arXiv subjects

Kristin A. Persson

Publications and source records attributed to Kristin A. Persson.

At least 55 records · Page 3Linked to original sources

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.

cond-mat.mtrl-sci↗

arXiv2023

Assessing Thermodynamic Selectivity of Solid-State Reactions for the Predictive Synthesis of Inorganic Materials

Synthesis is a major challenge in the discovery of new inorganic materials. Currently, there is limited theoretical guidance for identifying optimal solid-state synthesis procedures. We introduce two selectivity metrics, primary and secondary competition, to assess the favorability of target/impurity phase formation in solid-state reactions. We used these metrics to analyze 3,520 solid-state reactions in the literature, ranking existing approaches to popular target materials. Additionally, we implemented these metrics in a data-driven synthesis planning workflow and demonstrated its application in the synthesis of barium titanate (BaTiO$_3$). Using an 18-element chemical reaction network with first-principles thermodynamic data from the Materials Project, we identified 82,985 possible BaTiO$_3$ synthesis reactions and selected nine for experimental testing. Characterization of reaction pathways via synchrotron powder X-ray diffraction reveals that our selectivity metrics correlate with observed target/impurity formation. We discovered two efficient reactions using unconventional precursors (BaS/BaCl$_2$ and Na$_2$TiO$_3$) that produce BaTiO$_3$ faster and with fewer impurities than conventional methods, highlighting the importance of considering complex chemistries with additional elements during precursor selection. Our framework provides a foundation for predictive inorganic synthesis, facilitating the optimization of existing recipes and the discovery of new materials, including those not easily attainable with conventional precursors.

cond-mat.mtrl-sci↗

arXiv2023

Extracting Structured Seed-Mediated Gold Nanorod Growth Procedures from Literature with GPT-3

Although gold nanorods have been the subject of much research, the pathways for controlling their shape and thereby their optical properties remain largely heuristically understood. Although it is apparent that the simultaneous presence of and interaction between various reagents during synthesis control these properties, computational and experimental approaches for exploring the synthesis space can be either intractable or too time-consuming in practice. This motivates an alternative approach leveraging the wealth of synthesis information already embedded in the body of scientific literature by developing tools to extract relevant structured data in an automated, high-throughput manner. To that end, we present an approach using the powerful GPT-3 language model to extract structured multi-step seed-mediated growth procedures and outcomes for gold nanorods from unstructured scientific text. GPT-3 prompt completions are fine-tuned to predict synthesis templates in the form of JSON documents from unstructured text input with an overall accuracy of $86\%$. The performance is notable, considering the model is performing simultaneous entity recognition and relation extraction. We present a dataset of 11,644 entities extracted from 1,137 papers, resulting in 268 papers with at least one complete seed-mediated gold nanorod growth procedure and outcome for a total of 332 complete procedures.

physics.app-ph↗

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.

cond-mat.mtrl-sci↗

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.

cond-mat.mtrl-sci↗

arXiv2023

Crystal Toolkit: A Web App Framework to Improve Usability and Accessibility of Materials Science Research Algorithms

Crystal Toolkit is an open source tool for viewing, analyzing and transforming crystal structures, molecules and other common forms of materials science data in an interactive way. It is intended to help beginners rapidly develop web-based apps to explore their own data or to help developers make their research algorithms accessible to a broader audience of scientists who might not have any training in computer programming and who would benefit from graphical interfaces. Crystal Toolkit comes with a library of ready-made components that can be assembled to make complex web apps: simulation of powder and single crystalline diffraction patterns, convex hull phase diagrams, Pourbaix diagrams, electronic band structures, analysis of local chemical environments and symmetry, and more. Crystal Toolkit is now powering the Materials Project website frontend, providing user-friendly access to its database of computed materials properties. In the future, it is hoped that new visualizations might be prototyped using Crystal Toolkit to help explore new forms of data being generated by the materials science community, and that this in turn can help new materials scientists develop intuition for how their data behaves and the insights that might be found within. Crystal Toolkit will remain a work-in-progress and is open to contributions from the community.

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.

cond-mat.mtrl-sci↗

arXiv2022

Inferring global dynamics from local structure in liquid electrolytes

Ion transport in concentrated electrolytes plays a fundamental role in electrochemical systems such as lithium ion batteries. Nonetheless, the mechanism of transport amid strong ion-ion interactions remains enigmatic. A key question is whether the dynamics of ion transport can be predicted by the local static structure alone, and if so what are the key structural motifs that determine transport. In this paper, we show that machine learning can successfully decompose global conductivity into the spatio-temporal average of local, instantaneous ionic contributions, and relate this ``local molar conductivity" field to the local ionic environment. Our machine learning model accurately predicts the molar conductivity of electrolyte systems that were not part of the training set, suggesting that the dynamics of ion transport is predictable from local static structure. Further, through analysing this machine-learned local conductivity field, we observe that fluctuations in local conductivity at high concentration are negatively correlated with total molar conductivity. Surprisingly, these fluctuations arise due to a long tail distribution of low conductivity ions, rather than distinct ion pairs, and are spatially correlated through both like- and unlike-charge interactions. More broadly, our approach shows how machine learning can aid the understanding of complex soft matter systems, by learning a function that attributes global collective properties to local, atomistic contributions.

cond-mat.soft↗

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.

cond-mat.mtrl-sci↗

arXiv2022

Expanding the Materials Search Space for Multivalent Cathodes

Multivalent batteries are an energy storage technology with the potential to surpass lithium-ion batteries, however their performance has been limited by the low voltages and poor solid-state ionic mobility of available cathodes. A computational screening approach to identify high-performance multivalent intercalation cathodes among materials that do not contain the working ion of interest has been developed which greatly expands the search space that can be considered for materials discovery. This approach has been applied to magnesium cathodes as a proof of concept and four resulting candidate materials (NASICON V$_2$(PO$_4$)$_3$, birnessite NaMn$_4$O$_8$, tavorite MnPO$_4$F, and spinel MnO$_2$) are discussed in further detail. In examining the ion migration environment and associated Mg$^{2+}$ migration energy in these materials, local energy maxima are found to correspond with pathway positions where Mg$^{2+}$ passes through a plane of anion atoms. While previous works have established the influence of local coordination on multivalent ion mobility, these results suggest that considering both the type of local bonding environment as well as available free volume for the mobile ion along its migration pathway can be significant for improving solid-state mobility.

cond-mat.mtrl-sci↗

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

Microscopic Theory of Magnetic Disorder-Induced Decoherence in Superconducting Nb Films

The performance of superconducting qubits is orders of magnitude below what is expected from theoretical estimates based on the loss tangents of the constituent bulk materials. This has been attributed to the presence of uncontrolled surface oxides formed during fabrication which can introduce defects and impurities that create decoherence channels. Here, we develop an ab initio Shiba theory to investigate the microscopic origin of magnetic-induced decoherence in niobium thin film superconductors and the formation of native oxides. Our ab initio calculations encompass the roles of structural disorder, stoichiometry, and strain on the formation of decoherence-inducing local spin moments. With parameters derived from these first-principles calculations we develop an effective quasi-classical model of magnetic-induced losses in the superconductor. We identify d-channel losses (associated with oxygen vacancies) as especially parasitic, resulting in a residual zero temperature surface impedance. This work provides a route to connecting atomic scale properties of superconducting materials and macroscopic decoherence channels affecting quantum systems.

cond-mat.supr-con↗

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

Selectivity in yttrium manganese oxide synthesis via local chemical potentials in hyperdimensional phase space

In sharp contrast to molecular synthesis, materials synthesis is generally presumed to lack selectivity. The few known methods of designing selectivity in solid-state reactions have limited scope, such as topotactic reactions or strain stabilization. This contribution describes a general approach for searching large chemical spaces to identify selective reactions. This novel approach explains the ability of a nominally "innocent" Na$_2$CO$_3$ precursor to enable the metathesis synthesis of single-phase Y$_2$Mn$_2$O$_7$ -- an outcome that was previously only accomplished at extreme pressures and which cannot be achieved with closely related precursors of Li$_2$CO$_3$ and K$_2$CO$_3$. By calculating the required change in chemical potential across all possible reactant-product interfaces in an expanded chemical space including Y, Mn, O, alkali metals, and halogens, using thermodynamic parameters obtained from density functional theory calculations, we identify reactions that minimize the thermodynamic competition from intermediates. In this manner, only the Na-based intermediates minimize the distance in the hyperdimensional chemical potential space to Y$_2$Mn$_2$O$_7$, thus providing selective access to a phase which was previously thought to be metastable. Experimental evidence validating this mechanism for pathway-dependent selectivity is provided by intermediates identified from in situ synchrotron-based crystallographic analysis. This approach of calculating chemical potential distances in hyperdimensional compositional spaces provides a general method for designing selective solid-state syntheses that will be useful for gaining access to metastable phases and for identifying reaction pathways that can reduce the synthesis temperature, and cost, of technological materials.

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

Efficient calculation of carrier scattering rates from first principles

The electronic transport behaviour of materials determines their suitability for technological applications. We develop an efficient method for calculating carrier scattering rates of solid-state semiconductors and insulators from first principles inputs. The present method extends existing polar and non-polar electron-phonon coupling, ionized impurity, and piezoelectric scattering mechanisms formulated for isotropic band structures to support highly anisotropic materials. We test the formalism by calculating the electronic transport properties of 16 semiconductors and comparing the results against experimental measurements. The present work is amenable for use in high-throughput computational workflows and enables accurate screening of carrier mobilities, lifetimes, and thermoelectric power.

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.

cond-mat.mtrl-sci↗