Competition of L21 and XA Ordering in Fe2CoAl Heusler Alloy: A First-Principles Study

The physical properties of Fe2CoAl (FCA) Heusler alloy are systematically investigated using the first-principles calculations within generalized gradient approximation (GGA) and GGA+U. The influence of atomic ordering with respect to the Wyckoff sites on the phase stability, magnetism and half metallicity in both the conventional L21 and XA phases of FCA is focused in this study. Various possible hypothetical structures viz., L21, XA-I, and XA-II are prepared by altering atomic occupancies at their Wyckoff sites. At first, we have determined the stable phase of FCA considering various non-magnetic (or paramagnetic), ferromagnetic (FM) and antiferromagnetic (AFM) configurations. Out of these, the ferromagnetic (FM) XA-I structure is found to be energetically most stable. The total magnetic moments per cell are not in agreement with the Slater-Pauling (SP) rule in any phases; therefore, the half-metallicity is not observed in any configurations. However, FM ordered XA-I type FCA shows 78% spin polarization at EF. Interestingly, the results of XA-I type FCA are closely matched with the experimental results.Comment: 15 pages, 6 figure

Automated computation of materials properties

Materials informatics offers a promising pathway towards rational materials design, replacing the current trial-and-error approach and accelerating the development of new functional materials. Through the use of sophisticated data analysis techniques, underlying property trends can be identified, facilitating the formulation of new design rules. Such methods require large sets of consistently generated, programmatically accessible materials data. Computational materials design frameworks using standardized parameter sets are the ideal tools for producing such data. This work reviews the state-of-the-art in computational materials design, with a focus on these automated $\textit{ab-initio}$ frameworks. Features such as structural prototyping and automated error correction that enable rapid generation of large datasets are discussed, and the way in which integrated workflows can simplify the calculation of complex properties, such as thermal conductivity and mechanical stability, is demonstrated. The organization of large datasets composed of $\textit{ab-initio}$ calculations, and the tools that render them programmatically accessible for use in statistical learning applications, are also described. Finally, recent advances in leveraging existing data to predict novel functional materials, such as entropy stabilized ceramics, bulk metallic glasses, thermoelectrics, superalloys, and magnets, are surveyed.Comment: 25 pages, 7 figures, chapter in a boo

A RESTful API for exchanging Materials Data in the AFLOWLIB.org consortium

The continued advancement of science depends on shared and reproducible data. In the field of computational materials science and rational materials design this entails the construction of large open databases of materials properties. To this end, an Application Program Interface (API) following REST principles is introduced for the AFLOWLIB.org materials data repositories consortium. AUIDs (Aflowlib Unique IDentifier) and AURLs (Aflowlib Uniform Resource locator) are assigned to the database resources according to a well-defined protocol described herein, which enables the client to access, through appropriate queries, the desired data for post-processing. This introduces a new level of openness into the AFLOWLIB repository, allowing the community to construct high-level work-flows and tools exploiting its rich data set of calculated structural, thermodynamic, and electronic properties. Furthermore, federating these tools would open the door to collaborative investigation of the data by an unprecedented extended community of users to accelerate the advancement of computational materials design and development.Comment: 22 pages, 7 figure

Discovery of high-entropy ceramics via machine learning

AbstractAlthough high-entropy materials are attracting considerable interest due to a combination of useful properties and promising applications, predicting their formation remains a hindrance for rational discovery of new systems. Experimental approaches are based on physical intuition and/or expensive trial and error strategies. Most computational methods rely on the availability of sufficient experimental data and computational power. Machine learning (ML) applied to materials science can accelerate development and reduce costs. In this study, we propose an ML method, leveraging thermodynamic and compositional attributes of a given material for predicting the synthesizability (i.e., entropy-forming ability) of disordered metal carbides. The relative importance of the thermodynamic and compositional features for the predictions are then explored. The approach’s suitability is demonstrated by comparing values calculated with density functional theory to ML predictions. Finally, the model is employed to predict the entropy-forming ability of 70 new compositions; several predictions are validated by additional density functional theory calculations and experimental synthesis, corroborating the effectiveness in exploring vast compositional spaces in a high-throughput manner. Importantly, seven compositions are selected specifically, because they contain all three of the Group VI elements (Cr, Mo, and W), which do not form room temperature-stable rock-salt monocarbides. Incorporating the Group VI elements into the rock-salt structure provides further opportunity for tuning the electronic structure and potentially material performance

First-principles calculations to investigate the structural, electronic, elastic, vibrational and thermodynamic properties of the full-Heusler alloys X2ScGa (X = Ir and Rh)

This study has investigated ab initio pseudopotential calculations on the structural, electronic, elastic, vibrational and thermodynamic properties of the full-Heusler X2ScGa (X = Ir and Rh) alloys. The calculations have taken place under consideration of the generalized gradient approximation (GGA) of the density functional theory (DFT) with using the plane-wave ab initio pseudopotential method. According to the calculations, the major contribution to electronic states at the Fermi energy has been achieved by d orbitals, revealing a more active role for transition metals Ir (Rh) and Sc atoms. The reckonings point out that the Ir2ScGa and Rh2ScGa have metallic behavior at the equilibrium lattice constant with the density of states (DOS) at the Fermi level (N (EF)) of 1.412 states/eV and 1.821 states/eV, respectively. The results of the elastic constants showed that these compounds met the criteria for Born mechanical stability. It was also observed that they have a ductile structure and exhibit anisotropic behavior according to Pugh criteria. Besides, the full phonon spectra and their projected partial density of states of the alloys have been analyzed with the first-principle linear-response approach of the density-functional perturbation theory. All the alloys behaved dynamically stable in the L21 phase. Furthermore, internal free energy, entropy, specific heat capacity at constant volume and vibrational free energy changes of Ir2ScGa and Rh2ScGa alloys were analyzed and discussed between the temperature range of 0–800 K using the quasi harmonic approximation. According to the results, these alloys are potential candidate for industrial use. © 2020 Elsevier Lt

Computational predictions of energy materials using density functional theory

In the search for new functional materials, quantum mechanics is an exciting starting point. The fundamental laws that govern the behaviour of electrons have the possibility, at the other end of the scale, to predict the performance of a material for a targeted application. In some cases, this is achievable using density functional theory (DFT). In this Review, we highlight DFT studies predicting energy-related materials that were subsequently confirmed experimentally. The attributes and limitations of DFT for the computational design of materials for lithium-ion batteries, hydrogen production and storage materials, superconductors, photovoltaics and thermoelectric materials are discussed. In the future, we expect that the accuracy of DFT-based methods will continue to improve and that growth in computing power will enable millions of materials to be virtually screened for specific applications. Thus, these examples represent a first glimpse of what may become a routine and integral step in materials discovery