期刊
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
卷 467, 期 2132, 页码 2271-2290出版社
ROYAL SOC
DOI: 10.1098/rspa.2010.0543
关键词
inorganic gene; high-temperature piezoelectrics; statistical learning; information theory; data-driven modelling
资金
- Air Force Office of Scientific Research [FA9550-06-10501, FA9550-08-1-0316]
- National Science Foundation [DMR-08-33853]
- NSF-ARI Program [CMMI 09-389018]
- NSF-CDI Type II program [PHY 09-41576]
- NSF-AF [CCF09-17202]
- DARPA [HR0011-06-0049]
- Army Research Office [W911NF-10-0397]
- Iowa State University
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [0751157] Funding Source: National Science Foundation
- Division Of Physics
- Direct For Mathematical & Physical Scien [0941576] Funding Source: National Science Foundation
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [0938918] Funding Source: National Science Foundation
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, BiTmO3-PbTiO3 and BiLuO3-PbTiO3 are predicted to have a T-C of 730 degrees C and 705 degrees 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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据