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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 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 and T  C (their 'sequencing'). We use this information to develop predictive models that can suggest new structure/chemistries and/or properties. In this manner, BiTmO 3 -PbTiO 3 and BiLuO 3 -PbTiO 3 are predicted to have a T  C of 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.

Identifying the ‘inorganic gene’ for high-temperature piezoelectric perovskites through statistical learning