arXiv subjects

Francesca Tavazza

Publications and source records attributed to Francesca Tavazza.

36 records · Page 2Linked to original sources

arXiv2020

Predicting Anomalous Quantum Confinement Effect in van der Waals Materials

Materials with van der Waals-bonding are known to exhibit quantum confinement effect, in which the electronic bandgap of the three-dimensional (3D) realization of a material is lower than that of its two-dimensional (2D) counterpart. However, the possibility of an anomalous quantum confinement effect (AQCE) exists, where the bandgap trend is reversed. In this work, we computationally identify materials for which such AQCE occurs. Using density functional theory (DFT), we compute ~1000 OptB88vdW (semi-local functional), ~50 HSE06 and ~50 PBE0 (hybrid functional) bandgaps for bulk and their corresponding monolayers in the JARVIS-DFT database. OptB88vdW identifies 65 AQCE materials, but the hybrid functionals only confirm such finding in 14 cases. Some of the AQCE systems identified through HSE06 and PBE0 are: hydroxides or oxide hydroxide compounds (AlOH2, Mg(OH)2, Mg2H2O3, Ni(OH)2, SrH2O3) as well as Sb-halogen-chalcogenide compounds (SbSBr, SbSeI) and alkali-chalcogenides (RbLiS and RbLiSe). A detailed electronic structure analysis, based on band-structure and projected density of states, shows AQCE is often characterized by lowering of the conduction band in the monolayer and corresponding changes in the pz electronic orbital contribution, with z being the non-periodic direction in the 2D case. We believe our computational results would spur the effort to validate the results experimentally and will have impact on bandgap engineering applications based on low-dimensional materials.

cond-mat.mtrl-sci↗

arXiv2020

Examining Experimental Raman Mode Behavior in Mono- and Bi-layer 2H-TaSe$_{2}$ via Density Functional Theory

Tantalum diselenide (TaSe$_{2}$) is a metallic transition metal dichalcogenide whose equilibrium structure and vibrational behavior strongly depends on temperature and thickness, including the emergence of charge density wave (CDW) states at very low T. In this work, observed modes for mono- and bi-layer are described across several spectral regions and com-pared to the bulk ones. Such modes, including an experimentally observed forbidden Raman mode and low frequency CDW modes, are then matched to corresponding density functional theory (DFT) predicted vibrations, to unveil their inner working. The excellent match between experimental and computational results justifies the presented vibrational visualizations of these modes. Additional support is provided by experimental phonons seen in Raman spectra as a function of temperature and thickness. These results highlight the importance of understanding interlayer interactions and their effects on mode behaviors.

cond-mat.mtrl-sci↗

arXiv2020

Uncertainty Quantification in Atomistic Modeling of Metals and its Effect on Mesoscale and Continuum Modeling A Review

The design of next-generation alloys through the Integrated Computational Materials Engineering (ICME) approach relies on multi-scale computer simulations to provide thermodynamic properties when experiments are difficult to conduct. Atomistic methods such as Density Functional Theory (DFT) and Molecular Dynamics (MD) have been successful in predicting properties of never before studied compounds or phases. However, uncertainty quantification (UQ) of DFT and MD results is rarely reported due to computational and UQ methodology challenges. Over the past decade, studies have emerged that mitigate this gap. These advances are reviewed in the context of thermodynamic modeling and information exchange with mesoscale methods such as Phase Field Method (PFM) and Calculation of Phase Diagrams (CALPHAD). The importance of UQ is illustrated using properties of metals, with aluminum as an example, and highlighting deterministic, frequentist and Bayesian methodologies. Challenges facing routine uncertainty quantification and an outlook on addressing them are also presented.

physics.comp-ph↗

arXiv2020

High-throughput Density Functional Perturbation Theory and Machine Learning Predictions of Infrared, Piezoelectric and Dielectric Responses

Many technological applications depend on the response of materials to electric fields, but available databases of such responses are limited. Here, we explore the infrared, piezoelectric and dielectric properties of inorganic materials by combining high-throughput density functional perturbation theory and machine learning approaches. We compute Γ-point phonons, infrared intensities, Born-effective charges, piezoelectric, and dielectric tensors for 5015 non-metallic materials in the JARVIS-DFT database. We find 3230 and 1943 materials with at least one far and mid-infrared mode, respectively. We identify 577 high-piezoelectric materials, using a threshold of 0.5 C/m2. Using a threshold of 20, we find 593 potential high-dielectric materials. Importantly, we analyze the chemistry, symmetry, dimensionality, and geometry of the materials to find features that help explain variations in our datasets. Finally, we develop high-accuracy regression models for the highest infrared frequency and maximum Born-effective charges, and classification models for maximum piezoelectric and average dielectric tensors to accelerate discovery.

cond-mat.mtrl-sci↗

arXiv2020

Computational Search for Magnetic and Non-magnetic 2D Topological Materials using Unified Spin-orbit Spillage Screening

Two-dimensional topological materials (TMs) have a variety of properties that make them attractive for applications including spintronics and quantum computation. However, there are only a few such experimentally known materials. To help discover new 2D TMs, we develop a unified and computationally inexpensive approach to identify magnetic and non-magnetic 2D TMs, including gapped and semi-metallic topological classifications, in a high-throughput way using density functional theory-based spin-orbit spillage, Wannier-interpolation, and related techniques. We first compute the spin-orbit spillage for the ~1000 2D materials in the JARVIS-DFT dataset (https://www.ctcms.nist.gov/~knc6/JVASP.html ), resulting in 122 materials with high-spillage values. Then, we use Wannier-interpolation to carry-out Z2, Chern-number, anomalous Hall conductivity, Curie temperature, and edge state calculations to further support the predictions. We identify various topologically non-trivial classes such as quantum spin-hall insulators (QSHI), quantum anomalous-hall insulators (QAHI), and semimetals. For a few predicted materials, we run G0W0+SOC and DFT+U calculations. We find that as we introduce many-body effects, only a few materials retain non-trivial band-topology, suggesting the importance of high-level DFT methods in predicting 2D topological materials. However, as an initial step, the automated spillage screening and Wannier-approach provide useful predictions for finding new topological materials and to narrow down candidates for experimental synthesis and characterization.

cond-mat.mtrl-sci↗

arXiv2020

Data-driven Discovery of 3D and 2D Thermoelectric Materials

In this work, we first perform a systematic search for high-efficiency three-dimensional (3D) and two-dimensional (2D) thermoelectric materials by combining semiclassical transport techniques with density functional theory (DFT) calculations and then train machine-learning models on the thermoelectric data. Out of 36000 three-dimensional and 900 two-dimensional materials currently in the publicly available JARVIS-DFT database, we identify 2932 3D and 148 2D promising thermoelectric materials using a multi-steps screening procedure, where specific thresholds are chosen for key quantities like bandgaps, Seebeck coefficients and power factors. We compute the Seebeck coefficients for all the materials currently in the database and validate our calculations by comparing our results, for a subset of materials, to experimental and existing computational datasets. We also investigate the effect of chemical, structural, crystallographic and dimensionality trends on thermoelectric performance. We predict several classes of efficient 3D and 2D materials such as Ba(MgX)2 (X=P,As,Bi), X2YZ6 (X=K,Rb, Y=Pd,Pt, Z=Cl,Br), K2PtX2(X=S,Se), NbCu3X4 (X=S,Se,Te), Sr2XYO6 (X=Ta, Zn, Y=Ga, Mo), TaCu3X4 (X=S, Se,Te), and XYN (X=Ti, Zr, Y=Cl, Br). Finally, as high-throughput DFT is computationally expensive, we train machine learning models using gradient boosting decision trees (GBDT) and classical force-field inspired descriptors (CFID) for n-and p-type Seebeck coefficients and power factors, to quickly pre-screen materials for guiding the next set of DFT calculations. The dataset and tools are made publicly available at the websites: https://www.ctcms.nist.gov/~knc6/JVASP.html , https://www.ctcms.nist.gov/jarvisml/ and https://jarvis.nist.gov/ .

cond-mat.mtrl-sci↗

arXiv2020

Uncertainty Quantification for Materials Properties in Density Functional Theory with k-Point Density

Many computational databases emerged over the last five years that report material properties calculated with density functional theory. The properties in these databases are commonly calculated to a precision that is set by choice of the basis set and the k-point density for the Brillouin zone integration. We determine how the precision of properties obtained from the Birch equation of state for 29 transition metals and aluminum in the three common structures -- fcc, bcc, and hcp -- correlate with the k-point density and the precision of the energy. We show that the precision of the equilibrium volume, bulk modulus, and the pressure derivative of the bulk modulus correlate comparably well with the k-point density and the precision of the energy, following an approximate power law. We recommend the k-point density as the convergence parameter because it is computationally efficient, easy to use as a direct input parameter, and correlates with property precision at least as well as the energy precision. We predict that common k-point density choices in high throughput DFT databases result in precision for the volume of 0.1%, the bulk modulus of 1%, and the pressure derivative of 10%.

cond-mat.mtrl-sci↗

arXiv2019

Density Functional Theory and Deep-learning to Accelerate Data Analytics in Scanning Tunneling Microscopy

We introduce the first systematic database of scanning tunneling microscope (STM) images obtained using density functional theory (DFT) for two-dimensional (2D) materials, calculated using the Tersoff-Hamann method. It currently contains data for 716 exfoliable 2D materials. Examples of the five possible Bravais lattice types for 2D materials and their Fourier-transforms are discussed. All the computational STM images generated in this work will be made available on the JARVIS-DFT website (https://www.ctcms.nist.gov/~knc6/JVASP.html). We find excellent qualitative agreement between the computational and experimental STM images for selected materials. As a first example application of this database, we train a convolution neural network (CNN) model to identify Bravais lattices from the STM images. We believe the model can aid high-throughput experimental data analysis. These computational STM images can directly aid the identification of phases, analyzing defects and lattice-distortions in experimental STM images, as well as be incorporated in the autonomous experiment workflows.

cond-mat.mtrl-sci↗

arXiv2019

Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods

Solar-energy plays an important role in solving serious environmental problems and meeting high-energy demand. However, the lack of suitable materials hinders further progress of this technology. Here, we present the largest inorganic solar-cell material search to date using density functional theory (DFT) and machine-learning approaches. We calculated the spectroscopic limited maximum efficiency (SLME) using Tran-Blaha modified Becke-Johnson potential for 5097 non-metallic materials and identified 1997 candidates with an SLME higher than 10%, including 934 candidates with suitable convex-hull stability and effective carrier mass. Screening for 2D-layered cases, we found 58 potential materials and performed G0W0 calculations on a subset to estimate the prediction-uncertainty. As the above DFT methods are still computationally expensive, we developed a high accuracy machine learning model to pre-screen efficient materials and applied it to over a million materials. Our results provide a general framework and universal strategy for the design of high-efficiency solar cell materials. The data and tools are publicly distributed at: https://www.ctcms.nist.gov/~knc6/JVASP.html, https://www.ctcms.nist.gov/jarvisml/, https://jarvis.nist.gov/ and https://github.com/usnistgov/jarvis .

cond-mat.mtrl-sci↗

arXiv2019

High-throughput Discovery of Topologically Non-trivial Materials using Spin-orbit Spillage

We present a novel methodology to identify topologically non-trivial materials based on band inversion induced by spin-orbit coupling (SOC) effect. Specifically, we compare the density functional theory (DFT) based wavefunctions with and without spin-orbit coupling and compute the spin-orbit-spillage as a measure of band-inversion. Due to its ease of calculation, without any need for symmetry analysis or dense k-point interpolation, the spillage is an excellent tool for identifying topologically non-trivial materials. Out of 30000 materials available in the JARVIS-DFT database, we applied this methodology to more than 4835 non-magnetic materials consisting of heavy atoms and low bandgaps. We found 1868 candidate materials with high-spillage (using 0.5 as a threshold). We validated our methodology by carrying out conventional Wannier-interpolation calculations for 289 candidate materials. We demonstrate that in addition to Z2 topological insulators, this screening method successfully identified many semimetals and topological crystalline insulators. Importantly, our approach is applicable to the investigation of disordered or distorted as well as magnetic materials, because it is not based on symmetry considerations. We discuss some individual example materials, as well as trends throughout our dataset, which is available at the websites: https://www.ctcms.nist.gov/~knc6/JVASP.html and https://jarvis.nist.gov/.

cond-mat.mtrl-sci↗

arXiv2019

Convergence and machine learning predictions of Monkhorst-Pack k-points and plane-wave cut-off in high-throughput DFT calculations

In this work, we developed an automatic convergence procedure for k-points and plane wave cut-off in density functional (DFT) calculations and applied it to more than 30000 materials. The computational framework for automatic convergence can take a user-defined input as a convergence criterion. For k-points, we converged energy per cell (EPC) to 0.001 eV/cell tolerance and compared the results with those obtained using an energy per atom (EPA) convergence criteria of 0.001 eV/atom. From the analysis of our results, we could relate k-point density and plane wave cut-off to material parameters such as density, the slope of bands, number of band-crossings, the maximum plane-wave cut-off used in pseudopotential generation, crystal systems, and the number of unique species in materials. We also identified some material species that would require more careful convergence than others. Moreover, we statistically investigated the dependence of k-points and cutoff on exchange-correlation functionals. We utilized all this data to train machine learning models to predict the k-point line density and plane-wave cut-off for generalized materials. This would provide users with a good starting point towards converged DFT calculations. The code used, and the converged data are available on the following websites: https://jarvis.nist.gov/, and https://github.com/usnistgov/jarvis .

cond-mat.mtrl-sci↗

arXiv2018

Prediction of Weyl semimetal, AFM topological insulator, nodal line semimetal, and Chern insulator phases in Bi2MnSe4

Three dimensional materials with strong spin-orbit coupling and magnetic interactions represent an opportunity to realize a variety of rare and potentially useful topological phases. In this work, we use first principles calculations to show that the recently synthesized material Bi2MnSe4 displays a combination of band inversion and magnetic interactions, leading to several topological phases. Bi2PbSe4, also studied, also displays band inversion and is a topological insulator. In bulk form, the ferromagnetic phase of Bi2MnSe4 is either a nodal line or Weyl semimetal, depending on the direction of the spins. When the spins are arranged in a layered antiferromagnetic configuration, the combination of time reversal plus a partial translation is a new symmetry, and the material instead becomes an antiferromagnetic topological insulator. However, the intrinsic TRS breaking at the surface of Bi2MnSe4 removes the typical Dirac cone feature, allowing the observation of the half-integer quantum anomalous Hall effect (AHC). Furthermore, we show that in thin film form, for some thicknesses, Bi2MnSe4 becomes a Chern insulator with a band gap of up to 58 meV. This combination of properties in a stoichiometric magnetic material makes Bi2MnSe4 an excellent candidate for displaying robust topological behavior.

cond-mat.mtrl-sci↗

arXiv2018

High-throughput assessment of vacancy formation and surface energies of materials using classical force-fields

In this work, we present an open access database for surface and vacancy-formation energies using classical force-fields (FFs). These quantities are essential in understanding diffusion behavior, nanoparticle formation and catalytic activities. FFs are often designed for a specific application, hence, this database allows the user to understand whether a FF is suitable for investigating particular defect and surface-related material properties. The FF results are compared to density functional theory and experimental data whenever applicable for validation. At present, we have 17,506 surface energies and 1,000 vacancy formation energies calculation in our database and the database is still growing. All the data generated, and the computational tools used, are shared publicly at the following websites https://www.ctcms.nist.gov/~knc6/periodic.html, https://jarvis.nist.gov and https://github.com/usnistgov/jarvis . Approximations used during the high-throughput calculations are clearly mentioned. Using some of the example cases, we show how our data can be used to directly compare different FFs for a material and to interpret experimental findings such as using Wulff construction for predicting equilibrium shape of nanoparticles. Similarly, the vacancy formation energies data can be useful in understanding diffusion related properties.

cond-mat.mtrl-sci↗

arXiv2018

Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape

We present a complete set of chemo-structural descriptors to significantly extend the applicability of machine-learning (ML) in material screening and mapping energy landscape for multicomponent systems. These new descriptors allow differentiating between structural prototypes, which is not possible using the commonly used chemical-only descriptors. Specifically, we demonstrate that the combination of pairwise radial, nearest neighbor, bond-angle, dihedral-angle and core-charge distributions plays an important role in predicting formation energies, bandgaps, static refractive indices, magnetic properties, and modulus of elasticity for three-dimensional (3D) materials as well as exfoliation energies of two-dimensional (2D) layered materials. The training data consists of 24549 bulk and 616 monolayer materials taken from JARVIS-DFT database. We obtained very accurate ML models using gradient boosting algorithm. Then we use the trained models to discover exfoliable 2D-layered materials satisfying specific property requirements. Additionally, we integrate our formation energy ML model with a genetic algorithm for structure search to verify if the ML model reproduces the DFT convex hull. This verification establishes a more stringent evaluation metric for the ML model than what commonly used in data sciences. Our learnt model is publicly available on the JARVIS-ML website (https://www.ctcms.nist.gov/jarvisml ) property predictions of generalized materials.

cond-mat.mtrl-sci↗

arXiv2018

Elastic properties of bulk and low-dimensional materials using Van der Waals density functional

In this work, we present a high-throughput first-principles study of elastic properties of bulk and monolayer materials mainly using the vdW-DF-optB88 functional. We discuss the trends on the elastic response with respect to changes in dimensionality. We identify a relation between exfoliation energy and elastic constants for layered materials that can help to guide the search for vdW bonding in materials. We also predicted a few novel materials with auxetic behavior. The uncertainty in structural and elastic properties due to the inclusion of vdW interactions is discussed. We investigated 11,067 bulk and 257 monolayer materials. Lastly, we found that the trends in elastic constants for bulk and their monolayer counterparts can be very different. All the computational results are made publicly available at easy-to-use websites: https://www.ctcms.nist.gov/~knc6/JVASP.html and https://jarvis.nist.gov/ . Our dataset can be used to identify stiff and flexible materials for industrial applications.

cond-mat.mtrl-sci↗

arXiv2018

Computational screening of high-performance optoelectronic materials using OptB88vdW and TB-mBJ formalisms

We perform high-throughput density functional theory (DFT) calculations for optoelectronic properties (electronic bandgap and frequency dependent dielectric function) using the OptB88vdW functional (OPT) and the Tran-Blaha modified Becke Johnson potential (MBJ). This data is distributed publicly through JARVIS-DFT database. We used this data to evaluate the differences between these two formalisms and quantify their accuracy, comparing to experimental data whenever applicable. At present, we have 17,805 OPT and 7,358 MBJ bandgaps and dielectric functions. MBJ is found to predict better bandgaps and dielectric functions than OPT, so it can be used to improve the well-known bandgap problem of DFT in a relatively inexpensive way. The peak positions in dielectric functions obtained with OPT and MBJ are in comparable agreement with experiments. The data is available on our websites http://www.ctcms.nist.gov/~knc6/JVASP.html and https://jarvis.nist.gov .

cond-mat.mtrl-sci↗

arXiv2017

Evolution of Raman spectra in Mo$_{1-x}$W$_x$Te$_2$ alloys

The structural polymorphism in transition metal dichalcogenides (TMDs) provides exciting opportunities for developing advanced electronics. For example, MoTe$_2$ crystallizes in the 2H semiconducting phase at ambient temperature and pressure, but transitions into the 1T$^\prime$ semimetallic phase at high temperatures. Alloying MoTe$_2$ with WTe$_2$ reduces the energy barrier between these two phases, while also allowing access to the T$_d$ Weyl semimetal phase. The MoWTe$_2$ alloy system is therefore promising for developing phase change memory technology. However, achieving this goal necessitates a detailed understanding of the phase composition in the MoTe$_2$-WTe$_2$ system. We combine polarization-resolved Raman spectroscopy with X-ray diffraction (XRD) and scanning transmission electron microscopy (STEM) to study MoWTe$_2$ alloys over the full compositional range x from 0 to 1. We identify Raman and XRD signatures characteristic of the 2H, 1T$^\prime$, and T$_d$ structural phases that agree with density-functional theory (DFT) calculations, and use them to identify phase fields in the MoTe$_2$-WTe$_2$ system, including single-phase 2H, 1T$^\prime$, and T$_d$ regions, as well as a two-phase 1T$^\prime$ + T$_d$ region. Disorder arising from compositional fluctuations in MoWTe$_2$ alloys breaks inversion and translational symmetry, leading to the activation of an infrared 1T$^\prime$-MoTe$_2$ mode and the enhancement of a double-resonance Raman process in 2H-MoWTe$_2$ alloys. Compositional fluctuations limit the phonon correlation length, which we estimate by fitting the observed asymmetric Raman lineshapes with a phonon confinement model. These observations reveal the important role of disorder in MoWTe$_2$ alloys, clarify the structural phase boundaries, and provide a foundation for future explorations of phase transitions and electronic phenomena in this system.

cond-mat.mtrl-sci↗

arXiv2016

MPInterfaces: A Materials Project based Python Tool for High-Throughput Computational Screening of Interfacial Systems

A Materials Project based open-source Python tool, MPInterfaces, has been developed to automate the high-throughput computational screening and study of interfacial systems. The framework encompasses creation and manipulation of interface structures for solid/solid hetero-structures, solid/implicit solvents systems, nanoparticle/ligands systems; and the creation of simple system-agnostic workflows for in depth computational analysis using density-functional theory or empirical energy models. The package leverages existing open-source high-throughput tools and extends their capabilities towards the understanding of interfacial systems. We describe the various algorithms and methods implemented in the package. Using several test cases, we demonstrate how the package enables high-throughput computational screening of advanced materials, directly contributing to the Materials Genome Initiative (MGI), which aims to accelerate the discovery, development, and deployment of new materials.

cond-mat.mtrl-sci↗