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Combinatorial and High Throughput Discovery of High Temperature Piezoelectric Ceramics.

机译：高温压电陶瓷的组合和高通量发现。

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This project has developed a statistical learning approach to identify potential new high temperature ferroelectric piezoelectric perovskite compounds. Our predictions of the Curie temperature (Tc) ranging from 700C- 1100C are the highest reported in either experimental or theoretical studies and the number of new proposed compounds based on our work nearly doubles the known candidate piezoelectric ferroelectric perovskites. 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. In parallel to the computational studies, we have developed a detailed experimental protocol for exploring the processing parameter space for synthesizing in a combinatorial fashion of known existing piezoelectric perovskites. We not only identified new compound chemistries with TC exceeding known ferroelectric perovskite materials but also optimized the site occupancy of newly the identified new compound chemistries. This provides strong evidence of the value of informatics to significantly accelerate materials discovery. Our experimental studies also provided us the framework to test our informatics based predictions. For instance we have synthesized BiLuO3- PbTiO3 and discovered that the system is indeed a perovskite structure as per our predictions and our measured Tc of approx. 550C though lower than the approx. 700C of our predictions, is one of the highest reported experimental measurements reported in the literature. Given that our models are based solely on crystal and electronic structure data and did not microstructural or impurity chemistry information, this is a very promising result. To the best of our knowledge, this is the first direct attempt to experimentally validate informatics based structure-property predictions.

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