Pattern Learning Electronic Density of States.

ABSTRACT: Electronic density of states (DOS) is a key factor in condensed matter physics and material science that determines the properties of metals. First-principles density-functional theory (DFT) calculations have typically been used to obtain the DOS despite the considerable computation cost. Herein, we report a fast machine learning method for predicting the DOS patterns of not only bulk structures but also surface structures in multi-component alloy systems by a principal component analysis. Within this framework, we use only four features to define the composition, atomic structure, and surfaces of alloys, which are the d-orbital occupation ratio, coordination number, mixing factor, and the inverse of miller indices. While the DFT method scales as O(N³) in which N is the number o

SUBMITTER: Yeo BC 

PROVIDER: S-EPMC6458116 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature