arXiv subjects

Marco Fornari

Publications and source records attributed to Marco Fornari.

At least 19 recordsLinked to original sources

arXiv2023

Magnetic Transparent Conductors for Spintronic Applications

Transparent Conductors (TCs) exhibit optical transparency and electron conductivity, and are essential for many opto-electronic and photo-voltaic devices. The most common TCs are electron-doped oxides, which have few limitations when transition metals are used as dopants. Non-oxides TCs have the potential of extending the class of materials to the magnetic realm, bypass technological bottlenecks, and bring TCs to the field of spintronics. Here we propose new functional materials that combine transparency and conductivity with magnetic spin polarization that can be used for spintronic applications, such as spin filters. By using high-throughput first-principles techniques, we identified a large number of potential TCs, including non-oxides materials. Our results indicate that proper doping with transition metals introduces a finite magnetization that can provide spin filtering up to 90% in the electrical conductivity, still maintaining a transparency greater than 90%.

cond-mat.mtrl-sci↗

arXiv2023

How to verify the precision of density-functional-theory implementations via reproducible and universal workflows

In the past decades many density-functional theory methods and codes adopting periodic boundary conditions have been developed and are now extensively used in condensed matter physics and materials science research. Only in 2016, however, their precision (i.e., to which extent properties computed with different codes agree among each other) was systematically assessed on elemental crystals: a first crucial step to evaluate the reliability of such computations. We discuss here general recommendations for verification studies aiming at further testing precision and transferability of density-functional-theory computational approaches and codes. We illustrate such recommendations using a greatly expanded protocol covering the whole periodic table from Z=1 to 96 and characterizing 10 prototypical cubic compounds for each element: 4 unaries and 6 oxides, spanning a wide range of coordination numbers and oxidation states. The primary outcome is a reference dataset of 960 equations of state cross-checked between two all-electron codes, then used to verify and improve nine pseudopotential-based approaches. Such effort is facilitated by deploying AiiDA common workflows that perform automatic input parameter selection, provide identical input/output interfaces across codes, and ensure full reproducibility. Finally, we discuss the extent to which the current results for total energies can be reused for different goals (e.g., obtaining formation energies).

cond-mat.mtrl-sci↗

arXiv2022

Microscopic picture of paraelectric perovskites from structural prototypes

We show with first-principles molecular dynamics the persistence of intrinsic $\langle111\rangle$ Ti off-centerings for BaTiO$_3$ in its cubic paraelectric phase. Intriguingly, these are inconsistent with the Pm$\bar 3$m space group often used to atomistically model this phase using density functional theory or similar methods. Therefore we deploy a systematic symmetry analysis to construct representative structural models in the form of supercells that satisfy a desired point symmetry but are built from the combination of lower-symmetry primitive cells. We define as structural prototypes the smallest of these that are both energetically and dynamically stable. Remarkably, two 40-atom prototypes can be identified for paraelectric BaTiO$_3$; these are also common to many other $AB$O$_3$ perovskites. These prototypes can offer structural models of paraelectric phases that can be used for the computational engineering of functional materials. Last, we show that the emergence of B-cation off-centerings and the primitive-cell phonon instabilities are controlled by the equilibrium volume, in turn dictated by the filler A cation.

cond-mat.mtrl-sci↗

arXiv2021

Relaxation time approximations in PAOFLOW 2.0

Regardless of its success, the constant relaxation time approximation has limited validity. Temperature and energy dependent effects are important to match experimental trends even in simple situations. We present the implementation of relaxation time approximation models in the calculation of Boltzmann transport in PAOFLOW 2.0 and apply those to model band-structures. In addition, using a self-consistent fitting of the model parameters to experimental conductivity data, we provide a flexible tool to extract scattering rates with high accuracy. We illustrate the approximations using simple models and then apply the method to GaAs, Si, Mg3Sb2, and CoSb3.

cond-mat.mtrl-sci↗

arXiv2021

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.

cond-mat.mtrl-sci↗

arXiv2021

Advanced modeling of materials with PAOFLOW 2.0: New features and software design

Recent research in materials science opens exciting perspectives to design novel quantum materials and devices, but it calls for quantitative predictions of properties which are not accessible in standard first principles packages. PAOFLOW is a software tool that constructs tight-binding Hamiltonians from self-consistent electronic wavefunctions by projecting onto a set of atomic orbitals. The electronic structure provides numerous materials properties that otherwise would have to be calculated via phenomenological models. In this paper, we describe recent re-design of the code as well as the new features and improvements in performance. In particular, we have implemented symmetry operations for unfolding equivalent k-points, which drastically reduces the runtime requirements of first principles calculations, and we have provided internal routines of projections onto atomic orbitals enabling generation of real space atomic orbitals. Moreover, we have included models for non-constant relaxation time in electronic transport calculations, doubling the real space dimensions of the Hamiltonian as well as the construction of Hamiltonians directly from analytical models. Importantly, PAOFLOW has been now converted into a Python package, and is streamlined for use directly within other Python codes. The new object oriented design treats PAOFLOWs computational routines as class methods, providing an API for explicit control of each calculation.

cond-mat.mtrl-sci↗

arXiv2021

Hyperbolic metamaterials with extreme mechanical hardness

Hyperbolic metamaterials (HMMs) are highly anisotropic optical materials that behave as metals or as dielectrics depending on the direction of propagation of light. They are becoming essential for a plethora of applications, ranging from aerospace to automotive, from wireless to medical and IoT. These applications often work in harsh environments or may sustain remarkable external stresses. This calls for materials that show enhanced optical properties as well as tailorable mechanical properties. Depending on their specific use, both hard and ultrasoft materials could be required, although the combination with optical hyperbolic response is rarely addressed. Here, we demonstrate the possibility to combine optical hyperbolicity and tunable mechanical properties in the same (meta)material, focusing on the case of extreme mechanical hardness. Using high-throughput calculations from first principles and effective medium theory, we explored a large class of layered materials with hyperbolic optical activity in the near-IR and visible range, and we identified a reduced number of ultrasoft and hard HMMs among more than 1800 combinations of transition metal rocksalt crystals. Once validated by the experiments, this new class of metamaterials may foster previously unexplored optical/mechanical applications.

physics.optics↗

arXiv2020

Vacancies in graphene: an application of adiabatic quantum optimization

Quantum annealers have grown in complexity to the point that quantum computations involving few thousands of qubits are now possible. In this paper, \textcolor{black}{with the intentions to show the feasibility of quantum annealing to tackle problems of physical relevance, we used a simple model, compatible with the capability of current quantum annealers, to study} the relative stability of graphene vacancy defects. By mapping the crucial interactions that dominate carbon-vacancy interchange onto a quadratic unconstrained binary optimization problem, our approach exploits \textcolor{black}{the ground state as well the excited states found by} the quantum annealer to extract all the possible arrangements of multiple defects on the graphene sheet together with their relative formation energies. This approach reproduces known results and provides a stepping stone towards applications of quantum annealing to problems of physical-chemical interest.

cond-mat.mtrl-sci↗

arXiv2020

Investigating the Chinese Postman Problem on a Quantum Annealer

The recent availability of quantum annealers has fueled a new area of information technology where such devices are applied to address practically motivated and computationally difficult problems with hardware that exploits quantum mechanical phenomena. D-Wave annealers are promising platforms to solve these problems in the form of quadratic unconstrained binary optimization. Here we provide a formulation of the Chinese postman problem that can be used as a tool for probing the local connectivity of graphs and networks. We treat the problem classically with a tabu algorithm and using a D-Wave device. We systematically analyze computational parameters associated with the specific hardware. Our results clarify how the interplay between the embedding due to limited connectivity of the Chimera graph, the definition of logical qubits, and the role of spin-reversal controls the probability of reaching the expected solution.

quant-ph↗

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

Machine Learning the Voltage of Electrode Materials in Metal-ion Batteries

Machine learning (ML) techniques have rapidly found applications in many domains of materials chemistry and physics where large data sets are available. Aiming to accelerate the discovery of materials for battery applications, in this work, we develop a tool (http://se.cmich.edu/batteries) based on ML models to predict voltages of electrode materials for metal-ion batteries. To this end, we use deep neural network, support vector machine, and kernel ridge regression as ML algorithms in combination with data taken from the Materials Project Database, as well as feature vectors from properties of chemical compounds and elemental properties of their constituents. We show that our ML models have predictive capabilities for different reference test sets and, as an example, we utilize them to generate a voltage profile diagram and compare it to density functional theory calculations. In addition, using our models, we propose nearly 5,000 candidate electrode materials for Na- and K-ion batteries. We also make available a web-accessible tool that, within a minute, can be used to estimate the voltage of any bulk electrode material for a number of metal-ions. These results show that ML is a promising alternative for computationally demanding calculations as a first screening tool of novel materials for battery applications.

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↗

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

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↗

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

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

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↗