Proton Channels along Oxygen Octahedral Chains in La<sub>3</sub>NbO<sub>7</sub>

Kato, Kunitada; Toyoura, Kazuaki; Nakamura, Atsutomo; Matsunaga, Katsuyuki

doi:10.1021/jp501791r

Cited by 12 publications

(21 citation statements)

References 30 publications

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“…C was estimated from the typical doping level (10% in this system), 1,2 d was set to be the distance between the adjacent most-stable hydroxide-ion sites (3.2Å), and n 0 was set at the typical frequency of 10 THz. 17,[34][35][36][37][38] In most cases, the calculated potential barriers for minimum energy pathways are comparable with experimental activation energies, and it can be thus said that the current DFT-based calculations predict theoretical potential barriers for ionic conduction that reasonably explain the experimental results. This is reasonable from the present FPMD simulations showing that the migration events during the FPMD are limited to twice in 100 ps.…”

Section: Hydroxide Ion Conduction Mechanismssupporting

confidence: 55%

Hydroxide-ion incorporation and conduction mechanisms in tin pyrophosphate – a first-principles study

et al. 2015

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The incorporation and conduction mechanisms of hydroxide ions in tin pyrophosphate (SnP 2 O 7 ) have been investigated theoretically on the basis of first-principles calculations. Hydroxide ions are not simply incorporated into interstitial sites in the crystal, and the incorporation is accompanied by dissociation of a P 2 O 7 unit (pyrophosphate ion) into two PO 4 units. From the calculated formation energies of various native defects, it was found that the interstitial proton and hydroxide ion are the dominant defect species and their concentrations can be controlled by doping heterovalent cations. The long-range migration of the hydroxide ion was observed in the first-principles molecular dynamics simulations, but its frequency during the simulation was extremely low. In fact, the calculated potential barrier for the conduction pathway was extremely high (1.92 eV), and the estimated bulk conductivity in SnP 2 O 7 was much lower than the experimentally reported conductivities. These results indicate that the experimentally observed hydroxide-ion conductivity of SnP 2 O 7 cannot be simply explained by motion of hydroxide ions through the crystal lattice.

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Section: Hydroxide Ion Conduction Mechanismssupporting

confidence: 55%

Hydroxide-ion incorporation and conduction mechanisms in tin pyrophosphate – a first-principles study

et al. 2015

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“…7 In this contribution, centrality measures are used to gain insight into ion conduction in solids. Mechanisms of conduction and activation barriers for transport of protons [8][9][10] and other ions 11,12 are sometimes determined based on conduction pathways built by concatenating single-step ion transfers between sites. This is true for many solid state conductors.…”

Section: Introductionmentioning

confidence: 99%

Centrality measures highlight proton traps and access points to proton highways in kinetic Monte Carlo trajectories

Krueger

Haibach²,

Fry

et al. 2015

The Journal of Chemical Physics

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A centrality measure based on the time of first returns rather than the number of steps is developed and applied to finding proton traps and access points to proton highways in the doped perovskite oxides: AZr 0.875 D 0.125 O 3 , where A is Ba or Sr and the dopant D is Y or Al. The high centrality region near the dopant is wider in the SrZrO 3 systems than the BaZrO 3 systems. In the aluminum-doped systems, a region of intermediate centrality (secondary region) is found in a plane away from the dopant. Kinetic Monte Carlo (kMC) trajectories show that this secondary region is an entry to fast conduction planes in the aluminum-doped systems in contrast to the highest centrality area near the dopant trap. The yttrium-doped systems do not show this secondary region because the fast conduction routes are in the same plane as the dopant and hence already in the high centrality trapped area. This centrality measure complements kMC by highlighting key areas in trajectories. The limiting activation barriers found via kMC are in very good agreement with experiments and related to the barriers to escape dopant traps. C 2015 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 Unported License. [http://dx

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“…The feasible set is the grid points on a 20×10×8 grid (grid interval: 0.25 ~ 0.3 Å), from which the grid points close to the host atoms are excluded, leading to 921 grid points in total. The second type of search space has lower dimensions by exploiting prior knowledge on protons in oxides, i.e., an OH bond formation in oxides [38][39][40][41][42][43][44][45]. Specifically, a spherical grid around an oxygen ion is introduced with the radius of 1 Å.…”

Section: A Definition Of the Feasible Setsmentioning

confidence: 99%

Machine-learning-based sampling method for exploring local energy minima of interstitial species in a crystal

Toyoura

Kanayama

2020

Phys. Rev. B

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An efficient machine-learning-based method combined with a conventional local optimization technique has been proposed for exploring local energy minima of interstitial species in a crystal. In the proposed method, an effective initial point for local optimization is sampled at each iteration from a given feasible set in the search space. The effective initial point is here defined as the grid point that most likely converges to a new local energy minimum by local optimization and/or is located in the vicinity of the boundaries between energy basins. Specifically, every grid point in the feasible set is classified by the predicted label indicating the local energy minimum that the grid point converges to.The classifier is created and updated at every iteration using the already-known information on the local optimizations at the earlier iterations, which is based on the support vector machine (SVM). The SVM classifier uses our original kernel function designed as reflecting the symmetries of both host crystal and interstitial species. The most distant unobserved point on the classification boundaries from the observed points is sampled as the next initial point for local optimization. The proposed method is applied to three model cases, i.e., the six-hump camelback function, a proton in strontium zirconate with the orthorhombic perovskite structure, and a water molecule in lanthanum sulfate with the monoclinic structure, to demonstrate the performance of the proposed method.

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Proton Channels along Oxygen Octahedral Chains in La₃NbO₇

Cited by 12 publications

References 30 publications

Hydroxide-ion incorporation and conduction mechanisms in tin pyrophosphate – a first-principles study

Hydroxide-ion incorporation and conduction mechanisms in tin pyrophosphate – a first-principles study

Centrality measures highlight proton traps and access points to proton highways in kinetic Monte Carlo trajectories

Machine-learning-based sampling method for exploring local energy minima of interstitial species in a crystal

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Proton Channels along Oxygen Octahedral Chains in La3NbO7

Cited by 12 publications

References 30 publications

Hydroxide-ion incorporation and conduction mechanisms in tin pyrophosphate – a first-principles study

Hydroxide-ion incorporation and conduction mechanisms in tin pyrophosphate – a first-principles study

Centrality measures highlight proton traps and access points to proton highways in kinetic Monte Carlo trajectories

Machine-learning-based sampling method for exploring local energy minima of interstitial species in a crystal

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Proton Channels along Oxygen Octahedral Chains in La₃NbO₇