Dynamic Simulation of the Migration of Oxygen Vacancy Defects in Rutile TiO<sub>2</sub>

Knaup, Jan M.; Wehlau, Michael; Frauenheim, Thomas

doi:10.1557/opl.2012.936

Cited by 3 publications

(5 citation statements)

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“…To avoid the known influence of thermostats on non‐equilibrium dynamics 27, the simulations were then continued in a microcanonic (NVE) ensemble for further 10 ps. We use a time step of 1 fs which was shown to be sufficient for TiO 2 MD at high temperatures 20 and the well tested tiorg 28 set of DFTB parameters. We model rutile with different oxygen deficiencies, by using a 3 × 3 × 6 supercell with 324 atoms, randomly removing 0 O, 15 O, 20 O or 27 O atoms, which corresponds to 0%, 6.9%, 9.3% and 12.5% oxygen deficiency, the latter being equivalent to Ti 4 O 7 stoichiometry.…”

Section: Methods and Modelsmentioning

confidence: 99%

“…The mechanism of this phase transition and the oxygen vacancy aggregation which has to precede it is not very well understood so far. Very short switching times in memristor devices indicate vacancy drift velocities which are faster than the relatively high hopping barriers in rutile 20, 21 suggest. Experimental findings indicate very strong Joule heating 22 and it is generally speculated that this is at least the driving force for the dissolution of the Magnéli phases 23.…”

Section: Introductionmentioning

confidence: 99%

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Reduction of the TiO_2–x melting temperature induced by oxygen deficiency with implications on experimental data accuracy and structural transition processes

Knaup

Marx

Frauenheim

2014

Physica Rapid Research Ltrs

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1 Introduction Transition metal oxides, such as TiO 2 are known to release oxygen upon heating [1][2][3][4]. This thermal self-reduction effect is of great importance to a multitude of applications, such as the manufacture of oxide electronics devices, heterogeneous catalysis, ultra high performance mechanical parts and possibly the memristive effect. In TiO 2 , the thermal self-reduction effect is employed to achieve n-type doping for semiconductor and transparent conductor applications [2 -5]. Titania, alone or in combination with metal nanoparticles, is being investigated as a catalyst for sustainable energy applications, especially water oxidation and CO 2 reduction. At the same time, Ti alloys are widely used as a structural material under conditions of high mechanical stress at high temperatures, such as gas turbine blades [6,7], and titania based ceramics are employed in high temperature applications. It was recently

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Section: Methods and Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reduction of the TiO_2–x melting temperature induced by oxygen deficiency with implications on experimental data accuracy and structural transition processes

Knaup

Marx

Frauenheim

2014

Physica Rapid Research Ltrs

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“…Yet, since a vacancy can change its position, albeit indirectly, a generalization of the vacancy position to allow the description of the transition is possible. So far, studies of vacancy migration focus on single hopping events, tracing the position of the moving neighbor atom, and implicitly defining the vacancy motion as the opposite of the ion motion [12][13][14][15] . This auxiliary definition suffers from the conceptual problem, that at or at least close to the transition state the vacancy defined as "where no ion is" coincides with the position of the moving ion.…”

Section: +mentioning

confidence: 99%

Permutation-invariant collective variable to track and drive vacancy dynamics in simulations of solids

2013

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Vacancy dynamics in oxides are vital for understanding redox reactions and resulting memristive effects or catalytic activity. We present a method to track and drive vacancies which we apply to metadynamics simulation of oxygen vacancies (V 2+ O ) in rutile, demonstrating its effectiveness. Using the density functional based tight binding method, it is possible to explore the free energy hyperplane of oxygen vacancies in TiO2. We show that the migration of V 2+O in TiO2 is governed by the jump with the highest degree of topological interconnection. Free energy profiles are consistent with minimum energy paths. PACS numbers: 61.72.jd, 66.30.Lw, 02.70.Ns Transition metal oxides exhibit a highly complex behavior, with strong coupling between electronic and ionic transport processes, making them highly interesting as electronic materials beyond their traditional use as dielectrics. The most prominent example of such effects is the memristive behavior 1 of TiO 2−x and many other substoichiometric oxides 2 . In titania, the memristive behavior is linked to the formation and dissolution of conducing filaments 1 or layers 3 of room-temperature metallic Ti n O 2n−1 Magnéli phases 4 in the insulating TiO 2 . These phases of titania are modifications of the rutile crystal structure in which oxygen vacancies (V O ) are ordered in slip-planes. In order to understand their formation and dissolution it is essential to understand the dynamics of oxygen vacancies aggregating and dispersing.Yet, the memristive effect is far from being the only interesting physics of metal oxides in which vacancy dynamics play a crucial role. Catalytic effects at oxide surfaces can rely on the presence and ability to replenish surface vacancies 5,6 or O ion transport through the catalyst 5,7 . To be efficient, the latter relies on vacancies and can in fact be mapped to a vacancy transport process in the reverse direction. Oxide based (photo)catalysis is regarded as one of the most promising fields of renewable energy conversion.These examples show that complex physical processes at the surface and in the bulk of reducible metal oxides require intimate understanding of the dynamical behavior of V O defects and the relevant driving forces. Many of these processes are rare events, i.e. their activation energy is larger than the average thermal energy at relevant conditions, which makes them inaccessible to unbiased molecular dynamics simulation. Techniques for rare event simulation8-11 rely on reaction coordinates describing the transition of interest. In most cases, the reaction coordinate is required to be continuous and at least piecewise differentiable with respect to the atomic positions r lat i . Achieving this for a vacancy coordinate r v is difficult, as vacancies are not directly simulated objects but an emergent property. In the strictest sense, the position of a vacancy cannot be continuous, as it is defined as an unoccupied lattice position. Yet, since a vacancy can change its position, albeit indirectly, a generalization of the vacanc...

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“…For example, it was found that oxygen , and acetone reactions on an R-110 surface associate with the diffused Ov. However, as far as we know, no microscopic dynamic picture of Ov diffusion has been reported at DFT-level accuracy. , …”

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confidence: 94%

“…However, as far as we know, no microscopic dynamic picture of Ov diffusion has been reported at DFTlevel accuracy. 18,19 Molecular dynamics (MD) simulations provide a powerful tool to investigate the microscopic structure of many reactions. With the strength of higher accuracy on describing atomic interactions, the ab initio molecular dynamics (AIMD) method is quite prevalent nowadays.…”

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confidence: 99%

Oxygen Vacancy Diffusion in Rutile TiO₂: Insight from Deep Neural Network Potential Simulations

Yin

Wen

et al. 2023

J. Phys. Chem. Lett.

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Defects play a crucial role in the surface reactivity and electronic engineering of titanium dioxide (TiO 2 ). In this work, we have used an active learning method to train deep neural network potentials from the ab initio data of a defective TiO 2 surface. Validations show a good consistency between the deep potentials (DPs) and density functional theory (DFT) results. Therefore, the DPs were further applied on the extended surface and executed for nanoseconds. The results show that the oxygen vacancy at various sites are very stable under 330 K. However, some unstable defect sites will convert to the most favorable ones after tens or hundreds of picoseconds, while the temperature was elevated to 500 K. The DP predicated barriers of oxygen vacancy diffusion were similar to those of DFT. These results show that machine-learning trained DPs could accelerate the molecular dynamics with a DFTlevel accuracy and promote people's understanding of the microscopic mechanism of fundamental reactions.

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Dynamic Simulation of the Migration of Oxygen Vacancy Defects in Rutile TiO₂

Cited by 3 publications

References 10 publications

Reduction of the TiO_2–x melting temperature induced by oxygen deficiency with implications on experimental data accuracy and structural transition processes

Reduction of the TiO_2–x melting temperature induced by oxygen deficiency with implications on experimental data accuracy and structural transition processes

Permutation-invariant collective variable to track and drive vacancy dynamics in simulations of solids

Oxygen Vacancy Diffusion in Rutile TiO₂: Insight from Deep Neural Network Potential Simulations

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Dynamic Simulation of the Migration of Oxygen Vacancy Defects in Rutile TiO2

Cited by 3 publications

References 10 publications

Reduction of the TiO2–x melting temperature induced by oxygen deficiency with implications on experimental data accuracy and structural transition processes

Reduction of the TiO2–x melting temperature induced by oxygen deficiency with implications on experimental data accuracy and structural transition processes

Permutation-invariant collective variable to track and drive vacancy dynamics in simulations of solids

Oxygen Vacancy Diffusion in Rutile TiO2: Insight from Deep Neural Network Potential Simulations

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Dynamic Simulation of the Migration of Oxygen Vacancy Defects in Rutile TiO₂

Reduction of the TiO_2–x melting temperature induced by oxygen deficiency with implications on experimental data accuracy and structural transition processes

Reduction of the TiO_2–x melting temperature induced by oxygen deficiency with implications on experimental data accuracy and structural transition processes

Oxygen Vacancy Diffusion in Rutile TiO₂: Insight from Deep Neural Network Potential Simulations