Showing papers by "Anubhav Jain published in 2020"

Efficient calculation of carrier scattering rates from first principles.

Abstract: 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.

A critical examination of compound stability predictions from machine-learned formation energies

Abstract: Machine learning has emerged as a novel tool for the efficient prediction of material properties, and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional Theory (DFT). The models tested in this work include five recently published compositional models, a baseline model using stoichiometry alone, and a structural model. By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85,014 unique chemical compositions, we show that while formation energies can indeed be predicted well, all compositional models perform poorly on predicting the stability of compounds, making them considerably less useful than DFT for the discovery and design of new solids. Most critically, in sparse chemical spaces where few stoichiometries have stable compounds, only the structural model is capable of efficiently detecting which materials are stable. The nonincremental improvement of structural models compared with compositional models is noteworthy and encourages the use of structural models for materials discovery, with the constraint that for any new composition, the ground-state structure is not known a priori. This work demonstrates that accurate predictions of formation energy do not imply accurate predictions of stability, emphasizing the importance of assessing model performance on stability predictions, for which we provide a set of publicly available tests.

Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm

Abstract: We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a materials composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully-automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm - namely, that crystal graph methods appear to outperform traditional machine learning methods given ~10^4 or greater data points. The pre-processed, ready-to-use Matbench tasks and the Automatminer source code are open source and available online (this http URL). We encourage evaluating new materials ML algorithms on the MatBench benchmark and comparing them against the latest version of Automatminer.

Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm

Abstract: Author(s): Dunn, A; Wang, Q; Ganose, A; Dopp, D; Jain, A | Abstract: We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a material’s composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm—namely, that crystal graph methods appear to outperform traditional machine learning methods given ~104 or greater data points. We encourage evaluating materials ML algorithms on the Matbench benchmark and comparing them against the latest version of Automatminer.

Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity

Abstract: Structure characterization and classification is frequently based on local environment information of all or selected atomic sites in the crystal structure. Therefore, reliable and robust procedures to find coordinated neighbors and to evaluate the resulting coordination pattern (e.g., tetrahedral, square planar) are critically important for both traditional and machine learning approaches that aim to exploit site or structure information for predicting materials properties. Here, we introduce new local structure order parameters (LoStOPs) that are specifically designed to rapidly detect highly symmetric local coordination environments (e.g., Platonic solids such as a tetrahedron or an octahedron) as well as less symmetric ones (e.g., Johnson solids such as a square pyramid). Furthermore, we introduce a Monte Carlo optimization approach to ensure that the different LoStOPs are comparable with each other. We then apply the new local environment descriptors to define site and structure fingerprints and to measure similarity between 61 known coordination environments and 40 commonly studied crystal structures, respectively. After extensive testing and optimization, we determine the most accurate structure similarity assessment procedure to compute all 2.45 billion structure similarities between each pair of the ≈70 000 materials that are currently present in the Materials Project database.

An automatically curated first-principles database of ferroelectrics

Abstract: Ferroelectric materials have technological applications in information storage and electronic devices. The ferroelectric polar phase can be controlled with external fields, chemical substitution and size-effects in bulk and ultrathin film form, providing a platform for future technologies and for exploratory research. In this work, we integrate spin-polarized density functional theory (DFT) calculations, crystal structure databases, symmetry tools, workflow software, and a custom analysis toolkit to build a library of known, previously-proposed, and newly-proposed ferroelectric materials. With our automated workflow, we screen over 67,000 candidate materials from the Materials Project database to generate a dataset of 255 ferroelectric candidates, and propose 126 new ferroelectric materials. We benchmark our results against experimental data and previous first-principles results. The data provided includes atomic structures, output files, and DFT values of band gaps, energies, and the spontaneous polarization for each ferroelectric candidate. We contribute our workflow and analysis code to the open-source python packages atomate and pymatgen so others can conduct analogous symmetry driven searches for ferroelectrics and related phenomena.

When Band Convergence is Not Beneficial for Thermoelectrics

Abstract: Band convergence is considered a clear benefit to thermoelectric performance because it increases the charge carrier concentration for a given Fermi level, which typically enhances charge conductivity while preserving the Seebeck coefficient. However, this advantage hinges on the assumption that interband scattering of carriers is weak or insignificant. With first-principles treatment of electron-phonon scattering in CaMg$_{2}$Sb$_{2}$-CaZn$_{2}$Sb$_{2}$ Zintl system and full Heusler Sr$_{2}$SbAu, we demonstrate that the benefit of band convergence can be intrinsically negated by interband scattering depending on the manner in which bands converge. In the Zintl alloy, band convergence does not improve weighted mobility or the density-of-states effective mass. We trace the underlying reason to the fact that the bands converge at one k-point, which induces strong interband scattering of both the deformation-potential and the polar-optical kinds. The case contrasts with band convergence at distant k-points (as in the full Heusler), which better preserves the single-band scattering behavior thereby successfully leading to improved performance. Therefore, we suggest that band convergence as thermoelectric design principle is best suited to cases in which it occurs at distant k-points.

propnet: A Knowledge Graph for Materials Science

Abstract: Author(s): Mrdjenovich, D; Horton, MK; Montoya, JH; Legaspi, CM; Dwaraknath, S; Tshitoyan, V; Jain, A; Persson, KA | Abstract: Discovering the ideal material for a new application involves determining its numerous properties, such as electronic, mechanical, or thermodynamic, to match those of its desired application. The rise of high-throughput computation has meant that large databases of material properties are now accessible to scientists. However, these databases contain far more information than might appear at first glance, since many relationships exist in the materials science literature to derive, or at least approximate, additional properties. propnet is a new computational framework designed to help scientists to automatically calculate additional information from their datasets. It does this by constructing a network graph of relationships between different materials properties and traversing this graph. Initially, propnet contains a catalog of over 100 property relationships but the hope is for this to expand significantly in the future, and contributions from the community are welcomed.

Machine Learning Chemical Guidelines for Engineering Electronic Structures in Half-Heusler Thermoelectric Materials.

Abstract: Half-Heusler materials are strong candidates for thermoelectric applications due to their high weighted mobilities and power factors, which is known to be correlated to valley degeneracy in the electronic band structure. However, there are over 50 known semiconducting half-Heusler phases, and it is not clear how the chemical composition affects the electronic structure. While all the n-type electronic structures have their conduction band minimum at either the Γ- or X-point, there is more diversity in the p-type electronic structures, and the valence band maximum can be at either the Γ-, L-, or W-point. Here, we use high throughput computation and machine learning to compare the valence bands of known half-Heusler compounds and discover new chemical guidelines for promoting the highly degenerate W-point to the valence band maximum. We do this by constructing an "orbital phase diagram" to cluster the variety of electronic structures expressed by these phases into groups, based on the atomic orbitals that contribute most to their valence bands. Then, with the aid of machine learning, we develop new chemical rules that predict the location of the valence band maximum in each of the phases. These rules can be used to engineer band structures with band convergence and high valley degeneracy.

Computational discovery of promising new n-type dopable ABX Zintl thermoelectric materials

Abstract: Computational prediction of good thermoelectric (TE) performance in several n-type doped Zintl phases, combined with successful experimental realization, has sparked interest in discovering new n-type dopable members of this family of materials. However, most known Zintls are typically only p-type dopable; prior successes in finding n-type Zintl phases have been largely serendipitous. Here, we go beyond previously synthesized Zintl phases and perform chemical substitutions in known n-type dopable ABX Zintl phases to discover new ones. We use first-principles calculations to predict their stability, potential for TE performance as well as their n-type dopability. Using this approach, we find 17 new ABX Zintl phases in the KSnSb structure type that are predicted to be stable. Several of these newly predicted phases (KSnBi, RbSnBi, NaGeP) are found to exhibit promising n-type TE performance and are n-type dopable. We propose these compounds for further experimental studies, especially KSnBi and RbSnBi, which are both predicted to be good TE materials with high electron concentrations due to self-doping by native defects, when grown under alkali-rich conditions.

High Thermoelectric Performance and Defect Energetics of Multi-pocketed Full-Heusler Compounds

Abstract: We report first-principles density-functional study of electron-phonon interactions and thermoelectric transport properties of full-Heusler compounds Sr$_{2}$BiAu and Sr$_{2}$SbAu. Our results show that ultrahigh intrinsic bulk thermoelectric performance across a wide range of temperatures is physically possible and point to the presence of multiply degenerate and highly dispersive carrier pockets as the key factor for achieving it. Sr$_{2}$BiAu, which features ten energy-aligned low effective mass pockets (six along $\Gamma-X$ and four at $L$), is predicted to deliver $n$-type $zT=0.4-4.9$ at $T=100-700$~K. Comparison with the previously investigated Ba$_{2}$BiAu compound shows that the additional $L$-pockets in Sr$_{2}$BiAu significantly increase its low-temperature power factor to a maximum value of $12$~mW~m$^{-1}$~K$^{-2}$ near $T=300$~K. However, at high temperatures the power factor of Sr$_{2}$BiAu drops below that of Ba$_{2}$BiAu because the $L$ states are heavier and subject to strong scattering by phonon deformation as opposed to the lighter $\Gamma-X$ states that are limited by polar-optical scattering. Sr$_{2}$SbAu is predicted to deliver lower $n$-type of $zT=3.4$ at $T=750$~K due to appreciable misalignment between the $L$ and $\Gamma-X$ carrier pockets, generally heavier scattering, and slightly higher lattice thermal conductivity. Soft acoustic modes, responsible for low lattice thermal conductivity, also increase vibrational entropies and high-temperature stability of the Heusler compounds, suggesting that their experimental synthesis may be feasible. The dominant intrinsic defects are found to be Au vacancies, which drive the Fermi level towards the conduction band and work in favor of $n$-doping.

Reducing Interanalyst Variability in Photovoltaic Degradation Rate Assessments

Abstract: The economic return on investment of a commercial photovoltaic system depends greatly on its performance over the long term and, hence, its degradation rate. Many methods have been proposed for assessing system degradation rates from outdoor performance data. However, comparing reported values from one analyst and research group to another requires a common baseline of performance; consistency between methods and analysts can be a challenge. An interlaboratory study was conducted involving different volunteer analysts reporting on the same photovoltaic performance data using different methodologies. Initial variability of the reported degradation rates was so high that analysts could not come to a consensus whether a system degraded or not. More consistent values are received when written guidance is provided to each analyst. Further improvements in analyst variance was accomplished by using the free open-source software RdTools, allowing a reduction in variance between analysts by more than two orders of magnitude over the first round, where multiple analysis methods are allowed. This article highlights many pitfalls in conducting “routine” degradation analysis, and it addresses some of the factors that must be considered when comparing degradation results reported by different analysts or methods.

A critical examination of compound stability predictions from machine-learned formation energies

Abstract: Machine learning has emerged as a novel tool for the efficient prediction of materials properties, and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional Theory (DFT). The models tested in this work include five recently published compositional models, a baseline model using stoichiometry alone, and a structural model. By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85,014 unique chemical compositions, we show that while formation energies can indeed be predicted well, all compositional models perform poorly on predicting the stability of compounds, making them considerably less useful than DFT for the discovery and design of new solids. Most critically, in sparse chemical spaces where few stoichiometries have stable compounds, only the structural model is capable of efficiently detecting which materials are stable. The non-incremental improvement of structural models compared with compositional models is noteworthy and encourages the use of structural models for materials discovery, with the constraint that for any new composition, the ground-state structure is not known a priori. This work demonstrates that accurate predictions of formation energy do not imply accurate predictions of stability, emphasizing the importance of assessing model performance on stability predictions, for which we provide a set of publicly available tests.

Gapped metals as thermoelectric materials revealed by high-throughput screening

Abstract: The typical strategy to design high performance thermoelectric materials is to dope a semiconducting material until optimal properties are obtained. However, some known thermoelectric materials such as La3Te4, Mo3Sb7, Yb14MnSb11, and NbCoSb are actually gapped metals, i.e., their band structure displays a gap slightly above or below the band crossed by the Fermi level. This key feature makes these metals comparable to degenerate semiconductors and thus suitable for thermoelectric applications. In this work, we perform a computational high-throughput search for such gapped metals exhibiting attractive thermoelectric properties. Several thousands of metals are found to present this key feature, and about one thousand of them show decent thermoelectric properties as evaluated by a computed zT. We present the different chemistry of gapped metals we discovered such as clathrates, Chevrel phases, or transition metal dichalcogenides and discuss their previous studies as thermoelectric and their potential as new thermoelectric materials.

Enumeration as a Tool for Structure Solution: A Materials Genomic Approach to Solving the Cation-Ordered Structure of Na3V2(PO4)2F3

Abstract: While powder diffraction methods are routinely utilized to optimize structural models for compounds whose crystal structures are known, the determination of unknown structures is far more challengi...

Optimal Band Structure for Thermoelectrics with Realistic Scattering and Bands.

Abstract: Understanding how to optimize electronic band structures for thermoelectrics is a topic of long-standing interest in the community. Prior models have been limited to simplified bands and/or scattering models. In this study, we apply more rigorous scattering treatments to more realistic model band structures - upward-parabolic bands that inflect to an inverted parabolic behavior - including cases of multiple bands. In contrast to common descriptors (e.g., quality factor and complexity factor), the degree to which multiple pockets improve thermoelectric performance is bounded by interband scattering and the relative shapes of the bands. We establish that extremely anisotropic `flat-and-dispersive' bands, although best-performing in theory, may not represent a promising design strategy in practice. Critically, we determine optimum bandwidth, dependent on temperature and lattice thermal conductivity, from perfect transport cutoffs that can in theory significantly boost $zT$ beyond the values attainable through intrinsic band structures alone. Our analysis should be widely useful as the thermoelectric research community eyes $zT>3$.

High Thermoelectric Performance and Defect Energetics of Multipocketed Full Heusler Compounds

Abstract: Author(s): Park, J; Xia, Y; Ganose, AM; Jain, A; Ozoliņs, V | Abstract: We report a first-principles density-functional study of electron-phonon interactions in and thermoelectric transport properties of the full Heusler compounds Sr2BiAu and Sr2SbAu. Our results show that ultrahigh intrinsic bulk thermoelectric performance across a wide range of temperatures is physically possible and point to the presence of multiply degenerate and highly dispersive carrier pockets as the key factor for achieving this. Sr2BiAu, which features ten energy-aligned low-effective-mass pockets (six along Γ-X and four at L), is predicted to deliver n-type zT=0.4-4.9 at T=100-700 K. Comparison with the previously investigated compound Ba2BiAu shows that the additional L pockets in Sr2BiAu significantly increase its low-temperature power factor to a maximum value of 12 mW m-1 K-2 near T=300 K. However, at high temperatures the power factor of Sr2BiAu drops below that of Ba2BiAu because the L states are heavier and subject to strong scattering by phonon deformation, as opposed to the lighter Γ-X states, which are limited by polar-optical scattering. Sr2SbAu is predicted to deliver a lower n-type zT=3.4 at T=750 K due to appreciable misalignment between the L and Γ-X carrier pockets, generally heavier scattering, and a slightly higher lattice thermal conductivity. Soft acoustic modes, which are responsible for the low lattice thermal conductivity, also increase the vibrational entropy and high-temperature stability of these Heusler compounds, suggesting that their experimental synthesis may be feasible. The dominant intrinsic defects are found to be Au vacancies, which drive the Fermi level towards the conduction band and work in favor of n-doping.

Beyond the competency frameworks-conceptualizing and deploying employee strengths at work

Abstract: Purpose With growing stress at work, the need for scholars to focus on humanizing organizations is pressing. Scholars agree five factors lead to humanizing organizations. This study dwells upon one factor – employee strengths at work (ESAW) – problematizes, identifies the gap in its conceptualization, deploys critical social systems theory and reconceptualizes the construct of ESAW by taking key contextual factors into consideration. Thereafter, this study aims to develop a conceptual model and makes propositions related to the mediating effects of ESAW on the association of leadership style and employee performance. Design/methodology/approach Aimed at contributing to humanizing organizations, this conceptual study problematizes the construct of competency and the trait-based conceptualization of strengths in identifying gaps in the construct of competency for humanizing organizations. Next, the study deploys the technique of construct mixology for evolving the new construct of ESAW. To empirically test ESAW in the field, the authors deploy the critical social systems theory and develop a conceptual model. Further, drawing upon the conceptual model and the extant literature, the authors develop many propositions for enabling future research. Findings The study develops a new construct of ESAW that holds the promise of contributing to humanizing organizations. By embedding the current trait-based conceptualization of employee strengths to the context of the organization, the new five-factor construct of ESAW is indigenous to the field of organization science, hence, has a higher relevance. The study develops a conceptual model and makes propositions for empirically testing the new construct in the field that future researchers may focus upon. Research limitations/implications There is a compelling need for humanizing organizations. This conceptual study attempts to bring back the focus of researchers on humanizing organizations, within the framework of the market-driven economy. The new construct of ESAW has huge potential for theory-building and empirical testing. Practical implications Deployment of ESAW will contribute to humanizing organizations. The construct of ESAW is relevant to practice as it has evolved from the domain of organization science, unlike the earlier trait-based conceptualization of strength that emerged in personality psychology. Practitioners can deploy the construct of ESAW and achieve the two seemingly conflicting objectives of enabling employee well-being while also ensuring superior performance. Social implications Any contribution toward humanizing organizations forebodes increasing the social capital and the personal well-being of employees. If employees are happy at work, their productivity increases. As per the broaden and build theory of Fredrickson, higher well-being and productivity at work creates a spiral of positivity that transcends the working life of an employee. Hence, the study has huge social implications at times when the social fabric is stretched because of multiple demands on an employee. Originality/value Constructs developed in other fields and adopted in organization science have less relevance than those evolved in the domain of organization science. Past deficient conceptualization and practices persist unless scholars logically challenge it an alternative and improved conceptualization provided. The new construct of ESAW uses the method of construct mixology after unravelling the assumptions that impedes humanizing organizations.

Author Correction: Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm

Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Photovoltaic String Sizing Using Site-Specific Modeling

Abstract: One key design decision for photovoltaic (PV) power plants is to select the number of PV modules connected in series, also called the string size. Longer strings typically lower total system costs, but the string size must still meet relevant electrical standards to ensure that the maximum system voltage remains less than the design voltage. Traditional methods calculate string size using the temperature coefficient of open-circuit voltage assuming that the coldest expected temperature occurs simultaneously with a full-sun irradiance of ${\text{1000}}\,{\text{W/m}}^{\text{2}}$ . Here, we demonstrate that this traditional method is unnecessarily conservative, resulting in a string size $\sim$ 10% shorter than necessary to maintain system voltage within limits. Instead, engineers may determine string size by modeling open-circuit voltage over time using historical weather data, a method consistent with the 2017 National Electric Code. For bifacial systems, we derive a simple additive term that predicts the additional voltage rise. We demonstrate that this site-specific modeling procedure predicts open-circuit voltages in close agreement with data from field measurements. We further perform a comprehensive sensitivity analysis to identify an appropriate safety factor. By using site-specific modeling instead of traditional methods, we estimate a $\sim$ 1.2% reduction in levelized cost of electricity, a significant improvement to PV power plant economics. The method is provided as an easy-to-use web tool and as an open-source Python package.

Aqueous Diels-Alder reactions for thermochemical storage and heat transfer fluids identified using density functional theory.

Abstract: Thermal storage and transfer fluids have important applications in industrial, transportation, and domestic settings. Current thermal fluids have relatively low specific heats, often significantly below that of water. However, by introducing a thermochemical reaction to a base fluid, it is possible to enhance the fluid's thermal properties. In this work, density functional theory (DFT) is used to screen Diels-Alder reactions for use in aqueous thermal fluids. From an initial set of 52 reactions, four are identified with moderate aqueous solubility and predicted turning temperature near the liquid region of water. These reactions are selectively modified through 60 total functional group substitutions to produce novel reactions with improved solubility and thermal properties. Among the reactions generated by functional group substitution, seven have promising predicted thermal properties, significantly improving specific heat (by as much as 30.5%) and energy storage density (by as much as 4.9%) compared to pure water.

Visual Characterization of Anti-Reflective Coating on Solar Module Glass

Abstract: Anti-reflection coatings (ARCs) are widely used on PV module glass to increase light transmission. The PV community is increasingly concerned with how long these coatings last in the field and would benefit from a simple method for quantifying performance on fielded modules. In this work, we demonstrate how a straightforward visual inspection of the color of reflected light can identify the presence of an interference-based ARC. By tracking the color shift over time a qualitative measurement of ARC degradation can be made. This method is applicable in full-sun outdoor conditions and only requires a flashlight and a standard RGB camera. We demonstrate how the physics of thin-film coating interference and color theory accurately predict the color the reflected light. This technique could gain widespread use for inspecting PV modules in the field because it is easy to perform and requires no specialized equipment.

Human strengths: a systematic conceptual review, conceptualizing employee strengths at work and a framework for management development

Abstract: Utilizing employee strengths contributes to humanizing organizations. However, the current concept of strengths has evolved from the domain of social work, advanced by personality and positive psychologists and adopted in management. The trait-like conceptualization of strengths conceptualized by psychologists is of lesser relevance to organizations as it discounts the significance of contextual factors for manifesting employee strengths. This study traces the evolution of strengths conceptualization, identifies gaps in its relevance to organizations, employs the concept relation method for developing a conceptualization of employee strengths at work and proposes a framework for management development that predicts improved employee engagement and performance.,The study utilizes the Cochrane method for carrying out a systematic conceptual review and shortlists 19 articles from an initial selection of 430 articles. Drawing insights from the 19 reviewed studies, the study deploys the concept relation method to conceptualize the concept of employees' strengths at work (ESAW) that has a higher relevance for management and organizational behavior. Thereafter, utilizing ESAW, the study proposes a conceptual framework that has huge implications for improving employee engagement and performance by carrying out effective management development. The conceptual framework additionally serves as a springboard for future empirical research.,The conceptualization of human strengths in extant literature favors a trait-based conceptualization advanced by personality psychologists. Concepts borrowed from other domains have lesser relevance than those indigenously developed in the field of management. Incorporating the recent empirical evidence highlighting the importance of factoring in key contextual attributes for the strengths to manifest at work, this study develops a new higher-order construct of ESAW that factors in personal as well as situational variables. Thereafter, the study suggests a conceptual framework for effectively carrying out management development by utilizing the new construct of ESAW.,Deployment of ESAW will contribute to humanize organizations, improve employee engagement and performance. The construct of ESAW is relevant to practice as it has evolved from the domain of organization science, unlike the earlier trait-based conceptualization of strength that emerged in personality psychology. The conceptual framework proposed in the study can be utilized by practitioners for carrying out effective management development.,Any contribution to increasing employee engagement predicts increasing social capital. If employees are happy at work, their productivity increases. Furthermore, higher engagement and productivity at work creates a spiral of positivity that transcends the working life of an employee. Hence, the study has huge social implications at times when the social fabric is stretched due to multiple demands on an employee.,Constructs developed in other fields and adopted in management have less relevance than those evolved indigenously in the domain of management. The systematic conceptual review of the concept of human strengths reveals a gap in its relevance to organizations. The study develops a new concept of ESAW that has higher relevance for organizational behavior and holds the promise of humanizing organizations. The next originality of the study lies in proposing a conceptual framework for carrying out effective management development that predicts higher employee engagement and performance. The methodological originality lies in utilizing the systematic conceptual review for developing a new concept.

High Thermoelectric Performance in Multi-pocketed Full-Heuslers and Their Defect Energetics

Abstract: This study, utilizing high-fidelity methods for computing electron-phonon scattering rates, theoretically demonstrates that ultrahigh intrinsic bulk thermoelectric performance across cryogenic-to-high temperatures is physically possible. It also demonstrates the benefit of accidental band degeneracy to thermoelectric performance is conditional upon their characters. Full-Heusler Sr$_{2}$BiAu featuring ten energy-aligned dispersive pockets (six along $\Gamma-X$ and four at $L$) is herein predicted to be theoretically capable of delivering $zT=0.4-4.9$ at $100-700$ K. Relative to the previously investigated Ba$_{2}$BiAu, the additional $L$-pockets in Sr$_{2}$BiAu significantly increase the power factor at low temperatures, as high as 12 mW m$^{-1}$ K$^{-2}$ near room temperature. As temperature rises, the performance decays quickly and sinks below that of Ba$_{2}$BiAu due to the differing dispersion and scattering characteristics of the $L$ and $\Gamma-X$ states. Sr$_{2}$SbAu is generally projected to deliver worse performance due to the appreciable energy-misalignment in the two accessible band pockets. The dominant intrinsic defect at play is Bi/Sb$_{\text{Au}}$ antisites, which limit the $n$-dopabilities of all of the Heusler compounds. Calculations suggest only Sr$_{2}$SbAu potentially has both a large enough stability region and high enough Sb$_{\text{Au}}$ antisite formation energies to retain some small chance at experimental realization as a high-performing thermoelectric.

A Model for Thermodynamic Properties of Monoatomic Liquids

Abstract: We present an analytical model for calculating the thermodynamic properties of monoatomic liquids using a rough potential energy surface (PES). The PES is transformed into an equivalent simple harmonic oscillator. Without employing any adjustable parameters, the model agrees closely with experimental entropy, heat capacity, and latent heat of fusion and vaporization data for monatomic liquids. In addition, it offers a simple, physical explanation for Richard Melting rule, and provides a material-dependent correction to Trouton Vaporization rule.