Identifying the 'inorganic gene' for high-temperature piezoelectric perovskites through statistical learning.

ABSTRACT: This paper develops a statistical learning approach to identify potentially new high-temperature ferroelectric piezoelectric perovskite compounds. Unlike most computational studies on crystal chemistry, where the starting point is some form of electronic structure calculation, we use a data-driven approach to initiate our search. This is accomplished by identifying patterns of behaviour between discrete scalar descriptors associated with crystal and electronic structure and the reported Curie temperature (T_C) of known compounds; extracting design rules that govern critical structure-property relationships; and discovering in a quantitative fashion the exact role of these materials descriptors. Our approach applies linear manifold methods for data dimensionality reduction

SUBMITTER: Balachandran PV 

PROVIDER: S-EPMC4042451 | biostudies-literature | 2011 Aug

REPOSITORIES: biostudies-literature