Dataset Information

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 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^°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.

SUBMITTER: Balachandran PV

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

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Balachandran Prasanna V PV Broderick Scott R SR Rajan Krishna K

Proceedings. Mathematical, physical, and engineering sciences 20110302 2132

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 te ...[more]

PMID: 24959095

Similar Datasets

Project description:Hybrid organic-inorganic perovskites (HOIPs) are crystals with the structural formula ABX3, where A, B, and X are organic and inorganic ions, respectively. While known for several decades, HOIPs have only in recent years emerged as extremely promising semiconducting materials for solar energy applications. In particular, power-conversion efficiencies of HOIP-based solar cells have improved at a record speed and, after only little more than 6 years of photovoltaics research, surpassed the 20% threshold, which is an outstanding result for a solution-processable material. It is thus of fundamental importance to reveal physical and chemical phenomena that contribute to, or limit, these impressive photovoltaic efficiencies. To understand charge-transport and light-absorption properties of semiconducting materials, one often invokes a lattice of ions displaced from their static positions only by harmonic vibrations. However, a preponderance of recent studies suggests that this picture is not sufficient for HOIPs, where a variety of structurally dynamic effects, beyond small harmonic vibrations, arises already at room temperature. In this Account, we focus on these effects. First, we review structure and bonding in HOIPs and relate them to the promising charge-transport and absorption properties of these materials, in terms of favorable electronic properties. We point out that HOIPs are much "softer" mechanically, compared to other efficient solar-cell materials, and that this can result in large ionic displacements at room temperature. We therefore focus next on dynamic structural effects in HOIPs, going beyond a static band-structure picture. Specifically, we discuss pertinent experimental and theoretical findings as to phase-transition behavior and molecular/octahedral rearrangements. We then discuss atomic diffusion phenomena in HOIPs, with an emphasis on the migration of intrinsic and extrinsic ionic species. From this combined perspective, HOIPs appear as highly dynamic materials, in which structural fluctuations and long-range ionic motion have an unusually strong impact on charge-transport and optical properties. We highlight the potential implications of these effects for several intriguing phenomenological observations, ranging from scattering mechanisms and lifetimes of charge carriers to light-induced structural effects and ionic conduction.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets