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Organic molecular crystals are expected to feature appreciable electron-phonon interactions that influence their electronic properties at zero and finite temperature. In this work, we report first-principles calculations and an analysis of the electron-phonon self-energy in naphthalene crystals. We compute the zero-point renormalization and temperature dependence of the fundamental band gap, and the resulting scattering lifetimes of electronic states near the valence- and conduction-band edges employing density functional theory. Further, our calculated phonon renormalization of the GW-corrected quasiparticle band structure predicts a fundamental band gap of 5 eV for naphthalene at room temperature, in good agreement with experiments. From our calculated phonon-induced electron lifetimes, we obtain the temperature-dependent mobilities of electrons and holes in good agreement with experimental measurements at room temperature. Finally, we show that an approximate energy self-consistent computational scheme for the electron-phonon self-energy leads to the prediction of strong satellite bands in the electronic band structure. We find that a single calculation of the self-energy can reproduce the self-consistent results of the band gap renormalization and electrical mobilities for naphthalene, provided that the on-the-mass-shell approximation is used, i.e., if the self-energy is evaluated at the bare eigenvalues.

We present a systematic study on the influence of the miscut orientation on structural and electronic properties in the homoepitaxial growth on off-oriented β-Ga2O3 (100) substrates by metalorganic chemical vapour phase epitaxy. Layers grown on (100) substrates with 6° miscut toward the [00 1 ¯ ] direction show high electron mobilities of about 90 cm2 V−1 s−1 at electron concentrations in the range of 1–2 × 1018 cm−3, while layers grown under identical conditions but with 6° miscut toward the [001] direction exhibit low electron mobilities of around 10 cm2 V−1 s−1. By using high-resolution scanning transmission electron microscopy and atomic force microscopy, we find significant differences in the surface morphologies of the substrates after annealing and of the layers in dependence on their miscut direction. While substrates with miscuts toward [00 1 ¯ ] exhibit monolayer steps terminated by ( 2 ¯ 01) facets, mainly bilayer steps are found for miscuts toward [001]. Epitaxial growth on both substrates occurs in step-flow mode. However, while layers on substrates with a miscut toward [00 1 ¯ ] are free of structural defects, those on substrates with a miscut toward [001] are completely twinned with respect to the substrate and show stacking mismatch boundaries. This twinning is promoted at step edges by transformation of the (001)-B facets into ( 2 ¯ 01) facets. Density functional theory calculations of stoichiometric low index surfaces show that the ( 2 ¯ 01) facet has the lowest surface energy following the (100) surface. We conclude that facet transformation at the step edges is driven by surface energy minimization for the two kinds of crystallographically inequivalent miscut orientations in the monoclinic lattice of β-Ga2O3. [ABSTRACT FROM AUTHOR]

Big data has ushered in a new wave of predictive power using machine-learning models. In this work, we assess what big means in the context of typical materials-science machine-learning problems. This concerns not only data volume, but also data quality and veracity as much as infrastructure issues. With selected examples, we ask (i) how models generalize to similar datasets, (ii) how high-quality datasets can be gathered from heterogenous sources, (iii) how the feature set and complexity of a model can affect expressivity, and (iv) what infrastructure requirements are needed to create larger datasets and train models on them. In sum, we find that big data present unique challenges along very different aspects that should serve to motivate further work.

Organic/inorganic hybrid systems offer great potential for novel solar cell design combining the tunability of organic chromophore absorption properties with high charge carrier mobilities of inorganic semiconductors. However, often such material combinations do not show the expected performance: while ZnO, for example, basically exhibits all necessary properties for a successful application in light-harvesting, it was clearly outpaced by TiO 2 in terms of charge separation efficiency. The origin of this deficiency has long been debated. This study employs femtosecond time-resolved photoelectron spectroscopy and many-body ab initio calculations to identify and quantify all elementary steps leading to the suppression of charge separation at an exemplary organic/ZnO interface. It is demonstrated that charge separation indeed occurs efficiently on ultrafast (350 fs) timescales, but that electrons are recaptured at the interface on a 100 ps timescale and subsequently trapped in a strongly bound (0.7 eV) hybrid exciton state with a lifetime exceeding 5 µs. Thus, initially successful charge separation is followed by delayed electron capture at the interface, leading to apparently low charge separation efficiencies. This finding provides a sufficiently large time frame for counter-measures in device design to successfully implement specifically ZnO and, moreover, invites material scientists to revisit charge separation in various kinds of previously discarded hybrid systems., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

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 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., Competing Interests: G. J. C. is a shareholder and Director of Intellegens Ltd. G.-M. R. is a shareholder and Chief Innovation Officer of Matgenix SRL. E. B. is a shareholder and Director of Materials Platform for Data Science OÜ., (This journal is © The Royal Society of Chemistry.)

The goal of our study was to identify signatures of selection in the Turopolje pigs and other commercial pig breeds. We conducted a comprehensive analysis of five datasets, including one local pig breed (Turopolje) and four commercial pig breeds (Large White, Landrace, Pietrain, and Duroc), using strict quality control measures. Our final dataset consisted of 485 individuals and 54,075 single nucleotide polymorphisms (SNPs). To detect selection signatures within these pig breeds, we utilized the XP-EHH and XP-nSL methodologies, which allowed us to identify candidate genes that have been subject to positive selection. Our analysis consistently highlighted the PTBP2 and DPYD genes as commonly targeted by selection in the Turopolje breed. DPYD is associated with muscular development in pigs and other species and PTBP2 emerges as one of the potential genes linked to seminal characteristics. Furthermore, in the Large White breed, a number of genes were detected with the two methods, such as ATP1A1 , CASQ2 , CD2 , IGSF3 , MAB21L3 , NHLH2 , SLC22A15 , VANGL1 . In the Duroc breed, a different set of genes was detected, such as ARSB , BHMT , BHMT2 , DMGDH , JMY . The function of these genes was related to body weight, production efficiency and meat quality, average daily gain, and other similar traits. Overall, our results have identified a number of genomic regions that are under selective pressure between local and commercial pig breeds. This information can help to improve our understanding of the mechanisms underlying pig breeding, and ultimately contribute to the development of more efficient and sustainable pig production practices. Our study highlights the power of using multiple genomic methodologies to detect genetic signatures of selection, and provides important insights into the genetic diversity of pig breeds.

The identification of the stable phases in alloy materials is challenging because composition affects the structural stability of different intermediate phases. Computational simulation, via multiscale modelling approaches, can significantly accelerate the exploration of phase space and help to identify stable phases. Here, we apply such new approaches to understand the complex phase diagram of binary alloys of PdZn, with the relative stability of structural polymorphs considered through application of density functional theory coupled with cluster expansion (CE). The experimental phase diagram has several competing crystal structures, and we focus on three different closed-packed phases that are commonly observed for PdZn, namely the face-centred cubic (FCC), body-centred tetragonal (BCT) and hexagonal close packed (HCP), to identify their respective stability ranges. Our multiscale approach confirms a narrow range of stability for the BCT mixed alloy, within the Zn concentration range from 43.75% to 50%, which aligns with experimental observations. We subsequently use CE to show that the phases are competitive across all concentrations, but with the FCC alloy phase favoured for Zn concentrations below 43.75%, and that the HCP structure favoured for Zn-rich concentrations. Our methodology and results provide a platform for future investigations of PdZn and other close-packed alloy systems with multiscale modelling techniques., (Creative Commons Attribution license.)

In the quest for efficient thermoelectrics, semiconducting behavior is a targeted property. Yet, this is often difficult to achieve due to the complex interplay between electronic structure, temperature, and disorder. We find this to be the case for the thermoelectric clathrate Ba_{8}Al_{16}Si_{30}: Although this material exhibits a band gap in its ground state, a temperature-driven partial order-disorder transition leads to its effective closing. This finding is enabled by a novel approach to calculate the temperature-dependent effective band structure of alloys. Our method fully accounts for the effects of short-range order and can be applied to complex alloys with many atoms in the primitive cell, without relying on effective medium approximations.

The emerging interest in two-dimensional electron gases (2DEGs), formed at interfaces between two insulating oxide perovskites, poses a crucial fundamental question in view of future electronic devices. In the framework of density-functional theory, we investigate the possibility to control the characteristics of the 2DEG formed at the LaInO 3 /BaSnO 3 interface by including a ferroelectric layer. To do so, we consider BaTiO 3 as a prototype example and examine how the orientation of the ferroelectric polarization impacts density and confinement of the 2DEG. We find that aligning the ferroelectric polarization toward (outward) the LaInO 3 /BaSnO 3 interface leads to an accumulation (depletion) of the interfacial 2DEG. Varying its magnitude, we find a linear effect on the 2DEG charge density that is confined within the BaSnO 3 side. Analysis of the optimized geometries reveals that inclusion of the ferroelectric layer makes structural distortions at the LaInO 3 /BaSnO 3 junction less pronounced, which, in turn, enhances the 2DEG density. Thicker ferroelectric layers allow for reaching higher polarization magnitude. We discuss the mechanisms behind all these findings and rationalize how the characteristics of both 2DEGs and 2D hole gases can be controlled in the considered heterostructures. Overall, our results can be generalized to other combinations of ferroelectric, polar, and nonpolar materials.

In this paper, the history, present status, and future of density-functional theory (DFT) is informally reviewed and discussed by 70 workers in the field, including molecular scientists, materials scientists, method developers and practitioners. The format of the paper is that of a roundtable discussion, in which the participants express and exchange views on DFT in the form of 302 individual contributions, formulated as responses to a preset list of 26 questions. Supported by a bibliography of 777 entries, the paper represents a broad snapshot of DFT, anno 2022.

We develop a materials descriptor based on the electronic density-of-states (DOS) and investigate the similarity of materials based on it. As an application example, we study the Computational 2D Materials Database (C2DB) that hosts thousands of two-dimensional materials with their properties calculated by density-functional theory. Combining our descriptor with a clustering algorithm, we identify groups of materials with similar electronic structure. We introduce additional descriptors to characterize these clusters in terms of crystal structures, atomic compositions, and electronic configurations of their members. This allows us to rationalize the found (dis)similarities and to perform an automated exploratory and confirmatory analysis of the C2DB data. From this analysis, we find that the majority of clusters consist of isoelectronic materials sharing crystal symmetry, but we also identify outliers, i.e., materials whose similarity cannot be explained in this way., (© 2022. The Author(s).)

We present a formalism for the resonant inelastic X-ray scattering (RIXS) cross section. The resulting compact expression in terms of polarizability matrix elements, particularly lends itself to the implementation in an all-electron many-body perturbation theory (MBPT) framework, which is realized in the full-potential package exciting . With the carbon K edge RIXS of diamond and the oxygen K edge RIXS of β-Ga 2 O 3 , respectively, we demonstrate the importance of electron-hole correlation and atomic coherence in the RIXS spectra.

By enabling control of valley degrees of freedom in transition-metal dichalcogenides, valley-selective circular dichroism has become a key concept in valleytronics. Herein, we show that valley excitons, bound electron-hole pairs formed at the K or K̅ valleys upon absorption of circularly polarized light, are chiral quasiparticles characterized by a finite orbital angular momentum (OAM). We further formulate an ab initio many-body theory of valley-selective circular dichroism and valley excitons based on the Bethe-Salpeter equation. Besides governing the interaction with circularly polarized light, the OAM confers upon excitons a finite magnetization that manifests itself through an excitonic Zeeman splitting upon interaction with external magnetic fields. The good agreement between our ab initio calculations and recent experimental measurements of the exciton Zeeman shifts corroborates this picture, indicating that valley excitons can carry angular momentum even in their singlet state.

The prosperity and lifestyle of our society are very much governed by achievements in condensed matter physics, chemistry and materials science, because new products for sectors such as energy, the environment, health, mobility and information technology (IT) rely largely on improved or even new materials. Examples include solid-state lighting, touchscreens, batteries, implants, drug delivery and many more. The enormous amount of research data produced every day in these fields represents a gold mine of the twenty-first century. This gold mine is, however, of little value if these data are not comprehensively characterized and made available. How can we refine this feedstock; that is, turn data into knowledge and value? For this, a FAIR (findable, accessible, interoperable and reusable) data infrastructure is a must. Only then can data be readily shared and explored using data analytics and artificial intelligence (AI) methods. Making data 'findable and AI ready' (a forward-looking interpretation of the acronym) will change the way in which science is carried out today. In this Perspective, we discuss how we can prepare to make this happen for the field of materials science., (© 2022. Springer Nature Limited.)

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., (© 2021. The Author(s).)

We combine ultrafast electron diffuse scattering experiments and first-principles calculations of the coupled electron-phonon dynamics to provide a detailed momentum-resolved picture of lattice thermalization in black phosphorus. The measurements reveal the emergence of highly anisotropic nonthermal phonon populations persisting for several picoseconds after exciting the electrons with a light pulse. Ultrafast dynamics simulations based on the time-dependent Boltzmann formalism are supplemented by calculations of the structure factor, defining an approach to reproduce the experimental signatures of nonequilibrium structural dynamics. The combination of experiments and theory enables us to identify highly anisotropic electron-phonon scattering processes as the primary driving force of the nonequilibrium lattice dynamics in black phosphorus. Our approach paves the way toward unravelling and controlling microscopic energy flows in two-dimensional materials and van der Waals heterostructures, and may be extended to other nonequilibrium phenomena involving coupled electron-phonon dynamics such as superconductivity, phase transitions, or polaron physics.

While control over chemical reactions is largely achieved by altering the intrinsic properties of catalysts, novel strategies are constantly being proposed to improve the catalytic performance in an extrinsic way. Since the fundamental chemical behavior of molecules can remarkably change when their molecular scale is comparable to the size of the space where they are located, creating spatially confined environments around the active sites offers new means of regulating the catalytic processes. We demonstrate through first-principles calculations that acetylene hydrogenation can exhibit significantly improved selectivity within the confined sub-nanospace between two-dimensional (2D) monolayers and the Pd(111) substrate. Upon intercalation of molecules, the lifting and undulation of a 2D monolayer on Pd(111) influence the adsorption energies of intermediates to varying extents, which, in turn, changes the energy profiles of the hydrogenation reactions. Within the confined sub-nanospace, the formation of ethane is always unfavorable, demonstrating effective suppression of the unwanted overhydrogenation. Moreover, the catalytic properties can be further tuned by altering the coverage of the adsorbates as well as strains within the 2D monolayer. Our results also indicate that for improving the selectivity, the strategy of spatial confinement could not be combined with that of single-atom catalysis, since the reactant molecules cannot enter the sub-nanospace due to the too weak adsorbate-substrate interaction. This work sheds new light on designing novel catalysts with extraordinary performance for the selective hydrogenation of acetylene.