The application of Principal Component Analysis to materials science data

Abstract

The relationship between apparently disparate sets of data is a critical component of interpreting materials' behavior, especially in terms of assessing the impact of the microscopic characteristics of materials on their macroscopic or engineering behavior. In this paper we demonstrate the value of principal component analysis of property data associated with high temperature superconductivity to examine the statistical impact of the materials' intrinsic characteristics on high temperature superconducting behavior

Keywords

• Combinatorial response maps
• Data mining
• High temperature superconductors
• Materials science
• Principal component analysis (PCA)