SearcharxivSearch

arXiv · 1508.05247

Computational searches for iron oxides at high pressures

Abstract

We have used density-functional-theory methods and the ab initio random structure searching (AIRSS) approach to predict stable structures and stoichiometries of mixtures of iron and oxygen at high pressures. Searching was performed for 12 different stoichiometries at pressures of 100, 350 and 500 GPa, which involved relaxing more than 32,000 structures. We find that Fe$_2$O$_3$ and FeO$_2$ are the only phases stable to decomposition at 100 GPa, while at 350 and 500 GPa several stoichiometries are found to be stable or very nearly stable. We report a new structure of Fe$_2$O$_3$ with $P2_12_12_1$ symmetry which is found to be more stable than the known Rh$_2$O$_3$(II) phase at pressures above $\sim$233 GPa. We also report two new structures of FeO, with $Pnma$ and $R\bar{3}m$ symmetries, which are found to be stable within the ranges 195-285 GPa and 285-500 GPa, respectively, and two new structures of Fe$_3$O$_4$ with $Pca2_1$ and $P2_1/c$ symmetries, which are found to be stable within the ranges 100-340 GPa and 340-500 GPa, respectively. Finally, we report two new structures of Fe$_4$O$_5$ with $P4_2/n$ and $P\bar{3}m1$ symmetries, which are found to be stable within the ranges 100-231 GPa and 231-500 GPa, respectively. Our new structures of Fe$_3$O$_4$ and Fe$_4$O$_5$ are found to have lower enthalpies than their known structures within their respective stable pressure ranges.

Explore related subjects

Keep this discovery

BibTeXRIS

Gihan L. Weerasinghe, R. J. Needs, Chris J. Pickard. 2015-08-21. Computational searches for iron oxides at high pressures. https://arxiv.org/abs/1508.05247

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Measuring chiral phonons

Chiral phonons are quantized vibrations where the atomic motion in a solid breaks improper rotation symmetries. In many cases, chiral phonons possess angular momenta and are therefore selective to circularly polarized light. Both fundamental and applied research efforts on chiral phonons have been gaining increasing attention owing to their importance in a variety of fields including spintronics, spin-selective chemical reactions, thermal transport, quantum information processing and biosensing, where the bi-directional spin-lattice coupling enabled by chiral phonons can be harnessed in new ways, and potentially lead to new functionalities. Thus far, the studies of chiral phonons across diverse materials platforms have evolved largely independently within these fields, but the experimental techniques are often interrelated. In this perspective, we present a detailed description, as well as advantages and disadvantages of the current approaches for experimentally measuring chiral phonons in chiral and achiral materials. We conclude with a discussion of new methods for measuring chiral phonons. Ultimately, this work seeks to offer an experimental guide for systematically investigating the properties of chiral phonons in various materials systems and applications.

cond-mat.mtrl-sci

A model of grain growth in UN integrating molecular dynamics, phase-field modeling, and uncertainty quantification

Grain growth kinetics and grain-boundary (GB) properties in uranium mononitride (UN) are investigated through an integrated multiscale framework combining molecular dynamics (MD), phase-field modeling, and surrogate-assisted uncertainty quantification. MD simulations yield GB energies for 27 symmetric tilt boundaries from 0--2000~K, which are consistent with available DFT values. The average GB energy is nearly temperature-independent below 1000~K and increases at higher temperatures. A mechanistic pore-drag model applied to the only available grain growth dataset for actinide nitrides yields a mobility reduction factor of $s \approx 0.93$--$0.99$, statistically indistinguishable from unity, confirming that pore drag is negligible under the experimental conditions. The intrinsic GB mobility is therefore extracted directly from the effective mobility, yielding $M_0 = 2.05\times10^{-15}$~m$^4$/(J$\cdot$s) and $Q_M = 0.89$~eV. Phase-field simulations conducted from 1500--2000~K confirm normal curvature-driven grain growth, with grain size distributions converging to the Hillert-like form. A surrogate-assisted global sensitivity analysis---combining principal component analysis, Gaussian process regression, and Sobol decomposition---reveals that the mobility prefactor $M_0$ dominates output variance at all times, followed by the activation energy $Q_M$, while the GB energy $\gamma$ contributes minimally. These results establish the first quantitative grain growth framework for UN and identify the reduction of uncertainty in $M_0$ and $Q_M$ as the highest-priority target for future experimental efforts.

cond-mat.mtrl-sci

Silicon Solar Cell Design for >30% Efficiency via Singlet Fission

Singlet fission (SF) materials convert high-energy photons into multiple charge carriers, providing a route to exceed the efficiency limits of single-junction silicon solar cells without many of the complexities of multi-junction tandem designs. Following the first demonstration of an SF-enhanced silicon solar cell in 2025, there is a need to understand how SF materials can be effectively integrated into high-efficiency industrial silicon devices and translated from proof of concept to a manufacturable technology. Using coupled optical and electrical simulations, we assess the efficiency potential of several industrially relevant silicon cell architectures combined with SF materials. Interdigitated back-contact (IBC) cells offer the greatest potential for improvement due to unrestricted front-surface access and can achieve efficiencies exceeding 33%. However, performance is highly sensitive to front-surface passivation quality. Appropriate silicon design, particularly controlled surface doping and fixed interfacial charge, can mitigate recombination losses and relax passivation requirements for ultra-thin exciton-transfer layers.

cond-mat.mtrl-sci