R. Ramprasad

Magnetic properties of metallic ferromagnetic nanoparticle composites

Magnetic properties of nanoparticle composites, consisting of aligned ferromagnetic nanoparticles embedded in a nonmagnetic matrix, have been determined using a model based on phenomenological approaches. Input materials parameters for this model include the saturation magnetization (Ms), the crystal anisotropy field (Hk), a damping parameter (α) that describes the magnetic losses in the particles, and the conductivity (σ) of the particles; all particles are assumed to have identical properties. Control of the physical characteristics of the composite system—such as the particle size, shape, volume fraction, and orientation—is necessary in order to achieve optimal magnetic properties (e.g., the magnetic permeability) at GHz frequencies. The degree to which the physical attributes need to be controlled has been determined by analysis of the ferromagnetic resonance (FMR) and eddy current losses at varying particle volume fractions. Composites with approximately spherical particles with radii smaller than 10...

First principles study of oxygen vacancy defects in tantalum pentoxide

First principles total energy calculations were performed to characterize oxygen vacancy defects in tantalum pentoxide (Ta2O5). A simplified version of the crystalline orthorhombic phase of Ta2O5 was used in this study. Results indicate that O vacancies in Ta2O5 can be broadly classified based on their location in the lattice. One type of vacancy that occupies the “in-plane” sites displays deep or midgap occupied states and shallow unoccupied states, while a second type occupying “cap” sites results in shallow occupied states. For a wide range of Fermi levels or chemical potentials, the neutral and +2 charged states of the in-plane type vacancy and the +2 charge state of the cap type vacancy are found to be most stable.

Pathways towards ferroelectricity in hafnia

The question of whether one can systematically identify (previously unknown) ferroelectric phases of a given material is addressed, taking hafnia

Band gap tuning in GaN through equibiaxial in-plane strains

({\mathrm{HfO}}_{2})

Machine learning bandgaps of double perovskites.

as an example. Low free energy phases at various pressures and temperatures are identified using a first-principles based structure search algorithm. Ferroelectric phases are then recognized by exploiting group theoretical principles for the symmetry-allowed displacive transitions between nonpolar and polar phases. Two orthorhombic polar phases occurring in space groups

Dielectric response and tunability of a dielectric-paraelectric composite

Pca{2}_{1}

Machine learning strategy for accelerated design of polymer dielectrics

and

First principles study of CdSe quantum dots: Stability, surface unsaturations, and experimental validation

Pmn{2}_{1}

Accelerated materials property predictions and design using motif-based fingerprints

are singled out as the most viable ferroelectric phases of hafnia, as they display low free energies (relative to known nonpolar phases), and substantial switchable spontaneous electric polarization. These results provide an explanation for the recently observed surprising ferroelectric behavior of hafnia, and reveal pathways for stabilizing ferroelectric phases of hafnia as well as other compounds.

The intrinsic electrical breakdown strength of insulators from first principles

Structural transformations and the relative variation in the band gap energy (ΔEg) of (0001) gallium nitride (GaN) films as a function of equibiaxial in-plane strains are studied by density functional theory. For relatively small compressive misfits (−6%–0%), the band gap is estimated to be around its strain-free value, while for small tensile strains (0%–6%), it decreases by approximately 45%. In addition, at large tensile strains (>14.5%), our calculations indicate that GaN may undergo a structural phase transition from wurtzite to a graphitelike semimetallic phase.