Molecular dynamics investigation of oxygen vacancy diffusion in rutile

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

Defects play a crucial role in the surface reactivity and electronic engineering of titanium dioxide (TiO₂). In this work, we have used an active learning method to train deep neural network potentials from the ab initio data of a defective TiO₂ 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 DFT-level accuracy and promote people's understanding of the microscopic mechanism of fundamental reactions.

Accessing slow diffusion in solids by employing metadynamics simulation

Molecular dynamics (MD) is a powerful tool to investigate microscopic transport of atoms and molecules in condensed matter. However, there lies a large class of systems wherein atomic diffusion is too slow a process relative to the feasible time scales of typical atomistic simulations. Here, we demonstrate that with judicial implementation of a metadynamics (MTD) technique, the microscopic mechanism of atomic transport in solids can be accessed within a reasonable computational time. The calculations are carried out on the two end members of the true NASICON solid solutions, namely NaZr2(PO4)3 and Na4Zr2(SiO4)3, wherein Na+ diffusion is too slow to be accessed through standard MD simulations. The study also provides fresh insights on correlated ion hops and their implications on the effective diffusion barrier. The results are compared with climbing image nudged elastic band (CI-NEB) calculation, and available experimental results.

Protein Conformational Transitions from All-Atom Adaptively Biased Path Optimization

Simulation methods are valuable for elucidating protein conformational transitions between functionally diverse states given that transition pathways are difficult to capture experimentally. Nonetheless, specific computational algorithms are required because of the high free energy barriers between these different protein conformational states. Adaptively biased path optimization (ABPO) is an unrestrained, transition-path optimization method that works in a reduced-variable space to construct an adaptive biasing potential to aid convergence. ABPO was previously applied using a coarse-grained Go̅-model to study conformational activation of Lyn, a Src family tyrosine kinase. How effectively ABPO can be applied at the higher resolution of an all-atom model to explore protein conformational transitions is not yet known. Here, we report the all-atom conformational transition paths of three protein systems constructed using the ABPO methodology. Two systems, triose phosphate isomerase and dihydrofolate reductase, undergo local flipping of a short loop that promotes ligand binding. The third system, estrogen receptor α ligand binding domain, has a helix that adopts different conformations when the protein is bound to an agonist or an antagonist. For each protein, distance-based or torsion-angle reduced variables were identified from unbiased trajectories. ABPO was computed in this reduced variable space to obtain the transition path between the two states. The all-atom ABPO is shown to successfully converge an optimal transition path for each of the three systems.

Enhancement of Photo-Oxidation Activities Depending on Structural Distortion of Fe-Doped TiO2 Nanoparticles

To design a high-performance photocatalytic system with TiO2, it is necessary to reduce the bandgap and enhance the absorption efficiency. The reduction of the bandgap to the visible range was investigated with reference to the surface distortion of anatase TiO2 nanoparticles induced by varying Fe doping concentrations. Fe-doped TiO2 nanoparticles (Fe@TiO2) were synthesized by a hydrothermal method and analyzed by various surface analysis techniques such as transmission electron microscopy, Raman spectroscopy, X-ray diffraction, scanning transmission X-ray microscopy, and high-resolution photoemission spectroscopy. We observed that Fe doping over 5 wt.% gave rise to a distorted structure, i.e., Fe2Ti3O9, indicating numerous Ti(3+) and oxygen-vacancy sites. The Ti(3+) sites act as electron trap sites to deliver the electron to O2 as well as introduce the dopant level inside the bandgap, resulting in a significant increase in the photocatalytic oxidation reaction of thiol (-SH) of 2-aminothiophenol to sulfonic acid (-SO3H) under ultraviolet and visible light illumination.

Effects of various hydrogenated treatments on formation and photocatalytic activity of black TiO2 nanowire arrays

The effects of hydrogen thermal and plasma treatment on the formation and photocatalytic activities of black TiO2 nanowire arrays were investigated and discussed. After either the hydrogen thermal or plasma treatment, the TiO2 nanowires remained. However, in contrast to the plasma treated nanowires, the diameter of the thermal treated TiO2 nanowires reduced more significantly, which was attributed to a thicker surface amorphous layer and more oxygen vacancies. A higher photoresponse in both UV and visible light regions and more hydroxide groups were also observed for the thermal treated nanowires. In addition, the black nanowires possessed greater carrier concentration, leading to a more efficient separation of electron-hole pairs. As a consequence, much enhanced photoelectrochemical water splitting and photocatalytic degradation of methylene blue were obtained.

Formation of titanium monoxide (001) single-crystalline thin film induced by ion bombardment of titanium dioxide (110)

A plethora of technological applications justify why titanium dioxide is probably the most studied oxide, and an optimal exploitation of its properties quite frequently requires a controlled modification of the surface. Low-energy ion bombardment is one of the most extended techniques for this purpose and has been recently used in titanium oxides, among other applications, to favour resistive switching mechanisms or to form transparent conductive layers. Surfaces modified in this way are frequently described as reduced and defective, with a high density of oxygen vacancies. Here we show, at variance with this view, that high ion doses on rutile titanium dioxide (110) induce its transformation into a nanometric and single-crystalline titanium monoxide (001) thin film with rocksalt structure. The discovery of this ability may pave the way to new technical applications of ion bombardment not previously reported, which can be used to fabricate heterostructures and interfaces.

Current challenges in atomistic simulations of glasses for biomedical applications

Atomic-scale simulations of bioglasses are being used to tackle several challenging aspects, such as new structural markers of bioactivity, ion migration and nanosized samples.

ATR-FTIR measurements and quantum chemical calculations concerning the adsorption and photoreaction of oxalic acid on TiO2

The adsorption and photoreaction of oxalic acid on the surface of anatase and rutile TiO2 nanoparticles have been studied using a combined experimental and theoretical approach. In the dark, the experimental adsorption reaches an equilibrium state that can be described as a mixture of adsorbed water and oxalic acid molecules, with the latter forming two different surface complexes on anatase and one on rutile particles. When the system is subsequently illuminated with UV(A) light, the surface becomes enriched with absorbed oxalic acid, which replaces photo-desorbed water molecules, and one of the adsorbed oxalic acid structures on anatase is favoured over the other.

Equilibrium free energies from nonequilibrium metadynamics

In this Letter we propose a new formalism to map history-dependent metadynamics in a Markovian process. We apply this formalism to model Langevin dynamics and determine the equilibrium distribution of a collection of simulations. We demonstrate that the reconstructed free energy is an unbiased estimate of the underlying free energy and analytically derive an expression for the error. The present results can be applied to other history-dependent stochastic processes, such as Wang-Landau sampling.

Modeling fluid diffusion using the lattice density functional theory approach: counterdiffusion in an external field

The dependence of the diffusivity on temperature, pressure, and composition is not understood well; consequently, data is preferred significantly over correlations in most practical situations. Even in dilute gases, the contributions of attractions and repulsions to the diffusivity are difficult to understand on a molecular level without performing simulations. We have developed a Lattice Density Functional Theory (LDFT) approach for modeling diffusion to supplement existing methods that are very rigorous but computationally demanding. The LDFT approach is analogous to the van der Waals equation in how it accounts for molecular interactions in that it has first-order corrections to ideal behavior; it is an extension of the Equilibrium LDFT for adsorption and phase behavior. In this work, the LDFT approach is presented and demonstrated by modeling the problem of color counterdiffusion in an externally-applied potential field. This potential field, in combination with the intermolecular potential function, creates a diffusion regime in which repulsions cause oscillations in the density profile. Using the LDFT approach, the oscillations were described and attributed to nearest-neighbor and next nearest-neighbor interactions. The LDFT approach gives qualitative and quantitative agreement with dual control-volume Grand Canonical Molecular Dynamics simulations.