Authors: Panchapakesan Ganesh
Source: ECS Meeting Abstracts (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

(Invited) Functional Defects By Design—a High-Throughput Approach to Energy Materials Discovery Defects and impurities introduce localized heterogeneities in solids and decisively control the behavior of a wide range of energy technologies. Fuel cell materials, especially proton conducting fuel cells, are a quintessential example in this regard. Designing and developing solid oxide materials that can selectively transport protons will enable us to develop the next generation proton conducting solid oxide fuel cells. Protons require less activation energy compared to oxygen ions which results in a lower operating temperature, higher operating efficiency and better material reliability. In this work [1,2,3,4] we focus obtaining fundamental insights on how properties of host material structure along with dopants, disorder and strain influences proton transport properties in solid oxides by coupling high-throughput computations with functional imaging, neutron spectroscopy and transport measurements. We initially focus on the perovskite family of compounds (such as doped BaZrO 3 ). We benchmark our calculations against a wide range of experimental measurements such as kelvin probe force microscopy (KPFM), inelastic neutron scattering (INS) and atom probe tomography (APT). To obtain better insights on why certain cubic perovskite/dopant combinations are better at conducting protons compared to others, we developed a high-throughput framework to perform ab initio calculations. The high-throughput framework can scale massively to tens of thousands of nodes to fully exploit the computational capability of the Oak Ridge Leadership Computing facility. We employ this approach to calculate proton transport properties in several cubic perovskite materials with different host atoms and dopants. The results obtained from these calculations enables us to obtain better insights on how material structure – such as atomic properties (electronegativity, ionic radius) and lattice properties (sub-lattice distortion) influences proton transport. The results obtained from this high-throughput analysis is being employed to develop a machine learning framework to predict structure-property correlations on a larger set of perovskites materials. Finally, we explore the role of disorder on proton transport by studying for example fluorite based lanthanum tungstate materials. [1] “Defect Genome of Cubic Perovskites for Fuel Cell Applications”, Journal of Physical Chemistry C, 121, 26637 (2017) [2] “The Influence of Local Distortions on Proton Mobility in Acceptor Doped Perovskites”, Chemistry of Materials, 2018, 30 (15), pp 4919–4925 [3]“The Influence of the Local Structure on Proton Transport in a Solid Oxide Proton Conductor La0.8Ba1.2GaO3.9”, J. Mat. Chem. A, 5, 15507 (2017) [4] “Influence of Non-Stoichiometry on Proton Conductivity in Thin Film Yttrium-doped Barium Zirconate”, ACS Appl. Mater. Interfaces, 2018, 10 (5), pp 4816–4823 Materials science ; Dopant ; Perovskite (structure) ; Nanotechnology ; Chemical physics ; Doping ; Chemistry ; Optoelectronics ; Crystallography Machine Learning in Materials Science