arXiv subjects

Chris Wolverton

Publications and source records attributed to Chris Wolverton.

At least 37 records · Page 2Linked to original sources

arXiv2023

A Unified Understanding of Minimum Lattice Thermal Conductivity

We propose a first-principles model of minimum lattice thermal conductivity ($κ_{\rm L}^{\rm min}$) based on a unified theoretical treatment of thermal transport in crystals and glasses. We apply this model to thousands of inorganic compounds and discover a universal behavior of $κ_{\rm L}^{\rm min}$ in crystals in the high-temperature limit: the isotropically averaged $κ_{\rm L}^{\rm min}$ is independent of structural complexity and bounded within a range from $\sim$0.1 to $\sim$2.6 W/[m$\cdot$K], in striking contrast to the conventional phonon gas model which predicts no lower bound. We unveil the underlying physics by showing that for a given parent compound $κ_{\rm L}^{\rm min}$ is bounded from below by a value that is approximately insensitive to disorder, but the relative importance of different heat transport channels (phonon gas versus diffuson) depends strongly on the degree of disorder. Moreover, we propose that the diffuson-dominated $κ_{\rm L}^{\rm min}$ in complex and disordered compounds might be effectively approximated by the phonon gas model for an ordered compound by averaging out disorder and applying phonon unfolding. With these insights, we further bridge the knowledge gap between our model and the well-known Cahill-Watson-Pohl (CWP) model, rationalizing the successes and limitations of the CWP model in the absence of heat transfer mediated by diffusons. Finally, we construct graph network and random forest machine learning models to extend our predictions to all compounds within the Inorganic Crystal Structure Database (ICSD), which were validated against thermoelectric materials possessing experimentally measured ultralow $κ_{\rm L}$. Our work offers a unified understanding of $κ_{\rm L}^{\rm min}$, which can guide the rational engineering of materials to achieve $κ_{\rm L}^{\rm min}$.

cond-mat.mtrl-sci↗

arXiv2023

Accelerated Screening of Ternary Chalcogenides for High-Performance Optoelectronic Materials

Chalcogenides, which refer to chalcogen anions, have attracted considerable attention in multiple fields of applications, such as optoelectronics, thermoelectrics, transparent contacts, and thin film transistors. In comparison to oxide counterparts, chalcogenides have demonstrated higher mobility and \textit{p}-type dopability, owing to larger orbital overlaps between metal-X covalent chemical bondings and higher-energy valence bands derived by p-orbitals. Despite the potential of chalcogenides, the number of successfully synthesized compounds remains relatively low compared to oxides, suggesting the presence of numerous unexplored chalcogenides with fascinating physical characteristics. In this study, we implemented a systematic high-throughput screening process combined with first-principles calculations on ternary chalcogenides using 34 crystal structure prototypes. We generated a computational material database containing over 400,000 compounds by exploiting the ion-substitution approach at different atomic sites with elements in the periodic table. The thermodynamic stabilities of the candidates were validated using the chalcogenides included in the Open Quantum Materials Database. Moreover, we trained a model based on Crystal Graph Convolutional Neural Networks to predict the thermodynamic stability of novel materials. Furthermore, we theoretically evaluated the electronic structures of the stable candidates using accurate hybrid functionals. A series of in-depth characteristics, including the carrier effective masses, electronic configuration, and photovoltaic conversion efficiency, was also investigated. Our work provides useful guidance for further experimental research in the synthesis and characterization of such chalcogenides as promising candidates, as well as charting the stability and optoelectronic performance of ternary chalcogenides.

cond-mat.mtrl-sci↗

arXiv2022

The Phase Stability Network of all Inorganic Materials

One of the holy grails of materials science, unlocking structure-property relationships, has largely been pursued via bottom-up investigations of how the arrangement of atoms and interatomic bonding in a material determine its macroscopic behavior. Here we consider a complementary approach, a top-down study of the organizational structure of networks of materials, based on the interaction between materials themselves. We unravel the complete "phase stability network of all inorganic materials" as a densely-connected complex network of 21,000 thermodynamically stable compounds (nodes) interlinked by 41 million tie-lines (edges) defining their two-phase equilibria, as computed by high-throughput density functional theory. We find that the node connectivity in the materials network has a lognormal distribution, and the connectivity decreases with the number of elemental constituents in a material. Analyzing the topology of this network of materials has the potential to uncover new knowledge inaccessible from traditional atoms-to-materials paradigms. Using the connectivity of nodes in the phase stability network, we derive a rational, data-driven metric for material reactivity, the "nobility index", and quantitatively identify the noblest materials in nature.

cond-mat.mtrl-sci↗

arXiv2022

Testing the r$^2$SCAN density functional for the thermodynamic stability of solids with and without a van der Waals correction

A central aim of materials discovery is an accurate and numerically reliable description of thermodynamic properties, such as the enthalpies of formation and decomposition. The r$^2$SCAN revision of the strongly constrained and appropriately normed (SCAN) meta-generalized gradient approximation (meta-GGA) balances numerical stability with high general accuracy. To assess the r$^2$SCAN description of solid-state thermodynamics, we evaluate the formation and decomposition enthalpies, equilibrium volumes, and fundamental bandgaps of more than 1,000 solids using r$^2$SCAN, SCAN, and PBE, as well as two dispersion-corrected variants, SCAN+rVV10 and r$^2$SCAN+rVV10. We show that r$^2$SCAN achieves accuracy comparable to SCAN and often improves upon SCAN's already excellent accuracy. Whereas SCAN+rVV10 is often observed to worsen the formation enthalpies of SCAN, and makes no substantial correction to SCAN's cell volume predictions, r$^2$SCAN+rVV10 predicts marginally less-accurate formation enthalpies than r$^2$SCAN, and slightly more-accurate cell volumes than r$^2$SCAN. The average absolute errors in predicted formation enthalpies are found to decrease by a factor of 1.5 to 2.5 from the GGA level to the meta-GGA level. Smaller decreases in error are observed for decomposition enthalpies. For formation enthalpies r$^2$SCAN improves over SCAN for intermetallic systems. For a few classes of systems -- transition metals, intermetallics, weakly-bound solids, and enthalpies of decomposition into compounds -- GGAs are comparable to meta-GGAs. In total, r$^2$SCAN and r$^2$SCAN+rVV10 can be recommended as stable, general-purpose meta-GGAs for materials discovery.

cond-mat.mtrl-sci↗

arXiv2022

Assessing the accuracy of compound formation energies with quantum Monte Carlo

Accurately predicting the formation energy of a compound, which describes its thermodynamic stability, is a key challenge in materials physics. Here, we employ many-body quantum Monte Carlo (QMC) with single-reference trial functions to compute the formation energy of two electronically disparate compounds, the intermetallic VPt$_2$ and the semiconductor CuI, for which standard density functional theory (DFT) predictions using both the Perdew-Burke Ernzerhof (PBE) and the strongly constrained and appropriately normed (SCAN) density functional approximations deviate markedly from available experimental values. For VPt$_2$, we find an agreement between QMC, SCAN, and PBE0 estimates, which therefore remain in disagreement with the much less exothermic experimental value. For CuI, the QMC result agrees with neither SCAN nor PBE pointing towards DFT exchange-correlation biases, likely related to the localized Cu $3d$ electrons. Compared to the behavior of some density functional approximations within DFT, spin-averaged QMC exhibits a smaller but still appreciable deviation when compared to experiment. The QMC result is slightly improved by incorporating spin-orbit corrections for CuI and solid I$_2$, so that experiment and theory are brought into imperfect but reasonable agreement within about 120~meV/atom.

cond-mat.mtrl-sci↗

arXiv2022

Identification of High-Dielectric Constant Compounds from Statistical Design

The discovery of high-dielectric materials is crucial to increasing the efficiency of electronic devices and batteries. Here, we report three previously unexplored materials with very high dielectric constants (69 $<$ $ε$ $<$ 101) and large band gaps (2.9$<$ $E_{\text{g}}$(eV) $<$ 5.5) obtained by screening materials databases using statistical optimization algorithms aided by artificial neural networks (ANN). Two of these new dielectrics are mixed-anion compounds (Eu$_5$SiCl$_6$O$_4$ and HoClO), and are shown to be thermodynamically stable against common semiconductors via phase-diagram analysis. We also uncovered four other materials with relatively large dielectric constants (20$<$$ε$$<$40) and band gaps (2.3$<$$E_{\text{g}}$(eV)$<$2.7). While the ANN training data is obtained from Materials Project, the search-space consists of materials from Open Quantum Materials Database (OQMD) - demonstrating a successful implementation of cross-database materials design. Overall, we report dielectric properties of 17 materials calculated using ab-initio calculations, that were selected in our design workflow. The dielectric materials with high dielectric properties predicted in this work open up further experimental research opportunities.

cond-mat.mtrl-sci↗

arXiv2021

Recent Advances and Applications of Deep Learning Methods in Materials Science

Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. Recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep-learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science. The application of DL methods in materials science presents an exciting avenue for future materials discovery and design.

cond-mat.mtrl-sci↗

arXiv2021

Scale-invariant Machine-learning Model Accelerates the Discovery of Quaternary Chalcogenides with Ultralow Lattice Thermal Conductivity

Intrinsically low lattice thermal conductivity ($κ_l$) is a desired requirement in many crystalline solids such as thermal barrier coatings and thermoelectrics. Here, we design an advanced machine-learning (ML) model based on crystal graph convolutional neural network that is insensitive to volumes (i.e., scale) of the input crystal structures to discover novel quaternary chalcogenides, AMM'Q$_3$ (A/M/M'=alkali, alkaline-earth, post-transition metals, lanthanides, Q=chalcogens). Upon screening the thermodynamic stability of $\sim$ 1 million compounds using the ML model iteratively and performing density functional theory (DFT) calculations for a small fraction of compounds, we discover 99 compounds that are validated to be stable in DFT. Taking several DFT-stable compounds, we calculate their $κ_l$ using phonon-Boltzmann transport equation, which reveals ultralow-$κ_l$ ($<$ 2 Wm$^{-1}$K$^{-1}$ at room-temperature) due to their soft elasticity and strong phonon anharmonicity. Our work demonstrates the high-efficiency of scale-invariant ML model in predicting novel compounds and presents experimental research opportunities with these new compounds.

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

Exploring low lattice thermal conductivity materials using chemical bonding principles

Semiconductors with very low lattice thermal conductivities are highly desired for applications relevant to thermal energy conversion and management, such as thermoelectrics and thermal barrier coatings. Although the crystal structure and chemical bonding are known to play vital roles in shaping heat transfer behavior, material design approaches of lowering lattice thermal conductivity using chemical bonding principles are uncommon. In this work, we present an effective strategy of weakening interatomic interactions and therefore suppressing lattice thermal conductivity based on chemical bonding principles and develop a high-efficiency approach of discovering low $κ_{\rm L}$ materials by screening the local coordination environments of crystalline compounds. The followed first-principles calculations uncover 30 hitherto unexplored compounds with (ultra)low lattice thermal conductivities from thirteen prototype crystal structures contained in the inorganic crystal structure database. Furthermore, we demonstrate an approach of rationally designing high-performance thermoelectrics by additionally incorporating cations with stereochemically active lone-pair electrons. Our results not only provide fundamental insights into the physical origin of the low lattice thermal conductivity in a large family of copper-based compounds but also offer an efficient approach to discovery and design materials with targeted thermal transport properties.

cond-mat.mtrl-sci↗

arXiv2020

Accelerated Discovery of a Large Family of Quaternary Chalcogenides with very Low Lattice Thermal Conductivity

The development of efficient thermal energy management devices such as thermoelectrics, barrier coatings, and thermal data-storage disks often relies on compounds that possess very low lattice thermal conductivity ($κ_l$). Here, we present the computational prediction of a large family of 628 thermodynamically stable quaternary chalcogenides, AMM'Q$_3$ (A = alkali/alkaline earth/post-transition metals; M/M' = transition metals, lanthanides; Q = chalcogens) using high-throughput density functional theory (DFT) calculations. We validate the presence of low-$κ_l$ in this family of materials by calculating $κ_l$ of several predicted stable compounds using the Peierls-Boltzmann transport equation within a first-principles DFT framework. Our analysis reveals that the low-$κ_l$ in the AMM'Q$_3$ compounds originates from the presence of either a strong lattice anharmonicity that enhances the phonon scatterings or rattlers cations that lead to multiple scattering channels in their crystal structures. Our predictions suggest new experimental research opportunities in the synthesis and characterization of these stable, low-$κ_l$ compounds.

cond-mat.mtrl-sci↗

arXiv2020

Microscopic mechanism of unusual lattice thermal transport in TlInTe$_2$

We investigate the microscopic mechanism of ultralow lattice thermal conductivity ($κ_l$) of TlInTe$_2$ and its weak temperature dependence using a unified theory of lattice heat transport that considers contributions arising from the particle-like propagation as well as wave-like tunneling of phonons. While we use the Peierls-Boltzmann transport equation (PBTE) to calculate the particle-like contributions ($κ_l$(PBTE)), we explicitly calculate the off-diagonal (OD) components of the heat-flux operator within a first-principles density functional theory framework to determine the contributions ($κ_l$(OD)) arising from the wave-like tunneling of phonons. At each temperature, T, we anharmonically renormalize the phonon frequencies using the self-consistent phonon theory including quartic anharmonicity, and utilize them to calculate $κ_l$(PBTE) and $κ_l$(OD). With the combined inclusion of $κ_l$(PBTE), $κ_l$(OD), and additional grain-boundary scatterings, our calculations successfully reproduce the experimental results. Our analysis shows that large quartic anharmonicity of TlInTe$_2$ (a) strongly hardens the low-energy phonon branches, (b) diminishes the three-phonon scattering processes at finite T, and (c) recovers the weaker than T$^{-1}$ decay of the measured $κ_l$.

cond-mat.mtrl-sci↗

arXiv2020

Accurate and scalable multi-element graph neural network force field and molecular dynamics with direct force architecture

Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant, but rotationally-covariant to the coordinate of the atoms. We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems, but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system. Finally, we use our framework to perform an MD simulation of Li7P3S11, a superionic conductor, and show that resulting Li diffusion coefficient is within 14% of that obtained directly from AIMD. The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems.

physics.comp-ph↗

arXiv2020

Microscopic Mechanisms of Glass-Like Lattice Thermal Transport in Cubic Cu$_{12}$Sb$_{4}$S$_{13}$ Tetrahedrites

Materials based on cubic tetrahedrites (Cu$_{12}$Sb$_{4}$S$_{13}$) are useful thermoelectrics with unusual thermal and electrical transport properties, such as very low and nearly temperature-independent lattice thermal conductivity ($κ_{L}$). We explain the microscopic origin of the glass-like $κ_{L}$ in Cu$_{12}$Sb$_{4}$S$_{13}$ by explicitly treating anharmonicity up to quartic terms for both phonon energies and phonon scattering rates. We show that the strongly unstable phonon modes associated with trigonally coordinated Cu atoms are anharmonically stabilized above approximately $100$ K and continue hardening with increasing temperature, in accord with experimental data. This temperature induced hardening effect reduces scattering of heat carrying acoustic modes by reducing the available phase space for three-phonon processes, thereby balancing the conventional $\propto T$ increase in scattering due to phonon population and yielding nearly temperature-independent $κ_{L}$. Furthermore, we find that very strong phonon broadening lead to a qualitative breakdown of the conventional phonon-gas model and modify the dominant heat transport mechanism from the particle-like phonon wave packet propagation to incoherent tunneling described by off-diagonal terms in the heat-flux operator, which are typically prevailing in glasses and disordered crystals. Our work paves the way to a deeper understanding of glass-like thermal conductivity in complex crystals with strong anharmonicity.

cond-mat.mtrl-sci↗

arXiv2020

Prediction of Li intercalation voltages in rechargeable battery cathode materials: effects of exchange-correlation functional, van der Waals interactions, and Hubbard $U$

Quantitative predictions of the Li intercalation voltage and of the electronic properties of rechargeable battery cathode materials are a substantial challenge for first-principles theory due to the possibility of (1) strong correlations associated with localized transition metal $d$ electrons and (2) significant van der Waals (vdW) interactions in layered systems, both of which are not accurately captured by standard approximations to density functional theory (DFT). Here, we perform a systematic benchmark of electronic structure methods based on the widely-used generalized gradient approximation of Perdew, Burke, and Ernzerhof (PBE) and the new strongly constrained and appropriately normed (SCAN) meta-generalized gradient approximation for battery cathode materials. Studying layered Li$_x$TiS$_2$, Li$_x$NiO$_2$, and Li$_x$CoO$_2$, olivine Li$_x$FePO$_4$, and spinel Li$_x$Mn$_2$O$_4$, we compute the voltage, crystal structure, and electronic structure with and without extensions to incorporate on-site Hubbard interactions and vdW interactions. Within pure DFT (i.e., without corrections for on-site Hubbard interactions), SCAN is a significant improvement over PBE for describing cathode materials, decreasing the mean absolute voltage error by more than 50%. Although explicit vdW interactions are not critical and in cases even detrimental when applied in conjunction with SCAN, Hubbard $U$ corrections are still in general necessary to achieve reasonable agreement with experiment. We show that no single method considered here can accurately describe the voltage and overall structural, electronic, and magnetic properties (i.e., errors no more than 5% for voltage, volume, band gap, and magnetic moments) of battery cathode materials, motivating a strong need for improved electronic structure approaches for such systems.

cond-mat.mtrl-sci↗

arXiv2020

Direct Visualization of Electric Field induced Structural Dynamics in Monolayer Transition Metal Dichalcogenides

Layered transition metal dichalcogenides (TMDs) offer many attractive features for next-generation low-dimensional device geometries. Due to the practical and fabrication challenges related to in situ methods, the atomistic dynamics that give rise to realizable macroscopic device properties are often unclear. In this study, in situ transmission electron microscopy techniques are utilized in order to understand the structural dynamics at play, especially at interfaces and defects, in the prototypical film of monolayer MoS2 under electrical bias. Through our sample fabrication process, we clearly identify the presence of mass transport in the presence of a lateral electric field. In particular, we observe that the voids present at grain boundaries combine to induce structural deformation. The electric field mediates a net vacancy flux from the grain boundary interior to the exposed surface edge sites that leaves molybdenum clusters in its wake. Following the initial biasing cycles, however, the mass flow is largely diminished, and the resultant structure remains stable over repeated biasing. We believe insights from this work can help explain observations of non-uniform heating and preferential oxidation at grain boundary sites in these materials.

cond-mat.mtrl-sci↗

arXiv2019

Leveraging Electron-Phonon Interaction to Enhance Thermoelectric Power Factor in Graphene-Like Semimetals

Electron-phonon interaction (EPI) is presumably detrimental for thermoelectric performance in semiconductors because it limits carrier mobility. Here we show that enhanced EPI with strong energy dependence offers an intrinsic pathway to significant increase in the Seebeck coefficient and the thermoelectric power factor, particularly in the context of two-dimensional (2D) graphene-like Dirac bands. The increase is realized by enabling electron energy filtering through preferential scattering of electron/hole carriers. We prove this concept by implementing state-of-the-art first-principles computational methods with explicit treatment of EPI for a 2D gapless MoS$_{2}$ allotrope, which has both massless Dirac bands and a heavy-fermion state that acts as the filter. We determine that the optimal location of the heavy state and hence the onset of the filtering process is at the Dirac point. Our study opens a new avenue for attaining ultrahigh power factors via engineering the EPI in graphene-like semimetals or identifying new compounds that intrinsically possess the featured electronic structure.

cond-mat.mtrl-sci↗

arXiv2019

Electronic Structure and Phase Stability of Yb-filled CoSb$_3$ Skutterudite Thermoelectrics from First Principles

Filling the large voids in the crystal structure of the skutterudite CoSb$_3$ with rattler atoms $R$ provides an avenue for both increasing carrier concentration and disrupting lattice heat transport, leading to impressive thermoelectric performance. While the influence of $R$ on the lattice dynamics of skutterudite materials has been well studied, the phase stability of $R$-filled skutterudite materials and the influence of the presence and ordering of $R$ on the electronic structure remain unclear. Here, focusing on the Yb-filled skutterudite Yb$_x$Co$_4$Sb$_{12}$, we employ first-principles methods to compute the phase stability and electronic structure. Yb-filled CoSb$_3$ exhibits (1) a mild tendency for phase separation into Yb-rich and Yb-poor regions and (2) a strong tendency for chemical decomposition into Co--Sb and Yb--Sb binaries (i.e., CoSb$_3$, CoSb$_2$, and YbSb$_2$). We find that, at reasonable synthesis temperatures, configurational entropy stabilizes single-phase solid solutions with limited Yb solubility, in agreement with experiments. Filling CoSb$_3$ with Yb increases the band gap, enhances the carrier effective masses, and generates new low-energy ``emergent'' conduction band minima, which is distinct from the traditional band convergence picture of aligning the energies of existing band extrema. The explicit presence of $R$ is necessary to achieve the emergent conduction band minima, though the rattler ordering does not strongly influence the electronic structure. The emergent conduction bands are spatially localized in the Yb-rich regions, unlike the delocalized electronic states at the Brillouin zone center that form the unfilled skutterudite band edges.

cond-mat.mtrl-sci↗