arXiv subjects

Matthew K. Horton

Publications and source records attributed to Matthew K. Horton.

14 recordsLinked to original sources

arXiv2024

Realistic non-collinear ground states of solids with source-free exchange correlation functional

In this work, we extend the source-free (SF) exchange correlation (XC) functional developed by Sangeeta Sharma and co-workers to plane-wave density functional theory (DFT) based on the projector augmented wave (PAW) method. This constraint is implemented by the current authors within the VASP source code, using a fast Poisson solver that capitalizes on the parallel three-dimensional fast Fourier transforms (FFTs) implemented in VASP. Using this modified XC functional, we explore the improved convergence behavior that results from applying this constraint to the GGA-PBE+$U$+$J$ functional. In the process, we compare the non-collinear magnetic ground state computed by each functional and their SF counterpart for a select number of magnetic materials in order to provide a metric for comparing with experimentally determined magnetic orderings. We observe significantly improved agreement with experimentally measured magnetic ground state structures after applying the source-free constraint. Furthermore, we explore the importance of considering probability current densities in spin polarized systems, even under no applied field. We analyze the XC torque as well, in order to provide theoretical and computational analyses of the net XC magnetic torque induced by the source-free constraint. Along these lines, we highlight the importance of properly considering the real-space integral of the source-free local magnetic XC field. Our analysis on probability currents, net torque, and constant terms draws additional links to the rich body of previous research on spin-current density functional theory (SCDFT), and paves the way for future extensions and corrections to the SF corrected XC functional.

cond-mat.mtrl-sci↗

arXiv2024

An equivariant graph neural network for the elasticity tensors of all seven crystal systems

The elasticity tensor that describes the elastic response of a material to external forces is among the most fundamental properties of materials. The availability of full elasticity tensors for inorganic crystalline compounds, however, is limited due to experimental and computational challenges. Here, we report the materials tensor (MatTen) model for rapid and accurate estimation of the full fourth-rank elasticity tensors of crystals. Based on equivariant graph neural networks, MatTen satisfies the two essential requirements for elasticity tensors: independence of the frame of reference and preservation of material symmetry. Consequently, it provides a unified treatment of elasticity tensors for all seven crystal systems across diverse chemical spaces, without the need to deal with each separately.. MatTen was trained on a dataset of first-principles elasticity tensors garnered by the Materials Project over the past several years (we are releasing the data herein) and has broad applications in predicting the isotropic elastic properties of polycrystalline materials, examining the anisotropic behavior of single crystals, and discovering new materials with exceptional mechanical properties. Using MatTen, we have discovered a hundred new crystals with extremely large maximum directional Young's modulus and eleven polymorphs of elemental cubic metals with unconventional spatial orientation of Young's modulus.

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arXiv2023

SpinPSO: An agent-based optimization workflow for identifying global noncollinear magnetic ground-states from first-principles

We propose and implement a novel hybrid meta-heuristic optimization algorithm for the identification of non-collinear global ground-states in magnetic systems. The inputs to this optimization scheme are directly from non-collinear density functional theory (DFT), and the workflow is implemented in the atomate code framework, making it suitable to run on high-performance computing architectures. The hybrid algorithm provides a seamless theoretical extension of particle swarm optimization (PSO) algorithms to continuous $\mathcal S^2$ spins, giving it the name SpinPSO. The hybrid nature of the algorithm stems from setting the dynamics of individual spins to be governed by physically motivated atomistic spin dynamics. Using this algorithm, we are able to achieve convergence to experimentally resolved magnetic ground-states for a set of diverse test case materials that exhibit exotic spin textures.

cond-mat.mtrl-sci↗

arXiv2023

Magnetism and magnetocaloric properties of Co$_{1-x}$Mn$_x$Cr$_2$O$_4$

Co$_{1-x}$Mn$_x$Cr$_2$O$_4$ crystallizes as a normal spinel in the cubic $Fd \overline{3}m$ space group, and the end members have been reported to display a region of collinear ferrimagnetism as well as a low-temperature spin-spiral state with variable coherence lengths from 3 nm to 10 nm in polycrystalline samples. Here, we present the synthesis of the entire solid solution, and data showing that the ferrimagnetic ordering temperature as well as the spin-spiral lock-in temperature are tunable with the Co/Mn ratio. The peak magnetocaloric entropy change was determined to be $ΔS_M$ = -5.63 J kg$^{-1}$ K$^{-1}$ in an applied magnetic field change of $ΔH$ = 0 T to 5 T for the Mn end-member at the ferrimagnetic ordering temperature. Using density functional theory (DFT), we explore the shortcomings of the magnetic deformation proxy to identify trends in $ΔS_M$ across composition in this spinel system, and explore future extensions of theory to address these discrepancies.

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arXiv2023

Designing transparent conductors using forbidden optical transitions

Many semiconductors present weak or forbidden transitions at their fundamental band gaps, inducing a widened region of transparency. This occurs in high-performing n-type transparent conductors (TCs) such as Sn-doped In2O3 (ITO), however thus far the presence of forbidden transitions has been neglected in searches for new p-type TCs. To address this, we first compute high-throughput absorption spectra across ~18,000 semiconductors, showing that over half exhibit forbidden or weak optical transitions at their band edges. Next, we demonstrate that compounds with highly localized band edge states are more likely to present forbidden transitions. Lastly, we search this set for p-type and n-type TCs with forbidden or weak transitions. Defect calculations yield unexplored TC candidates such as ambipolar BeSiP2, Zr2SN2 and KSe, p-type BAs, Au2S, and AuCl, and n-type Ba2InGaO5, GaSbO4, and KSbO3, among others. We share our data set via the MPContribs platform, and we recommend that future screenings for optical properties use metrics representative of absorption features rather than band gap alone.

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arXiv2023

A method to computationally screen for tunable properties of crystalline alloys

Conventionally, high-throughput computational materials searches start from an input set of bulk compounds extracted from material databases, and this set is screened for candidate materials for specific applications. In contrast, many functional materials, and especially semiconductors, are heavily engineered alloys or solid solutions of multiple compounds rather than a single bulk compound. To improve our ability to design functional materials, in this work we propose a framework and open-source code to automatically construct possible "alloy pairs" and "alloy systems" and detect "alloy members" from a set of existing, experimental or calculated ordered compounds, without requiring any additional metadata beyond their crystal structure. We provide analysis tools to estimate stability across each alloy. As a demonstration, we apply this framework to all inorganic materials in the Materials Project database to create a new database of over 600,000 unique alloy pair entries that can then be used in materials discovery studies to search for materials with tunable properties. This new database has been incorporated into the Materials Project website and linked with corresponding material identifiers for any user to query and explore. Using an example of screening for p-type transparent conducting materials, we demonstrate how using this methodology reveals candidate material systems that might otherwise have been excluded by a traditional screening. This work lays a foundation from which materials databases can go beyond stoichiometric compounds, and approach a more realistic description of compositionally tunable materials.

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arXiv2022

Vapor-liquid-solid growth of highly-mismatched semiconductor nanowires with high-fidelity van der Waals layer stacking

Nanobelts, nanoribbons and other quasi-one-dimensional nanostructures formed from layered, so-called, van der Waals semiconductors have garnered much attention due to their high-performance, tunable optoelectronic properties. For layered alloys made from the gallium monochalcogenides GaS, GaSe, and GaTe, near-continuous tuning of the energy bandgap across the full composition range has been achieved in GaSe1-xSx and GaSe1-xTex alloys. Gold-catalyzed vapor-liquid-solid (VLS) growth of these alloys yields predominantly nanobelts, nanoribbons and other nanostructures for which the fast crystal growth front consists of layer edges in contact with the catalyst. We demonstrate that in the S-rich, GaS1-xTex system, unlike GaSe1-xSx and GaSe1-xTex, the Au-catalyzed VLS process yields van der Waals nanowires for which the fast growth direction is normal to the layers. The high mismatch between S and Te leads to extraordinary bowing of the GaS1-xTex alloy's energy bandgap, decreasing by at least 0.6 eV for x as small as 0.03. Calculations using density functional theory confirm the significant decrease in bandgap in S-rich GaS1-xTex. The nanowires can exceed fifty micrometers in length, consisting of tens of thousands of van der Waals-bonded layers with triangular or hexagonal cross-sections of uniform dimensions along the length of the nanowire. We propose that the low solubility of Te in GaS results in an enhancement in Te coverage around the Au catalyst-nanowire interface, confining the catalyst to the chalcogen-terminated basal plane (rather than the edges) and thereby enabling layer-by-layer, c-axis growth.

cond-mat.mtrl-sci↗

arXiv2022

High-throughput determination of Hubbard U and Hund J values for transition metal oxides via linear response formalism

DFT+U provides a convenient, cost-effective correction for the self-interaction error (SIE) that arises when describing correlated electronic states using conventional approximate density functional theory (DFT). The success of a DFT+U(+J) calculation hinges on the accurate determination of its Hubbard U and Hund's J parameters, and the linear response (LR) methodology has proven to be computationally effective and accurate for calculating these parameters. This study provides a high-throughput computational analysis of the U and J values for transition metal d-electron states in a representative set of over 2000 magnetic transition metal oxides (TMOs), providing a frame of reference for researchers who use DFT+U to study transition metal oxides. In order to perform this high-throughput study, an atomate workflow is developed for calculating U and J values automatically on massively parallel supercomputing architectures. To demonstrate an application of this workflow, the spin-canting magnetic structure and unit cell parameters of the multiferroic olivine LiNiPO4 are calculated using the computed Hubbard U and Hund J values for Ni-d and O-p states, and are compared with experiment. Both the Ni-d U and J corrections have a strong effect on the Ni-moment canting angle. Additionally, including a O-p U value results in a significantly improved agreement between the computed lattice parameters and experiment.

cond-mat.mtrl-sci↗

arXiv2022

High-throughput optical absorption spectra for inorganic semiconductors

An optical absorption spectrum constitutes one of the most fundamental material characteristics, with relevant applications ranging from material identification to energy harvesting and optoelectronics. However, the database of both experimental and computational spectra is currently lacking. In this study, we designed a computational workflow for the optical absorption spectrum and integrated the simulated spectra into the Materials Project. Using density-functional theory, we computed the frequency-dependent dielectric function and the corresponding absorption coefficient for more than 1000 solid compounds of varying crystal structure and chemistry. The computed spectra show excellent agreement, as quantified by a high value of the Pearson correlation, with experimental results when applying the band gap correction from the HSE functional. The demonstrated calculated accuracy in the spectra suggests that the workflow can be applied in screening studies for materials with specific optical properties.

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arXiv2022

Topological Graph-based Analysis of Solid-State Ion Migration

To accelerate the development of novel ion-conducting materials, we present a general graph-theoretic analysis framework for ion migration in any crystalline structure. The nodes of the graph represent metastable sites of the migrating ion and the edges represent discrete migration events between adjacent sites. Starting from a collection of possible metastable migration sites, the framework assigns a weight to the edges by calculating the individual migration energy barriers between those sites. Connected pathways in the periodic simulation cell corresponding to macroscopic ion migration are identified by searching for the lowest-cost cycle in the periodic migration graph. To exemplify the utility of the framework, we present the automatic analyses of Li migration in different polymorphs of VO(PO4), with the resulting identification of two distinct crystal structures with simple migration pathways demonstrating overall < 300 meV migration barriers.

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arXiv2022

The role of disorder in the synthesis of metastable zinc zirconium nitrides

In materials science, it is often assumed that ground state crystal structures predicted by density functional theory are the easiest polymorphs to synthesize. Ternary nitride materials, with many possible metastable polymorphs, provide a rich materials space to study what influences thermodynamic stability and polymorph synthesizability. For example, ZnZrN2 is theoretically predicted at zero Kelvin to have an unusual layered "wurtsalt" ground state crystal structure with compelling optoelectronic properties, but it is unknown whether this structure can be realized experimentally under practical synthesis conditions. Here, we use combinatorial sputtering to synthesize hundreds of ZnxZr1-xNy thin film samples, and find metastable rocksalt-derived or boron-nitride-derived structures rather than the predicted wurtsalt structure. Using a statistical polymorph sampler approach, it is demonstrated that although rocksalt is the least stable polymorph at zero Kelvin, it becomes the most stable polymorph at high effective temperatures similar to those achieved using this sputter deposition method, and thus corroborates experimental results. Additional calculations show that this destabilization of the wurtsalt polymorph is due to configurational entropic and enthalpic effects, and that vibrational contributions are negligible. Specifically, rocksalt- and boron-nitride-derived structures become the most stable polymorphs in the presence of disorder because of higher tolerances to cation cross-substitution and off-stoichiometry than the wurtsalt structure. This understanding of the role of disorder tolerance in the synthesis of competing polymorphs can enable more accurate predictions of synthesizable crystal structures and their achievable material properties.

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

A representation-independent electronic charge density database for crystalline materials

In addition to being the core quantity in density functional theory, the charge density can be used in many tertiary analyses in materials sciences from bonding to assigning charge to specific atoms. The charge density is data-rich since it contains information about all the electrons in the system. With increasing utilization of machine-learning tools in materials sciences, a data-rich object like the charge density can be utilized in a wide range of applications. The database presented here provides a modern and user-friendly interface for a large and continuously updated collection of charge densities as part of the Materials Project. In addition to the charge density data, we provide the theory and code for changing the representation of the charge density which should enable more advanced machine-learning studies for the broader community.

cond-mat.mtrl-sci↗

arXiv2020

High-throughput search for magnetic and topological order in transition metal oxides

The discovery of intrinsic magnetic topological order in $\rm MnBi_2Te_4$ has invigorated the search for materials with coexisting magnetic and topological phases. These multi-order quantum materials are expected to exhibit new topological phases that can be tuned with magnetic fields, but the search for such materials is stymied by difficulties in predicting magnetic structure and stability. Here, we compute over 27,000 unique magnetic orderings for over 3,000 transition metal oxides in the Materials Project database to determine their magnetic ground states and estimate their effective exchange parameters and critical temperatures. We perform a high-throughput band topology analysis of centrosymmetric magnetic materials, calculate topological invariants, and identify 18 new candidate ferromagnetic topological semimetals, axion insulators, and antiferromagnetic topological insulators. To accelerate future efforts, machine learning classifiers are trained to predict both magnetic ground states and magnetic topological order without requiring first-principles calculations.

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