Defect Genome of Cubic Perovskites for Fuel Cell Applications

Balachandran, Janakiraman; Lin, Lianshan; Anchell, Jonathan S.; Bridges, Craig A.; Ganesh, P.

doi:10.1021/acs.jpcc.7b08716

Title: Defect Genome of Cubic Perovskites for Fuel Cell Applications

Journal Article · Tue Oct 10 00:00:00 EDT 2017 · Journal of Physical Chemistry. C

DOI: https://doi.org/10.1021/acs.jpcc.7b08716 · OSTI ID:1424465

^[1]; Lin, Lianshan ^[2]; Anchell, Jonathan S. ^[1];

^[3]; Ganesh, P. ^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Materials Science and Technology Division
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Chemical Sciences Division

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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)

Sponsoring Organization:: USDOE Office of Science (SC); USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1424465

Journal Information:: Journal of Physical Chemistry. C, Journal Name: Journal of Physical Chemistry. C Journal Issue: 48 Vol. 121; ISSN 1932-7447

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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Title: Defect Genome of Cubic Perovskites for Fuel Cell Applications

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