Defect Genome of Cubic Perovskites for Fuel Cell Applications

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

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

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 work, 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. Such defect data sets and resultant correlations are necessary to build statistical machine learning models, which are required to accelerate discovery of new materials.

Original languageEnglish
Pages (from-to)26637-26647
Number of pages11
JournalJournal of Physical Chemistry C
Volume121
Issue number48
DOIs
StatePublished - Dec 7 2017

Funding

This research was sponsored by the Laboratory Directed Research and Development Program (LDRD) of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U.S. Department of Energy. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. We thank the developers of wraprun, Matt Belhorn and Adam Simpson, for their help and support in enabling the integration of wraprun with the workflow.

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