A Theoretical Study of Oxygen Anion Diffusion through the A-Site Deficient SrTiO3 Lattice Structure: A Machine Learning Approach

The performance of strontium titanate-based perovskite materials, widely employed as electrode materials for reversible solid oxide cells, is directly characterized by their efficiency and their ability to facilitate the diffusion of generated oxygen ions. A technique frequently employed for enhancing oxygen ion diffusivity involves artificially generating A-site vacancies in these structures. In this study, the molecular-level mechanism of oxygen ion diffusion for a range of A-site deficient structures is extensively investigated using combined molecular dynamics simulations and machine learning-based technique. The analysis of molecular simulation trajectories yields diffusion parameters for the bulk system. Additionally, clustering analysis of time-overlapped locations of oxygen ions provides a spatial distribution of oxygen ion dislocations. Concurrently, tracking the motion of individual oxygen ions elucidates the contribution of each ion to the overall ionic conductance. Overall, the systematic generation of A-site deficiency is found to significantly influence oxygen ion dislocations, thereby impacting the performance of these materials in terms of oxide ion conduction.

Oxygen Migration Pathways in Layered LnBaCo2O6-δ (Ln = La - Y) Perovskites

Layered LnBaCo2O6-δ perovskites are important mixed ionic-electronic conductors, exhibiting outstanding catalytic properties for the oxygen evolution/reduction reaction. These phases exhibit considerable structural complexity, in particular, near room temperature, where a number of oxygen vacancy ordered superstructures are found. This study uses bond valence site energy calculations to demonstrate the key underlying structural features that favor facile ionic migration. BVSE calculations show that the 1D vacancy ordering for Ln = Sm-Tb could be beneficial at low temperatures as new pathways with reduced barriers emerge. By contrast, the 2D vacancy ordering for Ln = Dy and Y is not beneficial for ionic transport with the basic layered parent material having lower migration barriers. Overall, the key criterion for low migration barriers is an expanded ab plane, supported by Ba, coupled to a small Ln size. Hence, Ln = Y should be the best composition, but this is stymied by the low temperature 2D vacancy ordering and moderate temperature stability. The evolution of the oxygen cycling capability of these materials is also reported.

Controlling surface cation segregation in a double perovskite for oxygen anion transport in high temperature energy conversion devices

Double perovskite materials have shown promising applications as an electrode in solid oxide fuel cells and Li-air batteries for oxygen reduction, evolution, and transport. However, degradation of the material due to cation migration to the surface, forming secondary phases, poses an existential bottleneck in materials development. Herein, a theoretical approach combining density functional theory and molecular dynamics simulations is presented to study the Ba-cation segregation in a double perovskite NdBaCo2O5+δ. Solutions to circumvent segregation at the molecular level are presented in two different forms by applying strain and introducing dopants in the structure. On applying compressive strain or Ca as a dopant in the NBCO structure, segregation is estimated to reduce significantly. A more direct way of estimating cation segregation is proposed in MD simulations, wherein the counting of the cations migrating from the sub-surface layers to the surface provided a reliable theoretical assessment of the level of cation segregation.

Nonstoichiometry, Defect Chemistry and Oxygen Transport in Fe-Doped Layered Double Perovskite Cobaltite PrBaCo2-xFexO6-δ (x = 0-0.6) Membrane Materials

Mixed conducting cobaltites PrBaCo_2-xFe_xO_6-δ (x = 0-0.6) with a double perovskite structure are promising materials for ceramic semi-permeable membranes for oxygen separation and purification due to their fast oxygen exchange and diffusion capability. Here, we report the results of the detailed study of an interplay between the defect chemistry, oxygen nonstoichiometry and oxygen transport in these materials as a function of iron doping. We show that doping leads to a systematic variation of both the thermodynamics of defect formation reactions and oxygen transport properties. Thus, iron doping can be used to optimize the performance of mixed conducting oxygen-permeable double perovskite membrane materials.

Insights into Oxygen Migration in LaBaCo2O6-δ Perovskites from In Situ Neutron Powder Diffraction and Bond Valence Site Energy Calculations

Layered cobalt oxide perovskites are important mixed ionic and electronic conductors. Here, we investigate LaBaCo₂O_6-δ using in situ neutron powder diffraction. This composition is unique because it can be prepared in cubic, layered, and vacancy-ordered forms. Thermogravimetric analysis and diffraction reveal that layered and disordered samples have near-identical oxygen cycling capacities. Migration barriers for oxide ion conduction calculated using the bond valence site energy approach vary from E _b ∼ 2.8 eV for the cubic perovskite to E _b ∼ 1.5 eV for 2D transport in the layered system. Vacancy-ordered superstructures were observed at low temperatures, 350-400 °C for δ = 0.25 and δ = 0.5. The vacancy ordering at δ = 0.5 is different from the widely reported structure and involves oxygen sites in both CoO₂ and LaO planes. Vacancy ordering leads to the emergence of additional migration pathways with low-energy barriers, for example, 1D channels with E _b = 0.5 eV and 3D channels with E _b = 2.2 eV. The emergence of these channels is caused by the strong orthorhombic distortion of the crystal structure. These results demonstrate that there is potential scope to manipulate ionic transport in vacancy-ordered LnBaCo₂O_6-δ perovskites with reduced symmetry.

JAMIP: an artificial-intelligence aided data-driven infrastructure for computational materials informatics

Materials informatics has emerged as a promisingly new paradigm for accelerating materials discovery and design. It exploits the intelligent power of machine learning methods in massive materials data from experiments or simulations to seek new materials, functionality, and principles, etc. Developing specialized facilities to generate, collect, manage, learn, and mine large-scale materials data is crucial to materials informatics. We herein developed an artificial-intelligence-aided data-driven infrastructure named Jilin Artificial-intelligence aided Materials-design Integrated Package (JAMIP), which is an open-source Python framework to meet the research requirements of computational materials informatics. It is integrated by materials production factory, high-throughput first-principles calculations engine, automatic tasks submission and monitoring progress, data extraction, management and storage system, and artificial intelligence machine learning based data mining functions. We have integrated specific features such as an inorganic crystal structure prototype database to facilitate high-throughput calculations and essential modules associated with machine learning studies of functional materials. We demonstrated how our developed code is useful in exploring materials informatics of optoelectronic semiconductors by taking halide perovskites as typical case. By obeying the principles of automation, extensibility, reliability, and intelligence, the JAMIP code is a promisingly powerful tool contributing to the fast-growing field of computational materials informatics.

Identification of oxygen diffusion mechanisms in Nd1-xAExBaInO4-x/2 (AE = Ca, Sr, Ba) compounds through molecular dynamics

Molecular dynamics simulations have been widely adopted to study oxygen ion diffusion mechanisms in materials for application in solid oxide fuel cells. Indeed, understanding the fundamental aspects of oxygen diffusion is important to develop new materials for this application. In this work, Nd1-xAExBaInO4-x/2 (AE = Ca, Sr, Ba) compounds have been studied by MD simulations focusing on oxygen diffusion mechanisms. Two general clustering methods were used, namely a convex hull classification method and a DBSCAN machine learning algorithm, to identify oxygen ion diffusion pathways. Here, relevant details are provided for an efficient use of these two approaches during MD analysis of ion conductors. The calculations show that Ca is the most favorable dopant for substituting Nd in NdBaInO4, while Ba is the least desired. Indeed, the substitution of Nd by Ca hardly changes the pristine lattice parameters of NdBaInO4 and leads to the highest oxygen diffusion coefficient compared to other dopants. The oxygen vacancies induced by doping mainly locate on two specific oxygen sites over four oxygen sites available. Concerning the diffusion process, jumps involving these two sites play the main role and are associated with smaller migration enthalpies. For the main diffusion path, ions migrate along the b (2 routes) and c (4 routes) directions. Some other oxygen sites can be considered as barriers for the diffusion process inducing a strong anisotropy in the diffusion process. Additionally, the residence time analysis of oxygen ions confirms that ions at different sites have different motion abilities. As a whole, the approach presented here can be extrapolated to other ion conductors for gaining detailed information about the diffusion process.

Identifying the Origin of the Limiting Process in a Double Perovskite PrBa0.5Sr0.5Co1.5Fe0.5O5+δ Thin-Film Electrode for Solid Oxide Fuel Cells

Oxygen reduction reaction in a double perovskite material, PrBa_0.5Sr_0.5Co_1.5Fe_0.5O_5+δ (PBSCF), was studied for application as a cathode in a solid oxide fuel cell (SOFC). Electrochemical measurements were performed on a geometrically well-defined dense thin-film (0.8-2 μm thickness) electrode, fabricated as a symmetric cell. In combination with density functional theory (DFT) and molecular dynamics (MD) simulations, experiments provided an insight into the operating mechanism of the SOFC material tested at an open-circuit voltage. The dense thin-film electrode of PBSCF showed a thickness-dependent electrochemical performance, suggesting bulk diffusion limitation. To understand the origin of this diffusion-limiting electrochemical performance, DFT calculations were utilized to calculate the surface (γ) and oxygen vacancy formation (E_OV) energies. For example, E_OV in the Pr plane (190 kJ/mol) of PBSCF was measured to be lower than that of the BaSr plane (E_OV = 297 kJ/mol). In addition, oxygen vacancies were difficult to be created in the BaSr/CoFe terminal surface (E_OV = 341.6 kJ/mol) as compared to other terminal surfaces. MD simulations further elaborated on the nature of cation disordering in the surface and subsurface regions, consequently leading to the preferential segregation of the Ba cations to the surface, which is a known phenomenon in such double perovskite materials. Because of cation disordering and segregation of Ba species, the oxygen anion diffusivity (∼10^-12 cm² s^-1), calculated from MD, in the near-surface region was observed to be 2 orders of magnitude lesser than that of the bulk (D = 2.98 × 10^-10 cm² s^-1) of the material at 973 K. Surface characterization of the thin-film electrode using X-ray photoelectron spectroscopy was indicative of a nonperovskite Ba²⁺ phase on the electrode surface. The segregation of Ba cations was linked with the transport of oxygen anions, which was limiting the electrochemical performance of the electrode.

First-Principles Study of Anisotropic Oxygen Diffusion in PrBaCo2O5.5

We address the anisotropic oxygen diffusion in PrBaCo2O5.5 using first-principles calculations based on the density functional theory. First, the experimentally observed magnetic properties such as ferromagnetic, ferrimagnetic, and paramagnetic phases are examined through systematic consideration of cobalt spin ordering and oxygen vacancy position. Then, the diffusion mechanism of an oxygen atom, assumed to be externally supplied, is explored by evaluating the oxygen migration barriers with the formation of one-dimensional oxygen-vacancy channel.

Data mining of molecular dynamics data reveals Li diffusion characteristics in garnet Li7La3Zr2O12

Understanding Li diffusion in solid conductors is essential for the next generation Li batteries. Here we show that density-based clustering of the trajectories computed using molecular dynamics simulations helps elucidate the Li diffusion mechanism within the Li₇La₃Zr₂O₁₂ (LLZO) crystal lattice. This unsupervised learning method recognizes lattice sites, is able to give the site type, and can identify Li hopping events. Results show that, while the cubic LLZO has a much higher hopping rate compared to its tetragonal counterpart, most of the Li hops in the cubic LLZO do not contribute to the diffusivity due to the dominance of back-and-forth type jumps. The hopping analysis and local Li configuration statistics give evidence that Li diffusivity in cubic LLZO is limited by the low vacancy concentration. The hopping statistics also shows uncorrelated Poisson-like diffusion for Li in the cubic LLZO, and correlated diffusion for Li in the tetragonal LLZO in the temporal scale. Further analysis of the spatio-temporal correlation using site-to-site mutual information confirms the weak site dependence of Li diffusion in the cubic LLZO as the origin for the uncorrelated diffusion. This work puts forward a perspective on combining machine learning and information theory to interpret results of molecular dynamics simulations.

Unraveling the effect of La A-site substitution on oxygen ion diffusion and oxygen catalysis in perovskite BaFeO3by data-mining molecular dynamics and density functional theory

The effects of La substitution on oxygen transport and catalysis in BaFeO₃are unraveled by data-driven molecular dynamics and density functional theory.