arXiv subjects

Kevin F. Garrity

Publications and source records attributed to Kevin F. Garrity.

At least 19 recordsLinked to original sources

arXiv2026

Weyl Magnons in the Non-Coplanar Antiferromagnet MnTe$_2$

Using a combination of band representation analysis, inelastic neutron scattering (INS), magneto-Raman spectroscopy measurements, and linear spin wave theory, we establish that the non-coplanar antiferromagnet MnTe$_2$ is a tunable Weyl magnon material, hosting symmetry-protected topological nodal lines in its magnon band structure, protected by the the non-coplanar nature of the antiferromagnetic ordering, that transition into Weyl magnons upon the application of symmetry-breaking perturbations using an external magnetic field. By constructing a spin model that reproduces the observed INS magnon spectra and field-dependence of the Raman $Γ$-magnons, we directly probe the topological magnon nodal lines and observe their associated signature of non-trivial topology through the pseudo-spin winding of the scattering intensity in angular scans near the nodal lines. Finally, we discuss how to induce Weyl magnons in the spectrum through an external magnetic field, shedding light on future in-field INS and thermal Hall experiments. This work establishes a clear magnonic analog to Weyl electrons, enabling further exploration of topological behavior in bosonic systems and highlighting the interplay between magnetic order and band topology in non-coplanar antiferromagnets.

cond-mat.mtrl-sci↗

arXiv2024

JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods

Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with both perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC) and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data-points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard

cond-mat.mtrl-sci↗

arXiv2023

Recent progress in the JARVIS infrastructure for next-generation data-driven materials design

The Joint Automated Repository for Various Integrated Simulations (JARVIS) infrastructure at the National Institute of Standards and Technology (NIST) is a large-scale collection of curated datasets and tools with more than 80000 materials and millions of properties. JARVIS uses a combination of electronic structure, artificial intelligence (AI), advanced computation and experimental methods to accelerate materials design. Here we report some of the new features that were recently included in the infrastructure such as: 1) doubling the number of materials in the database since its first release, 2) including more accurate electronic structure methods such as Quantum Monte Carlo, 3) including graph neural network-based materials design, 4) development of unified force-field, 5) development of a universal tight-binding model, 6) addition of computer-vision tools for advanced microscopy applications, 7) development of a natural language processing tool for text-generation and analysis, 8) debuting a large-scale benchmarking endeavor, 9) including quantum computing algorithms for solids, 10) integrating several experimental datasets and 11) staging several community engagement and outreach events. New classes of materials, properties, and workflows added to the database include superconductors, two-dimensional (2D) magnets, magnetic topological materials, metal-organic frameworks, defects, and interface systems. The rich and reliable datasets, tools, documentation, and tutorials make JARVIS a unique platform for modern materials design. JARVIS ensures openness of data and tools to enhance reproducibility and transparency and to promote a healthy and collaborative scientific environment.

cond-mat.mtrl-sci↗

arXiv2023

Fast and Accurate Prediction of Material Properties with Three-Body Tight-Binding Model for the Periodic Table

Parameterized tight-binding models fit to first principles calculations can provide an efficient and accurate quantum mechanical method for predicting properties of molecules and solids. However, well-tested parameter sets are generally only available for a limited number of atom combinations, making routine use of this method difficult. Furthermore, most previous models consider only simple two-body interactions, which limits accuracy. To tackle these challenges, we develop a density functional theory database of nearly one million materials, which we use to fit a universal set of tight-binding parameters for 65 elements and their binary combinations. We include both two-body and three-body effective interaction terms in our model, plus self-consistent charge transfer, enabling our model to work for metallic, covalent, and ionic bonds with the same parameter set. To ensure predictive power, we adopt a learning framework where we repeatedly test the model on new low energy crystal structures and then add them to the fitting dataset, iterating until predictions improve. We distribute the materials database and tools developed in this work publicly.

cond-mat.mtrl-sci↗

arXiv2023

High-Throughput DFT-Based Discovery of Next Generation Two-Dimensional (2D) Superconductors

High-throughput density functional theory (DFT) calculations allow for a systematic search for conventional superconductors. With the recent interest in two-dimensional (2D) superconductors, we used a high-throughput workflow to screen over 1,000 2D materials in the JARVIS-DFT database and performed electron-phonon coupling calculations, using the McMillan-Allen-Dynes formula to calculate the superconducting transition temperature ($T_c$) for 165 of them. Of these 165 materials, we identify 34 dynamically stable structures with transition temperatures above 5 K, including materials such as W$_2$N$_3$, NbO$_2$, ZrBrO, TiClO, NaSn$_2$S$_4$, Mg$_2$B$_4$C$_2$ and the previously unreported Mg$_2$B$_4$N$_2$ ($T_c$ = 21.8 K). Finally, we performed experiments to determine the $T_c$ of selected layered superconductors (2H-NbSe$_2$, 2H-NbS$_2$, ZrSiS, FeSe) and discuss the measured results within the context of our DFT results. We aim that the outcome of this workflow can guide future computational and experimental studies of new and emerging 2D superconductors by providing a roadmap of high-throughput DFT data.

cond-mat.supr-con↗

arXiv2022

On the Role of Defects in the Electronic Structure of MnBi$_{2-x}$Sb$_x$Te$_4$

Elemental substitution is a proven method of Fermi level tuning in topological insulators, which is needed for device applications. Through static and time resolved photoemission, we show that in MnBi$_2$Te$_4$, elemental substitution of Bi with Sb indeed tunes the Fermi level towards the bulk band gap, making the material charge neutral at 35\% Sb concentration. For the first time, we are able to directly probe the excited state band structure at this doping level, and their dynamics, which show that the decay channels at the Fermi level are severely restricted. However, elemental substitution widens the surface state gap, which we attribute to the increase in antisite defects resulting from Sb substitution. This hypothesis is supported by DFT calculations that include defects, which show a sensitivity of the topological surface state to their inclusion. Our results emphasize the need for defect control if MnBi$_{2-x}$Sb$_x$Te$_4$ is to be used for device applications.

cond-mat.mtrl-sci↗

arXiv2022

Topological Phases of Mn$A_{2}$$X_{4}$ (A=Bi, Sb; X = Se, Te) under Magnetic Field

The concept of electronic topology and the associated topological protection brings excellent opportunities for developing next-generation devices. Ideally, magnetic topological materials (MTM) should have their Dirac/Weyl points and/or associated mass gaps at the Fermi energy (EF) or be readily tunable such that they can be placed at EF via external perturbations such as electric field gating, chemical substitutions, or doping. Three-dimensional antiferromagnetic (AFM) materials like Mn$Bi_{2}$$X_{4}$ (X=Se, Te) that have strong spin-orbit coupling (SOC) and broken time-reversal symmetry (TRS) due to magnetic ordering have been the subject of enormous interest.. In this work, using density functional theory (DFT), we have studied the electronic properties and topological phases of the first intrinsic magnetic topological insulator family Mn$Bi_{2}$$Te_{4}$ (MBT) in the presence of an external magnetic field. Our calculations reveal that the topological phase of bulk rhombohedral (R$\overline{3}$m) Mn$A_{2}$$X_{4}$ (A = Sb, Bi; X=Se, Te) depends on the spin direction and the chemistry. The antiferromagnetic (AFM) ground state of Mn$Sb_{2}$$Se_{4}$ (MSS) is a trivial insulator, whereas the AFM ground state of Mn$Bi_{2}$$Se_{4}$(MBS) is an Axion insulator. Both materials become nodal point or nodal line Weyl semimetals in the presence of a sufficiently strong external magnetic field. The AFM ground state of Mn$Sb_{2}$$Te_{4}$ (MST) is an Axion insulator. MST is a type-II Weyl semimetal with spins aligned in the $\hat{Z}$-direction, but becomes insulating with an inverted band gap for spins in-plane. Similarly, the AFM phase of MBT is an Axion insulator, but remains insulating with an inverted gap in the ferromagnetic phase. Additionally, we demonstrated the evolution of the topological phase of Mn$Bi_{2}$$Te_{4}$ (MBT) by substituting the Bi atoms with the Sb atoms.

cond-mat.mtrl-sci↗

arXiv2021

The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design

The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-Tools. To date, JARVIS consists of 40,000 materials and 1 million calculated properties in JARVIS-DFT, 1,500 materials and 110 force-fields in JARVIS-FF, and 25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-Tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov .

cond-mat.mtrl-sci↗

arXiv2021

Topological surface states of MnBi$_2$Te$_4$ at finite temperatures and at domain walls

MnBi$_2$Te$_4$ has recently been the subject of intensive study, due to the prediction of axion insulator, Weyl semimetal, and quantum anomalous Hall insulator phases, depending on the structure and magnetic ordering. Experimental results have confirmed some aspects of this picture, but several experiments have seen zero-gap surfaces states at low temperature, in conflict with expectations. In this work, we develop a first-principles-based tight-binding model that allows for arbitrary control of the local spin direction and spin-orbit coupling, enabling us to accurately treat large unit-cells. Using this model, we examine the behavior of the topological surface state as a function of temperature, finding a gap closure only above the Néel temperature. In addition, we examine the effect of magnetic domains on the electronic structure, and we find that the domain wall zero-gap states extend over many unit-cells. These domain wall states can appear similar to the high temperature topological surface state when many domain sizes are averaged, potentially reconciling theoretical results with experiments.

cond-mat.mtrl-sci↗

arXiv2021

High-throughput search for magnetic topological materials using spin-orbit spillage, machine-learning and experiments

Magnetic topological insulators and semi-metals have a variety of properties that make them attractive for applications including spintronics and quantum computation, but very few high-quality candidate materials are known. In this work, we use systematic high-throughput density functional theory calculations to identify magnetic topological materials from 40000 three-dimensional materials in the JARVIS-DFT database (https://jarvis.nist.gov/jarvisdft). First, we screen materials with net magnetic moment > 0.5 μB and spin-orbit spillage > 0.25, resulting in 25 insulating and 564 metallic candidates. The spillage acts as a signature of spin-orbit induced band-inversion. Then, we carry out calculations of Wannier charge centers, Chern numbers, anomalous Hall conductivities, surface bandstructures, and Fermi-surfaces to determine interesting topological characteristics of the screened compounds. We also train machine learning models for predicting the spillage, bandgaps, and magnetic moments of new compounds, to further accelerate the screening process. We experimentally synthesize and characterize a few candidate materials to support our theoretical predictions.

cond-mat.mtrl-sci↗

arXiv2020

Database of Wannier Tight-binding Hamiltonians using High-throughput Density Functional Theory

We develop a computational workflow for high-throughput Wannierization of density functional theory (DFT) based electronic band structure calculations. We apply this workflow to 1771 materials, and we create a database with the resulting Wannier-function based tight binding Hamiltonians (WTBH). We evaluate the accuracy of the WTBHs by comparing the Wannier band structures to directly calculated DFT band structures on both the set of k-points used in the Wannierization as well as independent k-points from high symmetry lines. Accurate WTBH can be used for the calculation of many materials properties, and we include a few example applications. We also develop a web-app that can be used to predict electronic properties on-the-fly using WTBH from our database. The tools to generate the Hamiltonian and the database of the WTB parameters will be made publicly available through the websites https://github.com/usnistgov/jarvis and https://www.ctcms.nist.gov/jarviswtb.

cond-mat.mtrl-sci↗

arXiv2020

Distinct magneto-Raman signatures of spin-flip phase transitions in CrI3

The discovery of 2-dimensional (2D) materials, such as CrI3, that retain magnetic ordering at monolayer thickness has resulted in a surge of research in 2D magnetism from both pure and applied perspectives. Here, we report a magneto-Raman spectroscopy study on multilayered CrI3, focusing on two new features in the spectra which appear at temperatures below the magnetic ordering temperature and were previously assigned to high frequency magnons. We observe a striking evolution of the Raman spectra with increasing magnetic field in which clear, sudden changes in intensities of the modes are attributed to the interlayer ordering changing from antiferromagnetic to ferromagnetic at a critical magnetic field. Our work highlights the sensitivity of the Raman modes to weak interlayer spin ordering in CrI3. In addition, we theoretically examine potential origins for the new modes, which we deduce are unlikely single magnons.

cond-mat.mes-hall↗

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↗

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

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↗

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

Combined cluster and atomic displacement expansion for solid solutions and magnetism

Finite temperature disordered solid solutions and magnetic materials are difficult to study directly using first principles calculations, due to the large unit cells and many independent samples that are required. In this work, we develop a combined cluster expansion and atomic displacement expansion, which we fit to first principles energies, forces, and stresses. We then use the expansion to calculate thermodynamic quantities at nearly first principles levels of accuracy. We demonstrate that by treating all the relevant degrees of freedom explicitly, we can achieve improved convergence properties as compared to a simple cluster expansion, and our model naturally includes both configurational and vibrational entropy. In addition, we can treat coupling between structural and chemical or magnetic degrees of freedom. As examples, we use our expansion to calculate properties of Si$_{1-x}$Ge$_x$, magnetic MnO, Al with vacancies, and Ba$_x$Sr$_{1-x}$TiO$_3$.

cond-mat.mtrl-sci↗