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 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.

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“…In order to study these effects, one might consider doing the defect calculations in a systematic way, for example as described in Ref. [55].…”

Machine learned features from density of states for accurate adsorption energy prediction

“…Following the development of robust quantum chemistry software and the availability of high-performance computing, high-throughput screening is now an increasingly widespread approach for materials discovery 1 – 7 . The field of heterogeneous catalysis is no exception to this trend 8 – 11 .…”

Structure-mining: screening structure models by automated fitting to the atomic pair distribution function over large numbers of models

“…The perovskite structures with B site element replaced by one with a significantly different x-ray scattering power than Ti resulted in significantly poorer R w , away from the best-fit structures by ∆R w ∼ 0.15, such as MPD No. 1482 (BaRhO 3 ) (Balachandran et al, 2017) and COD No. 431 (BaNbO 3 ) (Grin et al, 2014).…”