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Combinatorial and guided screening of materials space with density-functional theory and related approaches has provided a wealth of hypothetical inorganic materials, which are increasingly tabulated in open databases. The OPTIMADE API is a standardised format for representing crystal structures, their measured and computed properties, and the methods for querying and filtering them from remote resources. Currently, the OPTIMADE federation spans over 20 data providers, rendering over 30 million structures accessible in this way, many of which are novel and have only recently been suggested by machine learning-based approaches. In this work, we outline our approach to non-exhaustively screen this dynamic trove of structures for the next-generation of optical materials. By applying MODNet, a neural network-based model for property prediction, within a combined active learning and high-throughput computation framework, we isolate particular structures and chemistries that should be most fruitful for further theoretical calculations and for experimental study as high-refractive-index materials. By making explicit use of automated calculations, federated dataset curation and machine learning, and by releasing these publicly, the workflows presented here can be periodically re-assessed as new databases implement OPTIMADE, and new hypothetical materials are suggested.

The chalcogenide perovskite BaZrS$_3$ has attracted much attention as a promising solar absorber for thin-film photovoltaics. Here, we use first-principles calculations to evaluate its carrier transport and defect properties. We find that BaZrS$_3$ has a phonon-limited electron mobility of 37 cm$^2$/Vs comparable to that in halide perovskites but lower hole mobility of 11 cm$^2$/Vs. The defect computations indicate that BaZrS$_3$ is intrinsically n-type due to shallow sulfur vacancies, but that strong compensation by sulfur vacancies will prevent attempts to make it p-type. We also establish that BaZrS$_3$ shows some degree of defect tolerance, presenting only few low formation energy, deep intrinsic defects. Among the deep defects, sulfur interstitials are the dominant nonradiative recombination centers but exhibit a moderate capture coefficient. Our work highlights the material's intrinsic limitations in carrier mobility and p-type doping and suggests focusing on suppressing the formation of sulfur interstitials to reach longer carrier lifetime.

Members of the DFT+U family of functionals are increasingly prevalent methods of addressing errors intrinsic to (semi-) local exchange-correlation functionals at minimum computational cost, but require their parameters U and J to be calculated in situ for a given system of interest, simulation scheme, and runtime parameters. The SCF linear response approach offers ab initio acquisition of the U and has recently been extended to compute the J analogously, which measures localized errors related to exchange-like effects. We introduce a renovated post-processor, the lrUJ utility, together with this detailed best-practices guide, to enable users of the popular, open-source Abinit first-principles simulation suite to engage easily with in situ Hubbard parameters and streamline their incorporation into material simulations of interest. Features of this utility, which may also interest users and developers of other DFT codes, include $n$-degree polynomial regression, error analysis, Python plotting facilities, didactic documentation, and avenues for further developments. In this technical introduction and guide, we place particular emphasis on the intricacies and potential pitfalls introduced by the projector augmented wave (PAW) method, SCF mixing schemes, and non-linear response, several of which are translatable to DFT+U(+J) implementations in other packages., Comment: 46 pages, 6 figures (+3 figures in appendix), 2 tables (+1 table in appendix)

Machine-learning interatomic potentials have revolutionized materials modeling at the atomic scale. Thanks to these, it is now indeed possible to perform simulations of \abinitio quality over very large time and length scales. More recently, various universal machine-learning models have been proposed as an out-of-box approach avoiding the need to train and validate specific potentials for each particular material of interest. In this paper, we review and evaluate five different universal machine-learning interatomic potentials (uMLIPs), all based on graph neural network architectures which have demonstrated transferability from one chemical system to another. The evaluation procedure relies on data both from a recent verification study of density-functional-theory implementations and from the Materials Project. Through this comprehensive evaluation, we aim to provide guidance to materials scientists in selecting suitable models for their specific research problems, offer recommendations for model selection and optimization, and stimulate discussion on potential areas for improvement in current machine-learning methodologies in materials science., Comment: 7 figures

The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADE specification (v1.0), the API has undergone significant development, leading to the upcoming v1.2 release, and has underpinned multiple scientific studies. In this work, we highlight the latest features of the API format, accompanying software tools, and provide an update on the implementation of OPTIMADE in contributing materials databases. We end by providing several use cases that demonstrate the utility of the OPTIMADE API in materials research that continue to drive its ongoing development.

We investigate the native point defects in the long-wavelength infrared (LWIR) detector material Hg$_{0.75}$Cd$_{0.25}$Te using a dielectric-dependent hybrid density functional combined with spin-orbit coupling. Characterizing these point defects is essential as they are responsible for intrinsic doping and nonradiative recombination centers in the detector material. The dielectric-dependent hybrid functional allows for an accurate description of the band gap ($E_g$) for Hg$_{1-x}$Cd$_{x}$Te (MCT) over the entire compositional range, a level of accuracy challenging with standard hybrid functionals. Our comprehensive examination of the native point defects confirms that cation vacancies $V_\text{Hg(Cd)}$ are the primary sources of $p$-type conductivity in the LWIR material given their low defect formation energies and the presence of a shallow acceptor level ($-$/0) near the valence-band maximum (VBM). In addition to the shallow acceptor level, the cation vacancies exhibit a deep charge transition level (2$-$/$-$) situated near the midgap, characteristic of nonradiative recombination centers. Our results indicate that Hg interstitial could also be a deep center in the LWIR MCT through a metastable configuration under the Hg-rich growth conditions. While an isolated Te antisite does not show deep levels, the formation of $V_\text{Hg}$-Te$_\text{Hg}$ defect complex introduces a deep acceptor level within the band gap., Comment: 12 pages, 7 figures

Surface Enhanced Infrared Absorption (SEIRA) is an experimental method where trace amount of a compound can be detected with high sensibility. This high detection sensibility is the result of the interaction of the molecules with a localized plasmon, usually from a metallic nano-particle. In this study we numerically investigate by discrete dipole approximation the origin of the Fano-like response of the system, including the induced transparency when the plasmon resonance and the molecular vibrational mode coincide. The detailed analysis of the localization of the absorption show that the modification of the absorption cross-section when the molecule is present comes from a change of the plasmonic resonance, not from the direct molecular response which is negligible. This sheds a new light on the SEIRA mechanism. In particular, it demonstrates that the sensibility is associated with the influence of the molecule on the plasmon resonance rather than with the local field enhancement itself., Comment: 12 pages, 5 figures

Thin-film photovoltaics offers a path to significantly decarbonize our energy production. Unfortunately, current materials commercialized or under development as thin-film solar cell absorbers are far from optimal as they show either low power conversion efficiency or issues with earth-abundance and stability. Entirely new and disruptive materials platforms are rarely discovered as the search for new solar absorbers is traditionally slow and serendipitous. Here, we use first principles high-throughput screening to accelerate this process. We identify new solar absorbers among known inorganic compounds using considerations on band gap, carrier transport, optical absorption but also on intrinsic defects which can strongly limit the carrier lifetime and ultimately the solar cell efficiency. Screening about 40,000 materials, we discover the Zintl-phosphide BaCd$_{2}$P$_{2}$ as a potential high-efficiency solar absorber. Follow-up experimental work confirms the predicted promises of BaCd$_{2}$P$_{2}$ highlighting an optimal band gap for visible absorption, bright photoluminescence, and long carrier lifetime of up to 30 ns even for unoptimized powder samples. Importantly, BaCd$_{2}$P$_{2}$ does not contain any critical elements and is highly stable in air and water. Our work opens an avenue for a new family of stable, earth-abundant, high-performance Zintl-based solar absorbers. It also demonstrates how recent advances in first principles computation can accelerate the search of photovoltaic materials by combining high-throughput screening with experiment.

Point defects in two-dimensional materials are of key interest for quantum information science. However, the space of possible defects is immense, making the identification of high-performance quantum defects extremely challenging. Here, we perform high-throughput (HT) first-principles computational screening to search for promising quantum defects within WS$_2$, which present localized levels in the band gap that can lead to bright optical transitions in the visible or telecom regime. Our computed database spans more than 700 charged defects formed through substitution on the tungsten or sulfur site. We found that sulfur substitutions enable the most promising quantum defects. We computationally identify the neutral cobalt substitution to sulfur (Co$_{\rm S}^{0}$) as very promising and fabricate it with scanning tunneling microscopy (STM). The Co$_{\rm S}^{0}$ electronic structure measured by STM agrees with first principles and showcases an attractive new quantum defect. Our work shows how HT computational screening and novel defect synthesis routes can be combined to design new quantum defects., Comment: 38 pages, 19 figures

In the past decades many density-functional theory methods and codes adopting periodic boundary conditions have been developed and are now extensively used in condensed matter physics and materials science research. Only in 2016, however, their precision (i.e., to which extent properties computed with different codes agree among each other) was systematically assessed on elemental crystals: a first crucial step to evaluate the reliability of such computations. We discuss here general recommendations for verification studies aiming at further testing precision and transferability of density-functional-theory computational approaches and codes. We illustrate such recommendations using a greatly expanded protocol covering the whole periodic table from Z=1 to 96 and characterizing 10 prototypical cubic compounds for each element: 4 unaries and 6 oxides, spanning a wide range of coordination numbers and oxidation states. The primary outcome is a reference dataset of 960 equations of state cross-checked between two all-electron codes, then used to verify and improve nine pseudopotential-based approaches. Such effort is facilitated by deploying AiiDA common workflows that perform automatic input parameter selection, provide identical input/output interfaces across codes, and ensure full reproducibility. Finally, we discuss the extent to which the current results for total energies can be reused for different goals (e.g., obtaining formation energies)., Comment: Main text: 23 pages, 4 figures. Supplementary: 68 pages. Nature Review Physics 2023

By investigating the vacancy and substitutional defects in monolayer WS$_2$ with hybrid functionals, we find that there is no unique amount of Fock exchange that concurrently satisfies the generalized Koopmans' condition and reproduces the band gap and band-edge positions. Fixing the mixing parameter of Fock exchange based upon the band gap can lead to qualitatively incorrect defect physics in two-dimensional materials. Instead, excellent agreement is achieved with experiment and many-body perturbation theory within $GW$ approximation once the mixing parameters are tuned individually for the defects and the band edges. We show the departure from a unique optimized mixing parameter is inherent to two-dimensional systems as the band edges experience a reduced screening whilst the localized defects are subject to bulklike screening., Comment: Main manuscript (7 pages, 3 figures) with supplemental material (3 pages, 5 figures)

The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data principles (Findable, Accessible, Interoperable, and Reusable) must not be too narrow. Besides, the wider materials-science community ought to agree on the strategies to tackle the challenges that are specific to its data, both from computations and experiments. In this paper, we present the result of the discussions held at the workshop on "Shared Metadata and Data Formats for Big-Data Driven Materials Science". We start from an operative definition of metadata, and what features a FAIR-compliant metadata schema should have. We will mainly focus on computational materials-science data and propose a constructive approach for the FAIRification of the (meta)data related to ground-state and excited-states calculations, potential-energy sampling, and generalized workflows. Finally, challenges with the FAIRification of experimental (meta)data and materials-science ontologies are presented together with an outlook of how to meet them.

Machine-learning models have recently encountered enormous success for predicting the properties of materials. These are often trained based on data that present various levels of accuracy, with typically much less high- than low-fidelity data. In order to extract as much information as possible from all available data, we here introduce an approach which aims to improve the quality of the data through denoising. We investigate the possibilities that it offers in the case of the prediction of the band gap relying on both limited experimental data and density-functional theory relying different exchange-correlation functionals (with an increasing amount of data as the accuracy of the functional decreases). We explore different ways to combine the data into training sequences and analyze the effect of the chosen denoiser. Finally, we analyze the effect of applying the denoising procedure several times until convergence. Our approach provides an improvement over existing methods to exploit multi-fidelity data.

High-power infrared laser systems with broadband tunability are of great importance due to their wide range of applications in spectroscopy and free-space communications. These systems require nonlinear optical (NLO) crystals for wavelength up/down conversion using sum/difference frequency generation, respectively. NLO crystals need to satisfy many competing criteria, including large nonlinear optical susceptibility, large laser induced damage threshold (LIDT), wide transparency range and phase-matchability. Here, we report bulk single crystals of SnP_2S_6 with a large non-resonant SHG coefficient of d33= 53 pm/V at 1550nm and a large LIDT of 350 GW/cm^2 for femtosecond laser pulses. It also exhibits a broad transparency range from 0.54 {\mu}m to 8.5{\mu}m (bandgap of ~2.3 eV) and can be both Type I and Type II phase-matched. The complete linear and SHG tensors are measured as well as predicted by first principles calculations, and they are in excellent agreement. A proximate double-resonance condition in the electronic band structure for both the fundamental and the SHG light is shown to enhance the non-resonant SHG response. Therefore, SnP2S6 is an outstanding candidate for infrared laser applications.

The mobility of carriers, as limited by their scattering with phonons, can now routinely be obtained from first-principles electron-phonon coupling calculations. However, so far, most computations have relied on some form of simplification of the linearized Boltzmann transport equation based on either the self-energy, the momentum- or constant relaxation time approximations. Here, we develop a high-throughput infrastructure and an automatic workflow and we compute 69 phonon-limited mobilities in semiconductors. We compare the results resorting to the approximations with the exact iterative solution. We conclude that the approximate values may deviate significantly from the exact ones and are thus not reliable. Given the minimal computational overhead, our work encourages to rely on this exact iterative solution and warns on the possible inaccuracy of earlier results reported using relaxation time approximations., Comment: 6 pages, 2 figures

We report a giant thermoelectric figure of merit $ZT$ (up to 6 at 1100 K) in $n$-doped lanthanum oxysulphate LaSO. Thermoelectric coefficients are computed from ab initio bands within Bloch-Boltzmann theory in an energy-, chemical potential- and temperature-dependent relaxation time approximation. The lattice thermal conductivity is estimated from a model employing the ab initio phonon and Gr\"uneisen-parameter spectrum. The main source of the large $ZT$ is the significant power factor which correlates with a large band complexity factor. We also suggest a possible $n$-type dopant for the material based on ab initio calculations.

The optical properties of silicon can be greatly tuned by applying strain and opening new perspectives, particularly in applications where infrared is key. In this work, we use a recent model for the indirect light absorption of silicon and include the effects of tensile and compressive uniaxial strains. The model is based on material properties such as the bandgap, the conduction and valence band density-of-states effective masses, and the phonon frequencies, which are obtained from first principles including strain up to +2% along the [110] and [111] directions. We show that the limit of absorption can increase from 1.14 (1.09) to 1.35 $\mu$m (0.92 eV) under 2% strain and that the absorption increases by a factor of 55 for the zero-strain cutoff wavelength of 1.14 $\mu$m when a 2% compressive strain is applied in the [110] direction. We demonstrate that this effect is mainly due to the impact of strain on the electronic bandgaps of silicon, directly followed by the valence band density-of-states effective mass., Comment: Published in Journal of Applied Physics (4 August 2021)

The prediction of material properties through electronic-structure simulations based on density-functional theory has become routinely common, thanks, in part, to the steady increase in the number and robustness of available simulation packages. This plurality of codes and methods aiming to solve similar problems is both a boon and a burden. While providing great opportunities for cross-verification, these packages adopt different methods, algorithms, and paradigms, making it challenging to choose, master, and efficiently use any one for a given task. Leveraging recent advances in managing reproducible scientific workflows, we demonstrate how developing common interfaces for workflows that automatically compute material properties can tackle the challenge mentioned above, greatly simplifying interoperability and cross-verification. We introduce design rules for reproducible and reusable code-agnostic workflow interfaces to compute well-defined material properties, which we implement for eleven different quantum engines and use to compute three different material properties. Each implementation encodes carefully selected simulation parameters and workflow logic, making the implementer's expertise of the quantum engine directly available to non-experts. Full provenance and reproducibility of the workflows is guaranteed through the use of the AiiDA infrastructure. All workflows are made available as open-source and come pre-installed with the Quantum Mobile virtual machine, making their use straightforward.

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., Comment: 11 pages, 1 table

As the number of novel data-driven approaches to material science continues to grow, it is crucial to perform consistent quality, reliability and applicability assessments of model performance. In this paper, we benchmark the Materials Optimal Descriptor Network (MODNet) method and architecture against the recently released MatBench v0.1, a curated test suite of materials datasets. MODNet is shown to outperform current leaders on 6 of the 13 tasks, whilst closely matching the current leaders on a further 2 tasks; MODNet performs particularly well when the number of samples is below 10,000. Attention is paid to two topics of concern when benchmarking models. First, we encourage the reporting of a more diverse set of metrics as it leads to a more comprehensive and holistic comparison of model performance. Second, an equally important task is the uncertainty assessment of a model towards a target domain. Significant variations in validation errors can be observed, depending on the imbalance and bias in the training set (i.e., similarity between training and application space). By using an ensemble MODNet model, confidence intervals can be built and the uncertainty on individual predictions can be quantified. Imbalance and bias issues are often overlooked, and yet are important for successful real-world applications of machine learning in materials science and condensed matter.

Combining ferroelectricity with other properties such as visible light absorption or long-range magnetic order requires the discovery of new families of ferroelectric materials. Here, through the analysis of a high-throughput database of phonon band structures, we identify a new structural family of anti-Ruddlesden-Popper phases A$_4$X$_2$O (A=Ca, Sr, Ba, Eu, X=Sb, P, As, Bi) showing ferroelectric and anti-ferroelectric behaviors. The discovered ferroelectrics belong to the new class of hyperferroelectrics which polarize even under open-circuit boundary conditions. The polar distortion involves the movement of O anions against apical A cations and is driven by geometric effects resulting from internal chemical strains. Within this new structural family, we show that Eu$_4$Sb$_2$O combines coupled ferromagnetic and ferroelectric order at the same atomic site, a very rare occurrence in materials physics., Comment: 21 pages, 4 figures with supplemental information attached

In order to make accurate predictions of material properties, current machine-learning approaches generally require large amounts of data, which are often not available in practice. In this work, an all-round framework is presented which relies on a feedforward neural network, the selection of physically-meaningful features and, when applicable, joint-learning. Next to being faster in terms of training time, this approach is shown to outperform current graph-network models on small datasets. In particular, the vibrational entropy at 305 K of crystals is predicted with a mean absolute test error of 0.009 meV/K/atom (four times lower than previous studies). Furthermore, joint-learning reduces the test error compared to single-target learning and enables the prediction of multiple properties at once, such as temperature functions. Finally, the selection algorithm highlights the most important features and thus helps understanding the underlying physics., Comment: 5 pages, 2 figures

Boron phosphide has recently been identified as a potential high hole mobility transparent conducting material. This promise arises from its low hole effective masses. However, BP has a relatively small 2 eV indirect band gap which will affect its transparency. In this work, we computationally study both optical absorption across the indirect gap and phonon-limited electronic transport to quantify the potential of boron phosphide as a \emph{p}-type transparent conductor. We find that phonon-mediated indirect optical absorption is weak in the visible spectrum and that the phonon-limited hole mobility is very high (around 900 cm$^2$/Vs) at room temperature. This exceptional mobility comes from a combination of low hole effective mass and very weak scattering by polar phonon modes. We rationalize the weak scattering by the less ionic bonding in boron phosphide compared to oxides. We suggest this could be a general advantage of non-oxides for \emph{p}-type transparent conducting applications. Using our computed properties, we assess the transparent conductor figure of merit of boron phosphide and shows that it exceeds by one order of magnitude that of established \emph{p}-type transparent conductors, confirming the potential of this material.

We include the treatment of quadrupolar fields beyond the Fr\"ohlich interaction in the first-principles electron-phonon vertex in semiconductors. Such quadrupolar fields induce long-range interactions that have to be taken into account for accurate physical results. We apply our formalism to Si (nonpolar), GaAs, and GaP (polar) and demonstrate that electron mobilities show large errors if dynamical quadrupoles are not properly treated.

We describe a new approach to compute the electron-phonon self-energy and carrier mobilities in semiconductors. Our implementation does not require a localized basis set to interpolate the electron-phonon matrix elements, with the advantage that computations can be easily automated. Scattering potentials are interpolated on dense $\mathbf{q}$ meshes using Fourier transforms and ab initio models to describe the long-range potentials generated by dipoles and quadrupoles. To reduce significantly the computational cost, we take advantage of crystal symmetries and employ the linear tetrahedron method and double-grid integration schemes, in conjunction with filtering techniques in the Brillouin zone. We report results for the electron mobility in Si, GaAs, and GaP obtained with this new methodology.

The colours of metals have attracted the attention of humanity since ancient times, and coloured metals, in particular gold compounds, have been employed for tools and objects symbolizing the aesthetics of power. In this work we develop a comprehensive framework to obtain the reflectivity and colour of metals, and show that the trends in optical properties and the colours can be predicted by straightforward first-principles techniques based on standard approximations. We apply this to predict reflectivity and colour of several elemental metals and of different types of metallic compounds (intermetallics, solid solutions and heterogeneous alloys), considering mainly binary alloys based on noble metals. We validate the numerical approach through an extensive comparison with experimental data and the photorealistic rendering of known coloured metals.

Ab initio many-body perturbation theory within the $GW$ approximation is a Green's function formalism widely used in the calculation of quasiparticle excitation energies of solids. In what has become an increasingly standard approach, Kohn-Sham eigenenergies, generated from a DFT calculation with a strategically-chosen exchange correlation functional ``starting point'', are used to construct $G$ and $W$, and then perturbatively corrected by the resultant $GW$ self-energy. In practice, there are several ways to construct the $GW$ self-energy, and these can lead to variations in predicted quasiparticle energies. For example, for ZnO and TiO$_2$, reported $GW$ fundamental gaps can vary by more than 1 eV. In this work, we address the convergence and key approximations in contemporary $G_0W_0$ calculations, including frequency-integration schemes and the treatment of the Coulomb divergence in the exact-exchange term. We study several systems,and compare three different $GW$ codes: BerkeleyGW, Abinit and Yambo. We demonstrate, for the first time, that the same quasiparticle energies for systems in the condensed phase can be obtained with different codes, and we provide a comprehensive assessment of implementations of the $GW$ approximation.

High-performance $p$-type transparent conducting materials (TCMs) must exhibit a rare combination of properties including high mobility, transparency and $p$-type dopability. The development of high-mobility/conductivity $p$-type TCMs is necessary for many applications such as solar cells, or transparent electronic devices. Oxides have been traditionally considered as the most promising chemical space to dig out novel $p$-type TCMs. However, non-oxides might perform better than traditional $p$-type TCMs (oxides) in terms of mobility. We report on a high-throughput (HT) computational search for non-oxide $p$-type TCMs from a large dataset of more than 30,000 compounds which identified CaTe and Li$_\textrm{3}$Sb as very good candidates for high-mobility $p$-type TCMs. From our calculations, both compounds are expected to be $p$-type dopable: intrinsically for Li$_\textrm{3}$Sb while CaTe would require extrinsic doping. Using electron-phonon computations, we estimate hole mobilities at room-temperature to be about 20 and 70 cm$^2$/Vs for CaTe and Li$_\textrm{3}$Sb, respectively. The computed hole mobility for Li$_\textrm{3}$Sb is quite exceptional and comparable with the electron mobility in the best $n$-type TCMs., Comment: 10 pages, 5 figures

Transparent conducting oxides (TCOs) are essential to many technologies including solar cells and transparent electronics. The search for high performance n- or p-type TCOs has mainly focused on materials offering transport through band carriers instead of small polarons. In this work, we break this paradigm and demonstrate using well-known physical models that, in certain circumstances, TCOs exhibiting transport by small polarons offer a better combination of transparency and conductivity than materials conducting through band transport. We link this surprising finding to the fundamentally different physics of optical absorption for band and polaronic carriers. Our work rationalizes the good performances of recently emerging small-polaronic Cr-based p-type TCOs such as Sr-doped LaCrO$_3$ and outlines design principles for the development of high-performance TCOs based on transport by small polarons. This opens new avenues for the discovery of high-performance TCOs especially p-type.

Materials combining both a high refractive index and a wide band gap are of great interest for optoelectronic and sensor applications. However, these two properties are typically described by an inverse correlation with high refractive index appearing in small gap materials and vice-versa. Here, we conduct a first-principles high-throughput study on more than 4000 semiconductors (with a special focus on oxides). Our data confirm the general inverse trend between refractive index and band gap but interesting outliers are also identified. The data are then analyzed through a simple model involving two main descriptors: the average optical gap and the effective frequency. The former can be determined directly from the electronic structure of the compounds, but the latter cannot. This calls for further analysis in order to obtain a predictive model. Nonetheless, it turns out that the negative effect of a large band gap on the refractive index can counterbalanced in two ways: (i) by limiting the difference between the direct band gap and the average optical gap which can be realized by a narrow distribution in energy of the optical transitions and (ii) by increasing the effective frequency which can be achieved through either a high number of transitions from the top of the valence band to the bottom of the conduction or a high average probability for these transitions. Focusing on oxides, we use our data to investigate how the chemistry influences this inverse relationship and rationalize why certain classes of materials would perform better. Our findings can be used to search for new compounds in many optical applications both in the linear and non-linear regime (waveguides, optical modulators, laser, frequency converter, etc.)., Comment: The manuscript is made of 10 pages with 6 figures and 1 table. At the end of the main text, there is an appendix with 2 figures. Supplemental material includes 7 figures and 6 tables. In the db.csv file there are the materials properties we computed in this work

In the design of materials with low lattice thermal conductivity, compounds with high density, low speed of sound, and complexity at either the atomic, nano- or microstructural level are preferred. The layered compound Mg$_3$Sb$_2$ defies these prevailing paradigms, exhibiting lattice thermal conductivity comparable to PbTe and Bi$_2$Te$_3$, despite its low density and simple structure. The excellent thermoelectric performance ($zT$ $\sim$ 1.5) in $n$-type Mg$_3$Sb$_2$ has thus far been attributed to its multi-valley conduction band, while its anomalous thermal properties have been largely overlooked. To explain the origin of the low lattice thermal conductivity of Mg$_3$Sb$_2$, we have used both experimental methods and ab initio phonon calculations to investigate trends in the elasticity, thermal expansion and anharmonicity of $A$Mg$_2Pn_2$ Zintl compounds with $A$ = Mg, Ca, Yb, and $Pn$ = Sb and Bi. Phonon calculations within the quasi-harmonic approximation reveal large mode Gr\"uneisen parameters in Mg$_3$Sb$_2$ compared with isostructural compounds, in particular in transverse acoustic modes involving shearing of adjacent anionic layers. Measurements of the elastic moduli and sound velocity as a function of temperature using resonant ultrasound spectroscopy provide a window into the softening of the acoustic branches at high temperature, confirming their exceptionally high anharmonicity. We attribute the anomalous thermal behavior of Mg$_3$Sb$_2$ to the diminutive size of Mg, which may be too small for the octahedrally-coordinated site, leading to weak, unstable interlayer Mg-Sb bonding. This suggests more broadly that soft shear modes resulting from undersized cations provide a potential route to achieving low lattice thermal conductivity low-density, earth-abundant materials., Comment: 11 pages, 9 figures

Using density functional theory (DFT) and density functional perturbation theory (DFPT), we investigate the stability and response functions of CsH$_2$PO$_4$, a ferroelectric material at low temperature. This material cannot be described properly by the usual (semi-)local approximations within DFT. The long-range e$^-$-e$^-$ correlation needs to be properly taken into account, using, for instance, Grimme's DFT-D methods, as investigated in this work. We find that DFT-D3(BJ) performs the best for the members of the dihydrogenated alkali phosphate family (KH$_2$PO$_4$, RbH$_2$PO$_4$, CsH$_2$PO$_4$), leading to experimental lattice parameters reproduced with an average deviation of 0.5 %. With these DFT-D methods, the structural, dielectric, vibrational and mechanical properties of CsH$_2$PO$_4$ are globally in excellent agreement with the available experiments ($<$ 2% MAPE for Raman-active phonons). Our study suggests the possible existence of a new low-temperature phase for CsH$_2$PO$_4$, not yet reported experimentally. Finally, we report the implementation of DFT-D contributions to elastic constants within DFPT., Comment: This paper was published in Physical Review B the 25 January 2017 (21 pages, 4 figures)

In this paper, we present the results of a large-scale, high-throughput computational search for electrides among all known inorganic materials. Analyzing a database of density functional theory results on more than 60,000 compounds, we identify 69 new electride candidates. We report on all these candidates and discuss the structural and chemical factors leading to electride formation. Among these candidates, our work identifies the first partially-filled 3d transition metal containing electrides Ba3CrN3 and Sr3CrN3; an unexpected finding that contravenes conventional chemistry., Comment: 5 page manuscript in letter format, 27 page Supplementary Information