Data mining density of states spectra for crystal structure classification: An inverse problem approach

Abstract The ability to model the density of states has been a long‐standing problem in condensed matter physics. The classical methods that have been used are based on a variety of approaches, ranging from maximum entropy methods to recursion methods involving high dimensional data. In this work, we classify the crystal structure of an alloy based on the electronic structure, the inverse process of first principles calculations which calculate the electronic structure from crystal structure‐based inputs. Here the electronic structure is represented by the density of states, and the classification of crystal structures is achieved through data mining. In addition to classifying alloys by crystal structure, we can classify alloys based on the degree of tetragonality and stoichiometry using solely the density of states spectra. This work seeks to describe the relationship between crystal and electronic structure based on a quantitative interpretation of the density of states, while discussing how capturing the principal contributions of the density of states suggests future work in modeling the density of states using less computationally expensive data mining techniques. © 2009 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 1: 000‐000, 2009