arXiv subjects

Stefano Curtarolo

Publications and source records attributed to Stefano Curtarolo.

At least 55 records · Page 3Linked to original sources

arXiv2020

The AFLOW Library of Crystallographic Prototypes: Part 3

The AFLOW Library of Crystallographic Prototypes has been extended to include a total of 1,100 common crystal structural prototypes (510 new ones with Part 3), comprising all of the inorganic crystal structures defined in the seven-volume Strukturbericht series published in Germany from 1937 through 1943. We cover a history of the Strukturbericht designation system, the evolution of the system over time, and the first comprehensive index of inorganic Strukturbericht designations ever published.

cond-mat.mtrl-sci↗

arXiv2020

High-throughput study of the static dielectric constant at high temperatures in oxide and fluoride cubic perovskites

Using finite-temperature phonon calculations and the Lyddane-Sachs-Teller relations, we calculate ab initio the static dielectric constants of 78 semiconducting oxides and fluorides with cubic perovskite structures at 1000 K. We first compare our method with experimental measurements, and we find that it succeeds in describing the temperature dependence and the relative ordering of the static dielectric constant $ε_{DC}$ in the series of oxides BaTiO$_{3}$, SrTiO$_{3}$, KTaO$_{3}$. We show that the effects of anharmonicity on the ion-clamped dielectric constant, on Born charges, and on phonon lifetimes, can be neglected in the framework of our high-throughput study. Based on the high-temperature phonon spectra, we find that the dispersion of $ε_{DC}$ is one order of magnitude larger amongst oxides than fluorides at 1000 K. We display the correlograms of the dielectric constants with simple structural descriptors, and we point out that $ε_{DC}$ is actually well correlated with the infinite-frequency dielectric constant $ε_{\infty}$, even in those materials with phase transitions in which $ε_{DC}$ is strongly temperature-dependent.

cond-mat.mtrl-sci↗

arXiv2020

On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning

Active learning - the field of machine learning (ML) dedicated to optimal experiment design, has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics [1]. In this work we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate autonomous research methodology (i.e. autonomous hypothesis definition and evaluation) that can place complex, advanced materials in reach, allowing scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. Additionally, this robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. We used the real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) at the synchrotron beamline to accelerate the fundamentally interconnected tasks of rapid phase mapping and property optimization, with each cycle taking seconds to minutes, resulting in the discovery of a novel epitaxial nanocomposite phase-change memory material.

cond-mat.mtrl-sci↗

arXiv2020

The Tin Pest Problem as a Test of Density Functionals Using High-Throughput Calculations

At ambient pressure tin transforms from its ground-state semi-metal $α$-Sn (diamond structure) phase to the compact metallic $β$-Sn phase at 13$^\circ$C (286K). There may be a further transition to the simple hexagonal $γ$-Sn above 450K. These relatively low transition temperatures are due to the small energy differences between the structures, $\approx 20$\,meV/atom between $α$- and $β$-Sn. This makes tin an exceptionally sensitive test of the accuracy of density functionals and computational methods. Here we use the high-throughput Automatic-FLOW (AFLOW) method to study the energetics of tin in multiple structures using a variety of density functionals. We look at the successes and deficiencies of each functional. As no functional is completely satisfactory, we look Hubbard U corrections and show that the Coulomb interaction can be chosen to predict the correct phase transition temperature. We also discuss the necessity of testing high-throughput calculations for convergence for systems with small energy differences.

cond-mat.mtrl-sci↗

arXiv2020

AFLOW-XtalFinder: a reliable choice to identify crystalline prototypes

The accelerated growth rate of repository entries in crystallographic databases makes it arduous to identify and classify their prototype structures. The open-source AFLOW-XtalFinder package was developed to solve this problem. It symbolically maps structures into standard designations following the AFLOW Prototype Encyclopedia and calculates the internal degrees of freedom consistent with the International Tables for Crystallography. To ensure uniqueness, structures are analyzed and compared via symmetry, local atomic geometries, and crystal mapping techniques, simultaneously grouping them by similarity. The software i. distinguishes distinct crystal prototypes and atom decorations, ii. determines equivalent spin configurations, iii. reveals compounds with similar properties, and iv. guides the discovery of unexplored materials. The operations are accessible through a Python module ready for workflows, and through command line syntax. All the 4+ million compounds in the AFLOW.org repositories are mapped to their ideal prototype, allowing users to search database entries via symbolic structure-type. Furthermore, 15,000 unique structures - sorted by prevalence - are extracted from the AFLOW-ICSD catalog to serve as future prototypes in the Encyclopedia.

cond-mat.mtrl-sci↗

arXiv2020

Ultrathin SnTe films as a route towards all-in-one spintronics devices

Spin transistors based on a semiconducting channel attached to ferromagnetic electrodes suffer from fast spin decay and extremely low spin injection/detection efficiencies. Here, we propose an alternative all-in-one spin device whose operation principle relies on electric manipulation of the spin lifetime in two-dimensional (2D) SnTe, in which the sizable spin Hall effect eliminates the need for using ferromagnets. In particular, we explore the persistent spin texture (PST) intrinsically present in the ferroelectric phase which protects the spin from decoherence and supports extraordinarily long spin lifetime. Our first-principles calculations followed by symmetry arguments revealed that such a spin wave mode can be externally detuned by perpendicular electric field, leading to spin randomization and decrease in spin lifetime. We further extend our analysis to ultrathin SnTe films and confirm the emergence of PST as well as a moderate enhancement of intrinsic spin Hall conductivity. The recent room-temperature observation of the ferroelectric phase in 2D-SnTe suggests that novel all-electric spintronics devices are within reach.

cond-mat.mtrl-sci↗

arXiv2019

Parametrically Constrained Geometry Relaxations for High-Throughput Materials Science

Reducing parameter spaces via exploiting symmetries has greatly accelerated and increased the quality of electronic-structure calculations. Unfortunately, many of the traditional methods fail when the global crystal symmetry is broken, even when the distortion is only a slight perturbation (e.g. Jahn-Teller like distortions). Here we introduce a flexible and generalizable parametric relaxation scheme, and implement it in the all-electron code FHI-aims. This approach utilizes parametric constraints to maintain symmetry at any level. After demonstrating the method's ability to relax metastable structures, we highlight its adaptability and performance over a test set of 359 materials, across thirteen lattice prototypes. Finally we show how these constraints can reduce the number of steps needed to relax local lattice distortions by an order of magnitude. The flexibility of these constraints enables a significant acceleration of the high-throughput searches for novel materials for numerous applications.

cond-mat.mtrl-sci↗

arXiv2019

Predicting Superhard Materials via a Machine Learning Informed Evolutionary Structure Search

Good agreement was found between experimental Vickers hardnesses, $H_\text{v}$, of a wide range of materials and those calculated by three macroscopic hardness models that employ the shear and/or bulk moduli obtained from: (i) first principles via AFLOW-AEL (AFLOW Automatic Elastic Library), and (ii) a machine learning (ML) model trained on materials within the AFLOW repository. Because $H_\text{v}^\text{ML} $ values can be quickly estimated, they can be used in conjunction with an evolutionary search to predict stable, superhard materials. This methodology is implemented in the XtalOpt evolutionary algorithm. Each crystal is minimized to the nearest local minimum, and its Vickers hardness is computed via a linear relationship with the shear modulus discovered by Teter. Both the energy/enthalpy and $H_\text{v, Teter}^{\text{ML}}$ are employed to determine a structure's fitness. This implementation is applied towards the carbon system, and 43 new superhard phases are found. A topological analysis reveals that phases estimated to be slightly harder than diamond contain a substantial fraction of diamond and/or lonsdaleite.

cond-mat.mtrl-sci↗

arXiv2019

Vibrational fingerprintings for chemical recognition of biominerals

Pathologies associated with calcified tissue, such as osteoporosis, demand in vivo and/or in situ spectroscopic analysis to assess the role of chemical substitutions in the inorganic component. High energy X-ray or NMR spectroscopies are often impractical or damaging in biomedical conditions. Low energy spectroscopies, such as IR and Raman techniques, are often the best alternative. In apatite biominerals, the vibrational signatures of the phosphate group are generally used as fingerprint of the materials although they provide only limited information. Here, we have used first principles calculations to unravel the complexity of the complete vibrational spectra of apatites. We determined the spectroscopic features of all the phonon modes of fluor-apatite, hydroxy-apatite, and carbonated fluoroapatite beyond the analysis of the phosphate groups, focusing on the effect of local corrections induced by the crystalline environment and the specific mineral composition. This provides a clear and unique reference to discriminate structural and chemical variations in biominerals, opening the way to a widespread application of non-invasive spectroscopies for in vivo diagnostics, and biomedical analysis.

cond-mat.mtrl-sci↗

arXiv2019

Unavoidable disorder and entropy in multi-component systems

The need for improved functionalities is driving the search for more complicated multi-component materials. Despite the factorially increasing composition space, ordered compounds with 4 or more species are rare. Here, we unveil the competition between the gain in enthalpy and entropy with increasing number of species by statistical analysis of the AFLOW data repositories. A threshold in the number of species is found where entropy gain exceeds enthalpy gain. Beyond that, enthalpy can be neglected, and disorder - complete or partial - is unavoidable.

cond-mat.mtrl-sci↗

arXiv2019

Coordination corrected ab initio formation enthalpies

The correct calculation of formation enthalpy is one of the enablers of ab-initio computational materials design. For several classes of systems (e.g. oxides) standard density functional theory produces incorrect values. Here we propose the "Coordination Corrected Enthalpies" method (CCE), based on the number of nearest neighbor cation-anion bonds, and also capable of correcting relative stability of polymorphs. CCE uses calculations employing the Perdew, Burke and Ernzerhof (PBE), Local Density Approximation (LDA) and Strongly Constrained and Appropriately Normed (SCAN) exchange correlation functionals, in conjunction with a quasiharmonic Debye model to treat zero-point vibrational and thermal effects. The benchmark, performed on binary and ternary oxides (halides), shows very accurate room temperature results for all functionals, with the smallest mean absolute error of 27 (24) meV/atom obtained with SCAN. The zero-point vibrational and thermal contributions to the formation enthalpies are small and with different signs - largely cancelling each other.

cond-mat.mtrl-sci↗

arXiv2019

Giant spin Hall Effect in two-dimensional monochalcogenides

One of the most exciting properties of two dimensional materials is their sensitivity to external tuning of the electronic properties, for example via electric field or strain. Recently discovered analogues of phosphorene, group-IV monochalcogenides (MX with M = Ge, Sn and X = S, Se, Te), display several interesting phenomena intimately related to the in-plane strain, such as giant piezoelectricity and multiferroicity, which combine ferroelastic and ferroelectric properties. Here, using calculations from first principles, we reveal for the first time giant intrinsic spin Hall conductivities (SHC) in these materials. In particular, we show that the SHC resonances can be easily tuned by combination of strain and doping and, in some cases, strain can be used to induce semiconductor to metal transitions that make a giant spin Hall effect possible even in absence of doping. Our results indicate a new route for the design of highly tunable spintronics devices based on two-dimensional materials.

cond-mat.mtrl-sci↗

arXiv2019

Metallic glasses for biodegradable implants

Metallic glasses are excellent candidates for biomedical implant applications due to their inherent strength and corrosion resistance. Use of metallic glasses in structural applications is limited, however, because bulk dimensions are challenging to achieve. Glass-forming ability (GFA) varies strongly with alloy composition and becomes more difficult to predict as the number of chemical species in a system increases. Here we present a theoretical model - implemented in the AFLOW framework - for predicting GFA based on the competition between crystalline phases, and apply it to biologically relevant binary and ternary systems. Elastic properties are estimated based on the rule of mixtures for alloy systems that are predicted to be bulk glass-formers. Focusing on Ca- and Mg-based systems for use in biodegradable orthopedic support applications, we suggest alloys in the AgCaMg and AgMgZn families for further study; and alloys based on the compositions: Ag$_{0.33}$Mg$_{0.67}$, Cu$_{0.5}$Mg$_{0.5}$, Cu$_{0.37}$Mg$_{0.63}$ and Cu$_{0.25}$Mg$_{0.5}$Zn$_{0.25}$.

cond-mat.mtrl-sci↗

arXiv2018

High-entropy high-hardness metal carbides discovered by entropy descriptors

High-entropy materials have attracted considerable interest due to the combination of useful properties and promising applications. Predicting their formation remains the major hindrance to the discovery of new systems. Here we propose a descriptor - entropy forming ability - for addressing synthesizability from first principles. The formalism, based on the energy distribution spectrum of randomized calculations, captures the accessibility of equally-sampled states near the ground state and quantifies configurational disorder capable of stabilizing high-entropy homogeneous phases. The methodology is applied to disordered refractory 5-metal carbides - promising candidates for high-hardness applications. The descriptor correctly predicts the ease with which compositions can be experimentally synthesized as rock-salt high-entropy homogeneous phases, validating the ansatz, and in some cases, going beyond intuition. Several of these materials exhibit hardness up to 50% higher than rule of mixtures estimations. The entropy descriptor method has the potential to accelerate the search for high-entropy systems by rationally combining first principles with experimental synthesis and characterization.

cond-mat.mtrl-sci↗

arXiv2018

The search for high entropy alloys: a high-throughput $\textit{ab-initio}$ approach

While the ongoing search to discover new high-entropy systems is slowly expanding beyond metals, a rational and effective method for predicting "in silico" the solid solution forming ability of multi-component systems remains yet to be developed. In this article, we propose a novel high-throughput approach, called "LTVC", for estimating the transition temperature of a solid solution: ab-initio energies are incorporated into a mean field statistical mechanical model where an order parameter follows the evolution of disorder. The LTVC method is corroborated by Monte Carlo simulations and the results from the current most reliable data for binary, ternary, quaternary and quinary systems (96.6%; 90.7%; 100% and 100%, of correct solid solution predictions, respectively). By scanning through the many thousands of systems available in the AFLOW consortium repository, it is possible to predict a plethora of previously unknown potential quaternary and quinary solid solutions for future experimental validation.

cond-mat.mtrl-sci↗

arXiv2018

AFLOW-QHA3P: Robust and automated method to compute thermodynamic properties of solids

Accelerating the calculations of finite-temperature thermodynamic properties is a major challenge for rational materials design. Reliable methods can be quite expensive, limiting their effective applicability in autonomous high-throughput workflows. Here, the 3-phonons quasi-harmonic approximation (QHA) method is introduced, requiring only three phonon calculations to obtain a thorough characterization of the material. Leveraging a Taylor expansion of the phonon frequencies around the equilibrium volume, the method efficiently resolves the volumetric thermal expansion coefficient, specific heat at constant pressure, the enthalpy, and bulk modulus. Results from the standard QHA and experiments corroborate the procedure, and additional comparisons are made with the recently developed self-consistent QHA. The three approaches - 3-phonons, standard, and self- consistent QHAs - are all included within the automated, open-source framework AFLOW, allowing automated determination of properties with various implementations within the same framework.

cond-mat.mtrl-sci↗

arXiv2018

SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates

The lack of reliable methods for identifying descriptors - the sets of parameters capturing the underlying mechanisms of a materials property - is one of the key factors hindering efficient materials development. Here, we propose a systematic approach for discovering descriptors for materials properties, within the framework of compressed-sensing based dimensionality reduction. SISSO (sure independence screening and sparsifying operator) tackles immense and correlated features spaces, and converges to the optimal solution from a combination of features relevant to the materials' property of interest. In addition, SISSO gives stable results also with small training sets. The methodology is benchmarked with the quantitative prediction of the ground-state enthalpies of octet binary materials (using ab initio data) and applied to the showcase example of predicting the metal/insulator classification of binaries (with experimental data). Accurate, predictive models are found in both cases. For the metal-insulator classification model, the predictive capability are tested beyond the training data: It rediscovers the available pressure-induced insulator->metal transitions and it allows for the prediction of yet unknown transition candidates, ripe for experimental validation. As a step forward with respect to previous model-identification methods, SISSO can become an effective tool for automatic materials development.

cond-mat.mtrl-sci↗

arXiv2018

The AFLOW Library of Crystallographic Prototypes: Part 2

Materials discovery via high-throughput methods relies on the availability of structural prototypes, which are generally decorated with varying combinations of elements to produce potential new materials. To facilitate the automatic generation of these materials, we developed $\textit{The AFLOW Library of Crystallographic Prototypes}$ $\unicode{x2014}$ a collection of crystal prototypes that can be rapidly decorated using the AFLOW software. Part 2 of this work introduces an additional 302 crystal structure prototypes, including at least one from each of the 138 space groups not included in Part 1. Combined with Part 1, the entire library consists of 590 unique crystallographic prototypes covering all 230 space groups. We also present discussions of enantiomorphic space groups, Wigner-Seitz cells, the two-dimensional plane groups, and the various different space group notations used throughout crystallography. All structures $\unicode{x2014}$ from both Part 1 and Part 2 $\unicode{x2014}$ are listed in the web version of the library available at aflow.org/CrystalDatabase.

cond-mat.mtrl-sci↗