title: 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 (TC) 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 to discover the dominant descriptors governing structure-property correlations (the 'genes') and Shannon entropy metrics coupled to recursive partitioning methods to quantitatively assess the specific combination of descriptors that govern the link between crystal chemistry andTC(their 'sequencing'). We use this information to develop predictive models that can suggest new structure/chemistries and/or properties. In this manner, BiTmO3-PbTiO3and BiLuO3-PbTiO3are predicted to have aTCof 730°C and 705°C, respectively. A quantitative structure-property relationship model similar to those used in biology and drug discovery not only predicts our new chemistries but also validates published reports.
authors: Department of Materials Science and Engineering and Institute for Combinatorial Discovery , Iowa State University , Ames, IA 50011, USA.
authors: Prasanna V
authors: PV
authors: Balachandran
authors: Department of Materials Science and Engineering and Institute for Combinatorial Discovery , Iowa State University , Ames, IA 50011, USA.
authors: Scott R
authors: SR
authors: Broderick
authors: Department of Materials Science and Engineering and Institute for Combinatorial Discovery , Iowa State University , Ames, IA 50011, USA.
authors: Krishna
authors: K
authors: Rajan
keywords: data-driven modelling; high-temperature piezoelectrics; information theory; inorganic gene; statistical learning