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Title: Defect Genome of Cubic Perovskites for Fuel Cell Applications

Abstract

Heterogeneities such as point defects, inherent to material systems, can profoundly influence material functionalities critical for numerous energy applications. This influence in principle can be identified and quantified through development of large defect data sets which we call the defect genome, employing high-throughput ab initio calculations. However, high-throughput screening of material models with point defects dramatically increases the computational complexity and chemical search space, creating major impediments toward developing a defect genome. In this paper, we overcome these impediments by employing computationally tractable ab initio models driven by highly scalable workflows, to study formation and interaction of various point defects (e.g., O vacancies, H interstitials, and Y substitutional dopant), in over 80 cubic perovskites, for potential proton-conducting ceramic fuel cell (PCFC) applications. The resulting defect data sets identify several promising perovskite compounds that can exhibit high proton conductivity. Furthermore, the data sets also enable us to identify and explain, insightful and novel correlations among defect energies, material identities, and defect-induced local structural distortions. Finally, such defect data sets and resultant correlations are necessary to build statistical machine learning models, which are required to accelerate discovery of new materials.

Authors:
ORCiD logo [1];  [2];  [1]; ORCiD logo [3];  [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Materials Science and Technology Division
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Chemical Sciences Division
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Org.:
USDOE Office of Science (SC); USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1424465
Grant/Contract Number:  
[AC05-00OR22725]
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Physical Chemistry. C
Additional Journal Information:
[ Journal Volume: 121; Journal Issue: 48]; Journal ID: ISSN 1932-7447
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; 30 DIRECT ENERGY CONVERSION; high-throughput; materials genome; defect genome; perovskites; point defects; fuel cells; proton conductors; oxides

Citation Formats

Balachandran, Janakiraman, Lin, Lianshan, Anchell, Jonathan S., Bridges, Craig A., and Ganesh, P. Defect Genome of Cubic Perovskites for Fuel Cell Applications. United States: N. p., 2017. Web. doi:10.1021/acs.jpcc.7b08716.
Balachandran, Janakiraman, Lin, Lianshan, Anchell, Jonathan S., Bridges, Craig A., & Ganesh, P. Defect Genome of Cubic Perovskites for Fuel Cell Applications. United States. doi:10.1021/acs.jpcc.7b08716.
Balachandran, Janakiraman, Lin, Lianshan, Anchell, Jonathan S., Bridges, Craig A., and Ganesh, P. Tue . "Defect Genome of Cubic Perovskites for Fuel Cell Applications". United States. doi:10.1021/acs.jpcc.7b08716. https://www.osti.gov/servlets/purl/1424465.
@article{osti_1424465,
title = {Defect Genome of Cubic Perovskites for Fuel Cell Applications},
author = {Balachandran, Janakiraman and Lin, Lianshan and Anchell, Jonathan S. and Bridges, Craig A. and Ganesh, P.},
abstractNote = {Heterogeneities such as point defects, inherent to material systems, can profoundly influence material functionalities critical for numerous energy applications. This influence in principle can be identified and quantified through development of large defect data sets which we call the defect genome, employing high-throughput ab initio calculations. However, high-throughput screening of material models with point defects dramatically increases the computational complexity and chemical search space, creating major impediments toward developing a defect genome. In this paper, we overcome these impediments by employing computationally tractable ab initio models driven by highly scalable workflows, to study formation and interaction of various point defects (e.g., O vacancies, H interstitials, and Y substitutional dopant), in over 80 cubic perovskites, for potential proton-conducting ceramic fuel cell (PCFC) applications. The resulting defect data sets identify several promising perovskite compounds that can exhibit high proton conductivity. Furthermore, the data sets also enable us to identify and explain, insightful and novel correlations among defect energies, material identities, and defect-induced local structural distortions. Finally, such defect data sets and resultant correlations are necessary to build statistical machine learning models, which are required to accelerate discovery of new materials.},
doi = {10.1021/acs.jpcc.7b08716},
journal = {Journal of Physical Chemistry. C},
number = [48],
volume = [121],
place = {United States},
year = {2017},
month = {10}
}

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