Simple point-ion electrostatic model explains the cation distribution in spinel oxides

The A2BO4 spinel oxides are distinguished by having either a normal (N) or an inverse (I) distribution of the A, B cations on their sublattices. A point-ion electrostatic model parametrized by the oxygen displacement parameter u and by the relative cation valencies ZA vs ZB provides a simple rule for the structural preference for N or I: if ZA>ZB the structure is normal for u>0.2592 and inverse for u0.2578. This rule is illustrated for the known spinel oxides, proving to be ∼98% successful. © 2010 The American Physical Society

A Caching Scheme to Accelerate Kinetic Monte Carlo Simulations of Catalytic Reactions

Kinetic Monte Carlo (KMC) simulations have been instrumental in advancing our fundamental understanding of heterogeneously catalyzed reactions, with particular emphasis on structure sensitivity, ensemble effects, and the interplay between adlayer structure and adsorbate-adsorbate lateral interactions in shaping the observed kinetics. Yet, the computational cost of KMC remains high, thereby motivating the development of acceleration schemes that would improve the simulation effciency. We present an exact such scheme, which implements a caching algorithm along with shared-memory parallelization to improve the computational performance of simulations incorporating long-range adsorbate-adsorbate lateral interactions. This scheme is based on caching information about the energetic interaction patterns associated with the products of each possible lattice process (adsorption, desorption, reaction etc). Thus, every time a reaction occurs ("ongoing reaction") it enables fast updates of the rate constants of "affected reactions", i.e. possible reactions in the region of influence of the "ongoing reaction". Benchmarks on KMC simulations of NOx oxidation/reduction, yield acceleration factors of up to 20× when comparing single-thread runs without caching to runs on 16 threads with caching, for simulations with a cluster expansion Hamiltonian that incorporates up to 8th nearest-neighbor interactions

Genetic-algorithm discovery of a direct-gap and optically allowed superstructure from indirect-gap Si and Ge semiconductors

Combining two indirect-gap materials-with different electronic and optical gaps-to create a direct gap material represents an ongoing theoretical challenge with potentially rewarding practical implications, such as optoelectronics integration on a single wafer. We provide an unexpected solution to this classic problem, by spatially melding two indirect-gap materials (Si and Ge) into one strongly dipole-allowed direct-gap material. We leverage a combination of genetic algorithms with a pseudopotential Hamiltonian to search through the astronomic number of variants of Si /Ge /.../Si /Ge superstructures grown on (001) Si Ge . The search reveals a robust configurational motif-SiGe Si Ge SiGe on (001) Si Ge substrate (x≤0.4) presenting a direct and dipole-allowed gap resulting from an enhanced Γ-X coupling at the band edges. © 2012 American Physical Society

Efficient Generalized Spherical CNNs

Many problems across computer vision and the natural sciences require the analysis of spherical data, for which representations may be learned efficiently by encoding equivariance to rotational symmetries. We present a generalized spherical CNN framework that encompasses various existing approaches and allows them to be leveraged alongside each other. The only existing non-linear spherical CNN layer that is strictly equivariant has complexity OpC2L5q, where C is a measure of representational capacity and L the spherical harmonic bandlimit. Such a high computational cost often prohibits the use of strictly equivariant spherical CNNs. We develop two new strictly equivariant layers with reduced complexity OpCL4q and OpCL3 log Lq, making larger, more expressive models computationally feasible. Moreover, we adopt efficient sampling theory to achieve further computational savings. We show that these developments allow the construction of more expressive hybrid models that achieve state-of-the-art accuracy and parameter efficiency on spherical benchmark problems

The atomistic structure of yttria stabilised zirconia at 6.7 mol%: an ab initio study.

Yttria stabilized zirconia (YSZ) is an important oxide ion conductor used in solid oxide fuel cells, oxygen sensing devices, and for oxygen separation. Doping pure zirconia (ZrO2) with yttria (Y2O3) stabilizes the cubic structure against phonon induced distortions and this facilitates high oxide ion conductivity. The local atomic structure of the dopant is, however, not fully understood. X-ray and neutron diffraction experiments have established that, for dopant concentrations below 40 mol% Y2O3, no long range order is established. A variety of local structures have been suggested on the basis of theoretical and computational models of dopant energetics. These studies have been restricted by the difficulty of establishing force field models with predictive accuracy or exploring the large space of dopant configurations with first principles theory. In the current study a comprehensive search for all symmetry independent configurations (2857 candidates) is performed for 6.7 mol% YSZ modelled in a 2 × 2 × 2 periodic supercell using gradient corrected density functional theory. The lowest energy dopant structures are found to have oxygen vacancy pairs preferentially aligned along the 〈210〉 crystallographic direction in contrast to previous results which have suggested that orientation along the 〈111〉 orientation is favourable. Analysis of the defect structures suggests that the Y(3+)-Ovac interatomic separation is an important parameter for determining the relative configurational energies. Current force field models are found to be poor predictors of the lowest energy structures. It is suggested that the energies from a simple point charge model evaluated at unrelaxed geometries is actually a better descriptor of the energy ordering of dopant structures. Using these observations a pragmatic procedure for identifying low energy structures in more complicated material models is suggested. Calculation of the oxygen vacancy migration activation energies within the lowest energy 〈210〉 oriented structures gives results consistent with experimental observations

Abnormal morphology biases haematocrit distribution in tumour vasculature and contributes to heterogeneity in tissue oxygenation

Oxygen heterogeneity in solid tumors is recognized as a limiting factor for therapeutic efficacy. This heterogeneity arises from the abnormal vascular structure of the tumor, but the precise mechanisms linking abnormal structure and compromised oxygen transport are only partially understood. In this paper, we investigate the role that red blood cell (RBC) transport plays in establishing oxygen heterogeneity in tumor tissue. We focus on heterogeneity driven by network effects, which are challenging to observe experimentally due to the reduced fields of view typically considered. Motivated by our findings of abnormal vascular patterns linked to deviations from current RBC transport theory, we calculated average vessel lengths L⎯⎯ and diameters d⎯⎯ from tumor allografts of three cancer cell lines and observed a substantial reduction in the ratio λ=L⎯⎯/d⎯⎯ compared to physiological conditions. Mathematical modeling reveals that small values of the ratio λ (i.e., λ<6 ) can bias hematocrit distribution in tumor vascular networks and drive heterogeneous oxygenation of tumor tissue. Finally, we show an increase in the value of λ in tumor vascular networks following treatment with the antiangiogenic cancer agent DC101. Based on our findings, we propose λ as an effective way of monitoring the efficacy of antiangiogenic agents and as a proxy measure of perfusion and oxygenation in tumor tissue undergoing antiangiogenic treatment

Spin and orbital magnetic response in metals: Susceptibility and NMR shifts

A DFT-based method is presented which allows the computation of all-electron NMR shifts of metallic compounds with periodic boundary conditions. NMR shifts in metals measure two competing physical phenomena. Electrons interact with the applied magnetic field (i) as magnetic dipoles (or spins), resulting in the Knight shift, and (ii) as moving electric charges, resulting in the chemical (or orbital) shift. The latter is treated through an extension to metals of the gauge-invariant projector augmented wave developed for insulators. The former is modeled as the hyperfine interaction between the electronic spin polarization and the nuclear dipoles. NMR shifts are obtained with respect to the computed shieldings of reference compounds, yielding fully ab initio quantities which are directly comparable to experiment. The method is validated by comparing the magnetic susceptibility of interacting and noninteracting homogeneous gas with known analytical results, and by comparing the computed NMR shifts of simple metals with experiment

MUSE: An open-source agent-based integrated assessment modelling framework

Integrated assessment models (IAMs) are a cornerstone of an effective approach to climate change mitigation. Despite the variety of methodologies for characterising the energy system, land use change, economics, and climate response, the modelling community has an open and urgent request for tools capable of more realistic interpretation of the energy transition, capturing human behaviour, and embodying the principles of transparency, reproducibility, and flexibility of use. This paper presents an open-source modelling framework designed to fill that gap. Named MUSE (ModUlar energy systems Simulation Environment), this new agent-based model supports flexible characterisation of agent decision-making, including individual goals, bounded-rationality, imperfect foresight, and limited knowledge during the decision process. MUSE integrates this agent-based approach in a partial-equilibrium framework and enables a technology-rich description of the energy systems with an unprecedented degree of flexibility for including technological, temporal, and geographical granularity. The structure of MUSE creates the ability to produce climate change mitigation assessments that are more grounded, and more tangible model outputs for conceiving effective approaches to mitigation. MUSE is available open source under a GNU General Public License v3.0 on GitHub at this link https://github.com/SGIModel/MUSE_OS

PURIFY

Next-generation radio interferometric imaging.Next-generation radio interferometric imaging.3.0.