Machine learning of optical properties of materials - predicting spectra from images and images from spectra.

ABSTRACT: As the materials science community seeks to capitalize on recent advancements in computer science, the sparsity of well-labelled experimental data and limited throughput by which it can be generated have inhibited deployment of machine learning algorithms to date. Several successful examples in computational chemistry have inspired further adoption of machine learning algorithms, and in the present work we present autoencoding algorithms for measured optical properties of metal oxides, which can serve as an exemplar for the breadth and depth of data required for modern algorithms to learn the underlying structure of experimental materials science data. Our set of 178 994 distinct materials samples spans 78 distinct composition spaces, includes 45 elements, and contains more than 80 000 uni

SUBMITTER: Stein HS 

PROVIDER: S-EPMC6334722 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature