arXiv subjects

Stefano Curtarolo

Publications and source records attributed to Stefano Curtarolo.

At least 73 records · Page 4Linked to original sources

arXiv2018

AFLOW-CHULL: Cloud-oriented platform for autonomous phase stability analysis

$\textit{A priori}$ prediction of phase stability of materials is a challenging practice, requiring knowledge of all energetically-competing structures at formation conditions. Large materials repositories $\unicode{x2014}$ housing properties of both experimental and hypothetical compounds $\unicode{x2014}$ offer a path to prediction through the construction of informatics-based, $\textit{ab-initio}$ phase diagrams. However, limited access to relevant data and software infrastructure has rendered thermodynamic characterizations largely peripheral, despite their continued success in dictating synthesizability. Herein, a new module is presented for autonomous thermodynamic stability analysis implemented within the open-source, $\textit{ab-initio}$ framework AFLOW. Powered by the AFLUX Search-API, AFLOW-CHULL leverages data of more than 1.8 million compounds currently characterized in the AFLOW.org repository and can be employed locally from any UNIX-like computer. The module integrates a range of functionality: the identification of stable phases and equivalent structures, phase coexistence, measures for robust stability, and determination of decomposition reactions. As a proof-of-concept, thorough thermodynamic characterizations have been performed for more than 1,300 binary and ternary systems, enabling the identification of several candidate phases for synthesis based on their relative stability criterion $\unicode{x2014}$ including 18 promising $C15_{b}$-type structures and two half-Heuslers. In addition to a full report included herein, an interactive, online web application has been developed showcasing the results of the analysis, and is located at aflow.org/aflow-chull.

cond-mat.mtrl-sci↗

arXiv2018

Autonomous data-driven design of inorganic materials with AFLOW

The expansion of programmatically-accessible materials data has cultivated opportunities for data-driven approaches. Highly-automated frameworks like AFLOW not only manage the generation, storage, and dissemination of materials data, but also leverage the information for thermodynamic formability modeling, such as the prediction of phase diagrams and properties of disordered materials. In combination with standardized parameter sets, the wealth of data is ideal for training machine learning algorithms, which have already been employed for property prediction, descriptor development, design rule discovery, and the identification of candidate functional materials. These methods promise to revolutionize the path to synthesis and, ultimately, transform the practice of traditional materials discovery to one of rational and autonomous materials design.

cond-mat.mtrl-sci↗

arXiv2018

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.

cond-mat.mtrl-sci↗

arXiv2018

Vibrational properties of metastable polymorph structures by machine learning

Despite vibrational properties being critical for the ab initio prediction of the finite temperature stability and transport properties of solids, their inclusion in ab initio materials repositories has been hindered by expensive computational requirements. Here we tackle the challenge, by showing that a good estimation of force constants and vibrational properties can be quickly achieved from the knowledge of atomic equilibrium positions using machine learning. A random-forest algorithm trained on only 121 metastable structures of KZnF$_3$ reaches a maximum absolute error of 0.17 eV/$\textrmÅ^2$ for the interatomic force constants, and it is much less expensive than training the complete force field for such compound. The predicted force constants are then used to estimate phonon spectral features, heat capacities, vibrational entropies, and vibrational free energies, which compare well with the ab initio ones. The approach can be used for the rapid estimation of stability at finite temperatures.

cond-mat.mtrl-sci↗

arXiv2018

Spinodal superlattices of topological insulators

Spinodal decomposition is proposed for stabilizing self-assembled interfaces between topological insulators (TIs) by combining layers of iso-structural and iso-valent TlBi$X_2$ ($X$=S, Se, Te) materials. The composition range for gapless states is addressed concurrently to the study of thermodynamically driven boundaries. By tailoring composition, the TlBiS$_2$-TlBiTe$_2$ system might produce both spinodal superlattices and two dimensional eutectic microstructures, either concurrently or separately. The dimensions and topological nature of the metallic channels are determined by following the spatial distribution of the charge density and the spin-texture. The results validate the proof of concept for obtaining spontaneously forming two-dimensional TI-conducting channels embedded into three-dimensional insulating environments without any vacuum interfaces. Since spinodal decomposition is a controllable kinetic phenomenon, its leverage could become the long-sought enabler for effective TI technological deployment.

cond-mat.mtrl-sci↗

arXiv2018

AFLOW-SYM: Platform for the complete, automatic and self-consistent symmetry analysis of crystals

Determination of the symmetry profile of structures is a persistent challenge in materials science. Results often vary amongst standard packages, hindering autonomous materials development by requiring continuous user attention and educated guesses. Here, we present a robust procedure for evaluating the complete suite of symmetry properties, featuring various representations for the point-, factor-, space groups, site symmetries, and Wyckoff positions. The protocol determines a system-specific mapping tolerance that yields symmetry operations entirely commensurate with fundamental crystallographic principles. The self consistent tolerance characterizes the effective spatial resolution of the reported atomic positions. The approach is compared with the most used programs and is successfully validated against the space group information provided for over 54,000 entries in the Inorganic Crystal Structure Database. Subsequently, a complete symmetry analysis is applied to all 1.7$+$ million entries of the AFLOW data repository. The AFLOW-SYM package has been implemented in, and made available for, public use through the automated, $\textit{ab-initio}$ framework AFLOW.

cond-mat.mtrl-sci↗

arXiv2017

The structure and composition statistics of 6A binary and ternary crystalline materials

The fundamental principles underlying the arrangement of elements into solid compounds with an enormous variety of crystal structures are still largely unknown. This study presents a general overview of the structure types appearing in an important subset of the solid compounds, i.e., binary and ternary compounds of the 6A column oxides, sulfides and selenides. It contains an analysis of these compounds, including the prevalence of various structure types, their symmetry properties, compositions, stoichiometries and unit cell sizes. It is found that these compound families include preferred stoichiometries and structure types that may reflect both their specific chemistry and research bias in the available empirical data. Identification of non-overlapping gaps and missing stoichiometries in these structure populations may be used as guidance in the search for new materials.

cond-mat.mtrl-sci↗

arXiv2017

The AFLOW Fleet for Materials Discovery

The traditional paradigm for materials discovery has been recently expanded to incorporate substantial data driven research. With the intent to accelerate the development and the deployment of new technologies, the AFLOW Fleet for computational materials design automates high-throughput first principles calculations, and provides tools for data verification and dissemination for a broad community of users. AFLOW incorporates different computational modules to robustly determine thermodynamic stability, electronic band structures, vibrational dispersions, thermo-mechanical properties and more. The AFLOW data repository is publicly accessible online at aflow.org, with more than 1.7 million materials entries and a panoply of queryable computed properties. Tools to programmatically search and process the data, as well as to perform online machine learning predictions, are also available.

cond-mat.mtrl-sci↗

arXiv2017

AFLOW-ML: A RESTful API for machine-learning predictions of materials properties

Machine learning approaches, enabled by the emergence of comprehensive databases of materials properties, are becoming a fruitful direction for materials analysis. As a result, a plethora of models have been constructed and trained on existing data to predict properties of new systems. These powerful methods allow researchers to target studies only at interesting materials $\unicode{x2014}$ neglecting the non-synthesizable systems and those without the desired properties $\unicode{x2014}$ thus reducing the amount of resources spent on expensive computations and/or time-consuming experimental synthesis. However, using these predictive models is not always straightforward. Often, they require a panoply of technical expertise, creating barriers for general users. AFLOW-ML (AFLOW $\underline{\mathrm{M}}$achine $\underline{\mathrm{L}}$earning) overcomes the problem by streamlining the use of the machine learning methods developed within the AFLOW consortium. The framework provides an open RESTful API to directly access the continuously updated algorithms, which can be transparently integrated into any workflow to retrieve predictions of electronic, thermal and mechanical properties. These types of interconnected cloud-based applications are envisioned to be capable of further accelerating the adoption of machine learning methods into materials development.

cond-mat.mtrl-sci↗

arXiv2017

Machine learning modeling of superconducting critical temperature

Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between superconductivity and chemical/structural properties of materials. To bridge the gap, several machine learning schemes are developed herein to model the critical temperatures ($T_{\mathrm{c}}$) of the 12,000+ known superconductors available via the SuperCon database. Materials are first divided into two classes based on their $T_{\mathrm{c}}$ values, above and below 10 K, and a classification model predicting this label is trained. The model uses coarse-grained features based only on the chemical compositions. It shows strong predictive power, with out-of-sample accuracy of about 92%. Separate regression models are developed to predict the values of $T_{\mathrm{c}}$ for cuprate, iron-based, and "low-$T_{\mathrm{c}}$" compounds. These models also demonstrate good performance, with learned predictors offering potential insights into the mechanisms behind superconductivity in different families of materials. To improve the accuracy and interpretability of these models, new features are incorporated using materials data from the AFLOW Online Repositories. Finally, the classification and regression models are combined into a single integrated pipeline and employed to search the entire Inorganic Crystallographic Structure Database (ICSD) for potential new superconductors. We identify more than 30 non-cuprate and non-iron-based oxides as candidate materials.

cond-mat.supr-con↗

arXiv2017

Improved electronic structure and magnetic exchange interactions in transition metal oxides

We discuss the application of the Agapito Curtarolo and Buongiorno Nardelli (ACBN0) pseudo-hybrid Hubbard density functional to several transition metal oxides. ACBN0 is a fast, accurate and parameter-free alternative to traditional DFT+$U$ and hybrid exact exchange methods. In ACBN0, the Hubbard energy of DFT+$U$ is calculated via the direct evaluation of the local Coulomb and exchange integrals in which the screening of the bare Coulomb potential is accounted for by a renormalization of the density matrix. We demonstrate the success of the ACBN0 approach for the electronic properties of a series technologically relevant mono-oxides (MnO, CoO, NiO, FeO, both at equilibrium and under pressure). We also present results on two mixed valence compounds, Co$_3$O$_4$ and Mn$_3$O$_4$. Our results, obtained at the computational cost of a standard LDA/PBE calculation, are in excellent agreement with hybrid functionals, the GW approximation and experimental measurements.

cond-mat.mtrl-sci↗

arXiv2017

Universal Fragment Descriptors for Predicting Electronic Properties of Inorganic Crystals

Historically, materials discovery has been driven by a laborious trial-and-error process. The growth of materials databases and emerging informatics approaches finally offer the opportunity to transform this practice into data- and knowledge-driven rational design. By using data from the AFLOW repository for high-throughput ab-initio calculations, we have generated Quantitative Materials Structure-Property Relationship (QMSPR) models to predict eight critical electronic and thermomechanical materials properties, such as the metal/insulator classification, band gap energy, bulk and shear moduli, Debye temperature, and heat capacity. The prediction accuracy obtained with these QMSPR models approaches training data for virtually any stoichiometric inorganic crystalline material. The success and universality of these models is attributed to the construction of new materials descriptors---referred to as the universal Property-Labeled Materials Fragments (PLMF). The representation requires only minimal structural input and affords straightforward model interpretation in terms of simple heuristic design rules that guide rational materials design. This study demonstrates the power of materials informatics to dramatically accelerate the search for new materials.

cond-mat.mtrl-sci↗

arXiv2017

Combining the AFLOW GIBBS and Elastic Libraries for efficiently and robustly screening thermo-mechanical properties of solids

Thorough characterization of the thermo-mechanical properties of materials requires difficult and time-consuming experiments. This severely limits the availability of data and it is one of the main obstacles for the development of effective accelerated materials design strategies. The rapid screening of new potential systems requires highly integrated, sophisticated and robust computational approaches. We tackled the challenge by surveying more than 3,000 crystalline solids within the AFLOW framework with the newly developed "Automatic Elasticity Library" combined with the previously implemented GIBBS method. The first extracts the mechanical properties from automatic self-consistent stress-strain calculations, while the latter employs those mechanical properties to evaluate the thermodynamics within the Debye model. The new thermo-elastic library is benchmarked against a set of 74 experimentally characterized systems to pinpoint a robust computational methodology for the evaluation of bulk and shear moduli, Poisson ratios, Debye temperatures, Grüneisen parameters, and thermal conductivities of a wide variety of materials. The effect of different choices of equations of state is examined and the optimum combination of properties for the Leibfried-Schlömann prediction of thermal conductivity is identified, leading to improved agreement with experimental results than the GIBBS-only approach.

cond-mat.mtrl-sci↗

arXiv2017

How the Chemical Composition Alone Can Predict Vibrational Free Energies and Entropies of Solids

Computing vibrational free energies ($F_{vib}$) and entropies ($S_{vib}$) has posed a long standing challenge to the high-throughput ab initio investigation of finite temperature properties of solids. Here we use machine-learning techniques to efficiently predict $F_{vib}$ and $S_{vib}$ of crystalline compounds in the Inorganic Crystal Structure Database. By employing descriptors based simply on the chemical formula and using a training set of only 300 compounds, mean absolute errors of less than 0.04 meV/K/atom (15 meV/atom) are achieved for $S_{vib}$ ($F_{vib}$), whose values are distributed within a range of 0.9 meV/K/atom (300 meV/atom.) In addition, for training sets containing fewer than 2,000 compounds the chemical formula alone is shown to perform as well as, if not better than, four other more complex descriptors previously used in the literature. The accuracy and simplicity of the approach mean that it can be advantageously used for the fast screening of phase diagrams or chemical reactions at finite temperatures.

cond-mat.mtrl-sci↗

arXiv2016

AFLUX: The LUX materials search API for the AFLOW data repositories

Automated computational materials science frameworks rapidly generate large quantities of materials data useful for accelerated materials design. We have extended the data oriented AFLOW-repository API (Application-Program-Interface, as described in Comput. Mater. Sci. 93, 178 (2014)) to enable programmatic access to search queries. A URI-based search API (Uniform Resource Identifier) is proposed for the construction of complex queries with the intent of allowing the remote creation and retrieval of customized data sets. It is expected that the new language AFLUX, acronym for Automatic Flow of LUX (light), will facilitate the creation of remote search operations on the AFLOW.org set of computational materials science data repositories.

cond-mat.mtrl-sci↗

arXiv2016

The AFLOW Library of Crystallographic Prototypes

An easily available resource of common crystal structures is essential for researchers, teachers, and students. For many years this was provided by the U.S. Naval Research Laboratory's $Crystal\ Lattice\ Structures$ web page, which contained nearly 300 crystal structures, including a majority of those which were given $Strukturbericht$ designations. This article presents the updated version of the database, now including 288 standardized structures in 92 space groups. Similar to what was available on the web page before, we present a complete description of each structure, including the formulas for the primitive vectors, all of the basis vectors, and the AFLOW commands to generate the standardied cells. We also present a brief discussion of crystal systems, space groups, primitive and conventional lattices, Wyckoff positions, Pearson symbols and $Strukturbericht$ designations.

cond-mat.mtrl-sci↗

arXiv2016

Predicting the lattice thermal conductivity of solids by solving the Boltzmann transport equation: AFLOW - AAPL an automated, accurate and effcient framework

One of the most accurate approaches for calculating lattice thermal conductivity, $κ_l$, is solving the Boltzmann transport equation starting from third-order anharmonic force constants. In addition to the underlying approximations of ab-initio parameterization, two main challenges are associated with this path. High computational costs and lack of automation in the frameworks using this methodology affect the discovery rate of novel materials with ad-hoc properties. Here, we present the Automatic-Anharmonic-Phonon-Library, AAPL. It efficiently computes interatomic force constants by making effective use of crystal symmetry analysis, it solves the Boltzmann transport equation to obtain $κ_l$, and allows a fully integrated operation with minimum user intervention, a rational addition to the current high-throughput accelerated materials development framework AFLOW. We show an "experiment versus theory" study of the approach, we compare accuracy and speed with respect to other available packages, and for materials characterized by strong electron localization and correlation, we demonstrate that it is possible to improve accuracy without increasing computational requirements by combining AAPL with the pseudo-hybrid functional ACBN0.

cond-mat.mtrl-sci↗

arXiv2016

Addressing the lattice stability puzzle in the computational determination of intermetallic phase diagrams

The evaluation of phase stabilities of unstable elemental phases is a long-standing problem in the computational assessment of phase diagrams. Here we tackle this problem by explicitly calculating phase diagrams of intermetallic systems where its effect should be most conspicuous, binary systems of titanium with bcc transition metals (Mo, Nb, Ta and V). Two types of phase diagrams are constructed: one based on the lattice stabilities extracted from empirical data, and the other using the lattice stabilities computed from first principles. It is shown that the phase diagrams obtained using the empirical values contain clear contradictions with the experimental phase diagrams at the well known limits of low or high temperatures. Realistic phase diagrams, with a good agreement with the experimental observations, are achieved only when the computed lattice stability values are used. At intermediate temperatures, the computed phase diagrams resolve the controversy regarding the shape of the solvus in these systems, predicting a complex structure with a eutectoid transition and a miscibility gap between two bcc phases.

cond-mat.mtrl-sci↗