arXiv subjects

Stefano Curtarolo

Publications and source records attributed to Stefano Curtarolo.

At least 91 records · Page 5Linked to original sources

arXiv2016

Accurate $ab~initio$ tight-binding Hamiltonians: effective tools for electronic transport and optical spectroscopy from first principles

The calculations of electronic transport coefficients and optical properties require a very dense interpolation of the electronic band structure in reciprocal space that is computationally expensive and may have issues with band crossing and degeneracies. Capitalizing on a recently developed pseudo-atomic orbital projection technique, we exploit the exact tight-binding representation of the first principles electronic structure for the purposes of (1) providing an efficient strategy to explore the full band structure $E_n({\bf k})$, (2) computing the momentum operator differentiating directly the Hamiltonian, and (3) calculating the imaginary part of the dielectric function. This enables us to determine the Boltzmann transport coefficients and the optical properties within the independent particle approximation. In addition, the local nature of the tight-binding representation facilitates the calculation of the ballistic transport within the Landauer theory for systems with hundreds of atoms. In order to validate our approach we study the multi-valley band structure of CoSb$_3$ and a large core-shell nanowire using the ACBN0 functional. In CoSb$_3$ we point the many band minima contributing to the electronic transport that enhance the thermoelectric properties; for the core-shell nanowire we identify possible mechanisms for photo-current generation and justify the presence of protected transport channels in the wire.

cond-mat.mtrl-sci↗

arXiv2016

A computational high-throughput search for new ternary superalloys

In 2006, a novel cobalt-based superalloy was discovered [1] with mechanical properties better than some conventional nickel-based superalloys. As with conventional superalloys, its high performance arises from the precipitate-hardening effect of a coherent L1$_2$ phase, which is in two-phase equilibrium with the fcc matrix. Inspired by this unexpected discovery of an L1$_2$ ternary phase, we performed a first-principles search through 2224 ternary metallic systems for analogous precipitate-hardening phases of the form $X_{3}$[$A_{0.5}, B_{0.5}$], where $X$ = Ni, Co, or Fe, and [$A,B$] = Li, Be, Mg, Al, Si, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn Ga, Sr, Y, Zr, Nb, Mo, Tc, Ru, Rh, Pd, Ag, Cd, In, Sn, Sb, Hf, Ta, W, Re, Os, Ir, Pt, Au, Hg, or Tl. We found 102 systems that have a smaller decomposition energy and a lower formation enthalpy than the Co$_{3}$(Al, W) superalloy. They have a stable two-phase equilibrium with the host matrix within the concentration range $0<x<1$ ($X_{3}$[$A_{x}, B_{1-x}$]) and have a relative lattice mismatch with the host matrix of less than or equal to 5%. These new candidates, narrowed from 2224 systems, suggest possible experimental exploration for identifying new superalloys. Of these 102 systems, 37 are new; they have no reported phase diagrams in standard databases. Based on cost, experimental difficulty, and toxicity, we limit these 37 to a shorter list of six promising candidates of immediate interest. Our calculations are consistent with current experimental literature where data exists.

cond-mat.mtrl-sci↗

arXiv2016

High-Throughput Prediction of Finite-Temperature Properties using the Quasi-Harmonic Approximation

In order to calculate thermal properties in automatic fashion, the Quasi-Harmonic Approximation (QHA) has been combined with the Automatic Phonon Library (APL) and implemented within the AFLOW framework for high-throughput computational materials science. As a benchmark test to address the accuracy of the method and implementation, the specific heats, thermal expansion coefficients, Grüneisen parameters and bulk moduli have been calculated for 130 compounds. It is found that QHA-APL can reliably predict such values for several different classes of solids with root mean square relative deviation smaller than 28% with respect to experimental values. The automation, robustness, accuracy and precision of QHA-APL enable the computation of large material data sets, the implementation of repositories containing thermal properties, and finally can serve the community for data mining and machine learning studies.

cond-mat.mtrl-sci↗

arXiv2016

Spectral descriptors for bulk metallic glasses based on the thermodynamics of competing crystalline phases

Metallic glasses have attracted considerable interest in recent years due to their unique combination of superb properties and processability. Predicting bulk metallic glass formers from known parameters remains a challenge and the search for new systems is still performed by trial and error. It has been speculated that some sort of "confusion" during crystallization of the crystalline phases competing with glass formation could play a key role. Here, we propose a heuristic descriptor quantifying confusion and demonstrate its validity by detailed experiments on two well-known glass forming alloy systems. With the insight provided by these results, we develop a robust model for predicting glass formation ability based on the spectral decomposition of geometrical and energetic features of crystalline phases calculated ab-initio in the AFLOW high throughput framework. Our findings indicate that the formation of metallic glass phases could be a much more common phenomenon than currently estimated, with more than 17% of binary alloy systems being potential glass formers. Our approach is capable of pinpointing favorable compositions, overcoming a major bottleneck hindering the discovery of new materials. Hence, it is demonstrated that smart descriptors, based solely on the energetics and structure of competing crystalline phases calculated from first-principles and available in online databases, others the sought-after key for accelerated discovery of novel metallic glasses.

cond-mat.mtrl-sci↗

arXiv2016

High throughput thermal conductivity of high temperature solid phases: The case of oxide and fluoride perovskites

Using finite-temperature phonon calculations and machine-learning methods, we calculate the mechanical stability of about 400 semiconducting oxides and fluorides with cubic perovskite structures at 0 K, 300 K and 1000 K. We find 92 mechanically stable compounds at high temperatures -- including 36 not mentioned in the literature so far -- for which we calculate the thermal conductivity. We demonstrate that the thermal conductivity is generally smaller in fluorides than in oxides, largely due to a lower ionic charge, and describe simple structural descriptors that are correlated with its magnitude. Furthermore, we show that the thermal conductivities of most cubic perovskites decrease more slowly than the usual $T^{-1}$ behavior. Within this set, we also screen for materials exhibiting negative thermal expansion. Finally, we describe a strategy to accelerate the discovery of mechanically stable compounds at high temperatures.

cond-mat.mtrl-sci↗

arXiv2016

Thermoelectric characterization of fine-grained Ti5O9 Magneli phase ceramics

Magneli phase Ti5O9 ceramics with 200-nm grain-size were fabricated by hot-pressing nanopowders of titanium and anatase TiO2 at 1223 K. The thermoelectric properties of these ceramics were investigated from room temperature to 1076 K. We show that the experimental variation of the electrical conductivity with temperature follows a small-polaron model and that the Seebeck coefficient can be explained by a temperature-dependent Heikes-Chaikin-Beni model. The thermoelectric figure-of-merit ZT of this nanoceramic material reaches 0.3 at 1076 K.

cond-mat.mtrl-sci↗

arXiv2016

Predicting Bulk Metallic Glass Forming Ability with the Thermodynamic Density of Competing Crystalline States

Despite two decades of studies, the formation of metallic glasses, very promising systems for industrial applications, still remains mostly unexplained. This lack of knowledge hinders the search for new systems, still performed with combinatorial trial and error. In the past, it was speculated that some sort of "confusion" during crystallization could play a key role during their formation. In this article, we propose a heuristic descriptor quantifying such confusion. It is based on the "thermodynamic density of competing crystalline states", parameterized from high-throughput ab-initio calculations. The existence of highly enthalpy-degenerate but geometrically-different phases frustrates the crystallization process and promotes glass formation. Two test beds are considered. A good and a bad glass-former, CuZr and NiZr, are experimentally characterized with high-throughput synthesis. The experimental results corroborate the capability of the heuristic descriptor in predicting glass forming ability through the compositional space. Our analysis is expected to deepen the understanding of the underlying mechanisms and to accelerate the discovery of novel metallic glasses.

cond-mat.mtrl-sci↗

arXiv2016

Accurate Tight-Binding Hamiltonians for 2D and Layered Materials

We present a scheme to controllably improve the accuracy of tight-binding Hamiltonian matrices derived by projecting the solutions of plane-wave ab initio calculations on atomic orbital basis sets. By systematically increasing the completeness of the basis set of atomic orbitals, we are able to optimize the quality of the band structure interpolation over wide energy ranges including unoccupied states. This methodology is applied to the case of interlayer and image states, which appear several eV above the Fermi level in materials with large interstitial regions or surfaces such as graphite and graphene. Due to their spatial localization in the empty regions inside or outside of the system, these states have been inaccessible to traditional tight-binding models and even to ab initio calculations with atom-centered basis functions.

cond-mat.mtrl-sci↗

arXiv2015

Modeling Disordered Materials with a High Throughput ab-initio Approach

Predicting material properties of disordered systems remains a long-standing and formidable challenge in rational materials design. To address this issue, we introduce an automated software framework capable of modeling partial occupation within disordered materials using a high-throughput (HT) first principles approach. At the heart of the approach is the construction of supercells containing a virtually equivalent stoichiometry to the disordered material. All unique supercell permutations are enumerated and material properties of each are determined via HT electronic structure calculations. In accordance with a canonical ensemble of supercell states, the framework evaluates ensemble average properties of the system as a function of temperature. As proof of concept, we examine the framework's final calculated properties of a zinc chalcogenide (ZnS$_{1-x}$Se$_x$), a wide-gap oxide semiconductor (Mg$_{x}$Zn$_{1-x}$O), and an iron alloy (Fe$_{1-x}$Cu$_{x}$) at various stoichiometries.

cond-mat.mtrl-sci↗

arXiv2015

First principles thermodynamical modeling of the binodal and spinodal curves in lead chalcogenides

High-throughput ab-initio calculations, cluster expansion techniques and thermodynamic modeling have been synergistically combined to characterize the binodal and the spinodal decompositions features in the pseudo-binary lead chalcogenides PbSe-PbTe, PbS-PbTe, and PbS-PbSe. While our results agree with the available experimental data, our consolute temperatures substantially improve with respect to previous computational modeling. The computed phase diagrams corroborate that the formation of spinodal nanostructures causes low thermal conductivities in these alloys. The presented approach, making a rational use of online quantum repositories, can be extended to study thermodynamical and kinetic properties of materials of technological interest.

cond-mat.mtrl-sci↗

arXiv2015

The AFLOW Standard for High-Throughput Materials Science Calculations

The Automatic-Flow ( AFLOW ) standard for the high-throughput construction of materials science electronic structure databases is described. Electronic structure calculations of solid state materials depend on a large number of parameters which must be understood by researchers, and must be reported by originators to ensure reproducibility and enable collaborative database expansion. We therefore describe standard parameter values for k-point grid density, basis set plane wave kinetic energy cut-off, exchange-correlation functionals, pseudopotentials, DFT+U parameters, and convergence criteria used in AFLOW calculations.

cond-mat.mtrl-sci↗

arXiv2015

Improved predictions of the physical properties of Zn- and Cd-based wide band-gap semiconductors: a validation of the ACBN0 functional

We study the physical properties of Zn$X$ ($X$=O, S, Se, Te) and Cd$X$ ($X$=O, S, Se, Te) in the zinc-blende, rock-salt, and wurtzite structures using the recently developed fully $ab$ $initio$ pseudo-hybrid Hubbard density functional ACBN0. We find that both the electronic and vibrational properties of these wide-band gap semiconductors are systematically improved over the PBE values and reproduce closely the experimental measurements. Similar accuracy is found for the structural parameters, especially the bulk modulus. ACBN0 results compare well with hybrid functional calculations at a fraction of the computational cost.

cond-mat.mtrl-sci↗

arXiv2014

Materials Cartography: Representing and Mining Material Space Using Structural and Electronic Fingerprints

As the proliferation of high-throughput approaches in materials science is increasing the wealth of data in the field, the gap between accumulated-information and derived-knowledge widens. We address the issue of scientific discovery in materials databases by introducing novel analytical approaches based on structural and electronic materials fingerprints. The framework is employed to (i) query large databases of materials using similarity concepts, (ii) map the connectivity of the materials space (i.e., as a materials cartogram) for rapidly identifying regions with unique organizations/properties, and (iii) develop predictive Quantitative Materials Structure-Property Relation- ships (QMSPR) models for guiding materials design. In this study, we test these fingerprints by seeking target material properties. As a quantitative example, we model the critical temperatures of known superconductors. Our novel materials fingerprinting and materials cartography approaches contribute to the emerging field of materials informatics by enabling effective computational tools to analyze, visualize, model, and design new materials.

cond-mat.mtrl-sci↗

arXiv2014

Numerical Algorithm for Pólya Enumeration Theorem

Although the Pólya enumeration theorem has been used extensively for decades, an optimized, purely numerical algorithm for calculating its coefficients is not readily available. We present such an algorithm for finding the number of unique colorings of a finite set under the action of a finite group.

math.CO↗

arXiv2014

Reformulation of DFT+U as a pseudo-hybrid Hubbard density functional

The accurate prediction of the electronic properties of materials at a low computational expense is a necessary conditions for the development of effective high-throughput quantum-mechanics (HTQM) frameworks for accelerated materials discovery. HTQM infrastructures rely on the predictive capability of Density Functional Theory (DFT), the method of choice for the first principles study of materials properties. However, DFT suffers of approximations that result in a somewhat inaccurate description of the electronic band structure of semiconductors and insulators. In this article we introduce ACBN0, a pseudo-hybrid Hubbard density functional that yields an improved prediction of the band structure of insulators such as transition-metal oxides, as shown for TiO2, MnO, NiO and ZnO, with only a negligible increase in computational cost.

cond-mat.str-el↗

arXiv2014

High-Throughput Computational Screening of thermal conductivity, Debye temperature and Grüneisen parameter using a quasi-harmonic Debye Model

The quasi-harmonic Debye approximation has been implemented within the AFLOW and Materials Project frameworks for high-throughput computational science (Automatic Gibbs Library, AGL), in order to calculate thermal properties such as the Debye temperature and the thermal conductivity of materials. We demonstrate that the AGL method, which is significantly cheaper computationally compared to the fully ab initio approach, can reliably predict the ordinal ranking of the thermal conductivity for several different classes of semiconductor materials. We also find that for the set of 182 materials investigated in this work the Debye temperature, calculated with the AGL, is often a better predictor of the ordinal ranking of the experimental thermal conductivities than the calculated thermal conductivity. The Debye temperature is thus a potential descriptor for high-throughput screening of the thermal properties of materials.

cond-mat.mtrl-sci↗

arXiv2014

Nanograined half-Heusler semiconductors as advanced thermoelectrics: an ab-initio high-throughput statistical study

Nanostructuring has spurred a revival in the field of direct thermoelectric energy conversion. Nanograined materials can now be synthesized with higher figures of merit (ZT) than the bulk counterparts. This leads to increased conversion efficiencies. Despite considerable effort in optimizing the known and discovering the unknown, technology still relies upon a few limited solutions. Here we perform ab-initio modeling of ZT for 75 nanograined compounds obtained by filtering down the 79,057 half-Heusler entries available in the AFLOWLIB.org repository according to electronic and thermodynamic criteria. For many of the compounds the $ZT$s are markedly above those attainable with nanograined IV and III-V semiconductors. About 15% of them may even outperform ZT~2 at high temperatures. Our analysis elucidates the origin of the advantageous thermoelectric properties found within this broad material class. We use machine learning techniques to unveil simple rules determining if a nanograined half-Heusler compound is likely to be a good thermoelectric given its chemical composition.

cond-mat.mtrl-sci↗

arXiv2014

Composition-spread Growth and the Robust Topological Surface State of Kondo insulator SmB6 Thin Films

Topological insulators are a class of materials with insulating bulk but protected conducting surfaces due to the combination of spin-orbit interactions and time-reversal symmetry. The surface states are topologically non-trivial and robust against non-magnetic backscattering, leading to interesting physics and potential quantum computing applications1, 2. Recently there has been a fast growing interest in samarium hexboride (SmB6), a Kondo insulator predicted to be the first example of a correlated topological insulator3, 4. Here we fabricated smooth thin films of nanocrystalline SmB6 films. Their transport behavior indeed shows that SmB6 is a bulk insulator with topological surface states. Upon decreasing the temperature, the resistivity \r{ho} of Sm0.14B0.86 (SmB6) films display significant increase below 50 K due to hybridization gap formation, and it shows a saturation behavior below 10 K. The saturated resistance of our textured films is similar to that of the single crystals, suggesting that this conduction is from the surface and robust against grain boundary scatterings. Point contact spectroscopy (PCS) of the film using a superconducting tip displays both a Kondo Fano resonance and Andreev reflection, suggesting the existence of both an insulating Kondo lattice and metallic surface states.

cond-mat.mtrl-sci↗