Journal article
Learning from data to design functional materials without inversion symmetry
Nature communications, v 8(1), pp 14282-14282
17 Feb 2017
PMID: 28211456
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Accelerating the search for functional materials is a challenging problem. Here we develop an informatics-guided ab initio approach to accelerate the design and discovery of noncentrosymmetric materials. The workflow integrates group theory, informatics and density-functional theory to uncover design guidelines for predicting noncentrosymmetric compounds, which we apply to layered Ruddlesden-Popper oxides. Group theory identifies how configurations of oxygen octahedral rotation patterns, ordered cation arrangements and their interplay break inversion symmetry, while informatics tools learn from available data to select candidate compositions that fulfil the group-theoretical postulates. Our key outcome is the identification of 242 compositions after screening similar to 3,200 that show potential for noncentrosymmetric structures, a 25-fold increase in the projected number of known noncentrosymmetric Ruddlesden-Popper oxides. We validate our predictions for 19 compounds using phonon calculations, among which 17 have noncentrosymmetric ground states including two potential multiferroics. Our approach enables rational design of materials with targeted crystal symmetries and functionalities.
Metrics
Details
- Title
- Learning from data to design functional materials without inversion symmetry
- Creators
- Prasanna V. Balachandran - Los Alamos National LaboratoryJoshua Young - Drexel UniversityTurab Lookman - Los Alamos National LaboratoryJames M. Rondinelli - Northwestern University
- Publication Details
- Nature communications, v 8(1), pp 14282-14282
- Publisher
- NATURE PORTFOLIO
- Number of pages
- 13
- Grant note
- 20140013DR / Los Alamos National Laboratory (LANL) LDRD on Materials Informatics; United States Department of Energy (DOE); Los Alamos National Laboratory Center for Nonlinear Studies (CNLS) 1454688 / Division Of Materials Research; National Science Foundation (NSF); NSF - Directorate for Mathematical & Physical Sciences (MPS) DMR-1454688; DMR-1420620 / NSF; National Science Foundation (NSF)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Materials Science and Engineering
- Web of Science ID
- WOS:000394241900001
- Scopus ID
- 2-s2.0-85013155720
- Other Identifier
- 991019330795104721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Materials Science, Multidisciplinary