Torsional selection rules, Raman tensors, and cross sections for degenerate modes of C2H6

16 pages, 12 figures.-- PACS nrs.: 33.20.Tp, 33.15.Mt, 33.70.Fd.We analyze the peculiarities induced by the torsional motion on the Raman spectra of the degenerate vibrational bands of ethane. Selection rules for the Raman transitions between the torsionally split energy levels are derived first in terms of symmetry arguments. Then, their associated intensities are calculated with a model for the torsional dependence of the molecular polarizability. New experimental spectra of the Raman degenerate bands of C2H6, some recorded in jet expansions, are also included and analyzed to show the current state of the problem.This work was supported by the Spanish DGICYT and DGES, research projects PB94-1526 and PB97-1203.Peer reviewe

Disordered enthalpy–entropy descriptor for high-entropy ceramics discovery

The need for improved functionalities in extreme environments is fuelling interest in high-entropy ceramics1,2,3. Except for the computational discovery of high-entropy carbides, performed with the entropy-forming-ability descriptor4, most innovation has been slowly driven by experimental means1,2,3. Hence, advancement in the field needs more theoretical contributions. Here we introduce disordered enthalpy–entropy descriptor (DEED), a descriptor that captures the balance between entropy gains and enthalpy costs, allowing the correct classification of functional synthesizability of multicomponent ceramics, regardless of chemistry and structure. To make our calculations possible, we have developed a convolutional algorithm that drastically reduces computational resources. Moreover, DEED guides the experimental discovery of new single-phase high-entropy carbonitrides and borides. This work, integrated into the AFLOW computational ecosystem, provides an array of potential new candidates, ripe for experimental discoveries

OPTIMADE, an API for exchanging materials data

: The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification

OPTIMADE, an API for exchanging materials data.

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification

Roadmap on machine learning in electronic structure

In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century

Experimental and Computational Investigations of Kinetically Stable Selenides Synthesized by the Modulated Elemental Reactants Method

The controlled and targeted synthesis of new solid materials is still a challenge difficult to overcome. Slow diffusion rates and long diffusion lengths require long reaction times and high synthesis temperatures, resulting in limited control over the reaction pathway. The Modulated Elemental Reactants (MER) method uses compositionally modulated precursors with atomically thin elemental layers that form amorphous alloys upon annealing while maintaining composition modulation. In this amorphous intermediate, nucleation, not diffusion, control the formation of the product, enabling kinetic control of the reaction, and the synthesis of new metastable compounds, heterostructures with designed nanoarchitecture, and thin films with a high degree of texturing. This dissertation uses experimental and computational methods to investigate compounds synthesized by the MER method. Firth, the MER method is used to synthesize ferromagnetic CuCr2Se4 films that show a large degree of crystallographic alignment and interesting magnetic properties such as temperature-dependent easy axes and negative magnetoresistivity. The second part investigates ferecrystals, rotationally disordered members of the misfit layer compounds family. The MER method’s ability to control the nanoarchitecture of the products is used to synthesize a new type of structural isomers, allowing for the synthesis of thousands of ternary compounds using the same elements. Experimental methods are also used to monitor the formation of ferecrystalline compounds using [(SnSe)1+δ][VSe2] as a model system. Despite the vast number of compounds available, however, explaining the properties and stability of ferecrystals is still in its infancy. In the last part of this dissertation, ab initio methods are employed to investigate the components in our ferecrystals. Specifically, isolated layers of VSe2 with its structural distortions due to a charge density wave, SnSe with its thickness-dependent structures, and BiSe with its flexible lattice and anti-phase boundaries are investigated to complement experimental results. Some properties, such as the structural distortion in VSe2 and the different stabilities of BiSe layers, can be explained very well using this simplified model, but others, such as the structure of SnSe layers, are not exclusively determined by their dimensionality, underlining the complex nature of the interactions in ferecrystals. This dissertation includes previously published and unpublished co-authored material

Insights into the Charge-Transfer Stabilization of Heterostructure Components with Unstable Bulk Analogs

Solid state chemists have yet to find a targeted approach based on simple rules to predict new materials with desired physical properties. Recent advances in computational high-throughput methods have led to the creation of large databases with predicted new compounds. While many of these compounds are unstable, some may be stabilized inside heterostructures. BiSe is an example for such a compound where the rock-salt structure is unstable in bulk but can be found in misfit layer compounds and ferecrystals. In some of these heterostructures, BiSe also exhibits antiphase boundaries (APBs), periodic Bi–Bi pairings that interrupt the alternating pattern of the rock-salt structure. Understanding the behavior of BiSe may aid in the discovery of new heterostructure components where no stable bulk analog exists. We used density functional theory (DFT) and crystal orbital Hamilton populations (COHPs) to explain the different stabilities of rock-salt structured BiSe. COHPs show that rock-salt structured BiSe has occupied antibonding states at the Fermi level, which destabilize the structure. In heterostructures, these states can be depopulated by donating electrons into an adjacent layer or by forming APBs to localize electrons into a Bi–Bi bond. The results suggest that the depopulation of antibonding states is crucial to stabilizing rock-salt structured BiSe, and that BiSe needs to be paired with a suitable electron acceptor. We predict that this is a general principle that can be applied to other compounds with unstable polytypes and suggest that COHPs should play a larger role in the discovery of new heterostructure components

Automated Bonding Analysis with Crystal Orbital Hamilton Populations

Understanding crystalline structures based on their chemical bonding is growing in importance. In this context, chemical bonding can be studied with the Crystal Orbital Hamilton Population (COHP), allowing for quantifying interatomic bond strength. Here we present a new set of tools to automate the calculation of COHP and analyze the results. We use the program packages VASP and LOBSTER, and the Python packages atomate and pymatgen. The analysis produced by our tools includes plots, a textual description, and key data in a machine-readable format. To illustrate those capabilities, we have selected simple test compounds (NaCl, GaN), the oxynitrides BaTaO2N, CaTaO2N, and SrTaO2N, and the thermoelectric material Yb14Mn1Sb11. We show correlations between bond strengths and stabilities in the oxynitrides and the influence of the Mn

Automated Bonding Analysis with Crystal Orbital Hamilton Populations

Understanding crystalline structures based on their chemical bonding is growing in importance. In this context, chemical bonding can be studied with the Crystal Orbital Hamilton Population (COHP), allowing to quantify interatomic bond strength. Here we present a new set of tools to automate the calculation of COHP and analyze the results. We use the program packages VASP and LOBSTER and the Python packages atomate and pymatgen. The analysis produced by our tools includes plots, a textual description, and key data in machine-readable format. To illustrate those capabilities, we have selected simple test compounds (NaCl, GaN), the oxynitrides BaTaO2N, CaTaO2N, and SrTaO2N, and the thermoelectric material Yb14Mn1Sb11. We show correlations between bond strengths and stabilities in the oxynitrides, as well as the influence of the Mn-Sb bonds on the magnetism in Yb14Mn1Sb11. Our contribution enables high-throughput bonding analysis and will facilitate the use of bonding information for machine learning studies