en:
Materials that can host Majorana zero modes gained a lot of attention in recent years due to the possibility to engineer topologically protected quantum computing platforms. Promising candidates are heterostructures of topological insulators and superconductors. Here we present density-functional-theory-based calculations for Pd-doped Bi₂Te₃ and Pd(Bi,Te)x (x=1,2) in order to shed light on the superconducting properties in the self-formed superconducting phase when Pd is deposited on top of the topological insulator Bi₂Te₃.
This dataset accompanies a joint experiment/theory publication and publishes the related density functional theory calculations for:
- relaxed geometries for Pd intercalation in the Bi₂Te₃ vdW gap
- electronic structure of PdTe and PdTe₂ compared to alloy phases of Pd(Bi,Te) and Pd(Bi,Te)₂, collectively referred to as "xPBT"
- calculations for the superconducting state of xPBT phases within the Kohn-Sham Bogoliubov-de Gennes method

en: Pd-doping of Bi₂Te₃ and superconductivity of Pd(Bi,Te)x from density functional theory

materialscloud

en: Materials Cloud

density-functional-theory

display_name: density-functional-theory

majorana

display_name: majorana

superconductivity

display_name: superconductivity

topological-materials

display_name: topological-materials


$oid: OBSOLETE

en: On-surface synthesis has emerged as a powerful strategy to fabricate unprecedented forms of atomically precise graphene nanoribbons (GNRs). However, the on-surface synthesis of zigzag GNRs (ZGNR) has met with only limited success. In the paper where the data are discussed, we report the synthesis and on-surface reactions of 2,7-dibromo-9,9'-bianthryl as the precursor towards π-extended ZGNRs. Characterization by scanning tunneling microscopy and high-resolution noncontact atomic force microscopy clearly demonstrated the formation of anthracene-fused ZGNRs. Unique skeletal rearrangements were also observed, which could be explained by intramolecular Diels-Alder cycloaddition. Theoretical calculations of the electronic properties of the anthracene-fused ZGNRs revealed spin-polarized edge-states and a narrow bandgap of 0.20 eV.

en: On-surface synthesis of anthracene-fused zigzag graphene nanoribbons from 2,7-dibromo-9,9'-bianthryl reveals unexpected ring rearrangements

materialscloud

approval_status: approved

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

graphene-nanoribbons

display_name: graphene-nanoribbons

marvel

display_name: marvel

on-surface-synthesis

display_name: on-surface-synthesis


en:
Hubbard-corrected density-functional theory has proven to be successful in addressing self-interaction errors in 3D magnetic materials. However, the effectiveness of this approach for 2D magnetic materials has not been extensively explored. Here, we use PBEsol+U and its extensions PBEsol+U+V to investigate the electronic, structural, and vibrational properties of 2D antiferromagnetic FePS₃ and ferromagnetic CrI₃, and compare the monolayers with their bulk counterparts. Hubbard parameters (on-site U and inter-site V) are computed self-consistently using density-functional perturbation theory, thus avoiding any empirical assumptions. We show that for FePS₃ the Hubbard corrections are crucial in obtaining the experimentally observed insulating state with the correct crystal symmetry, providing also vibrational frequencies in good agreement with Raman experiments. While empirical U can lead to an unstable ground-state (i.e. imaginary phonons), the system remains stable through the self-consistent process of calculating Hubbard parameters.
For ferromagnetic CrI₃, we discuss how a straightforward application of Hubbard corrections worsens the results and introduces a spurious separation between spin-majority and minority conduction bands. Promoting the Hubbard U to be a spin-resolved parameter — that is, applying different (first-principles) values to the spin-up and spin-down manifolds — recovers a more physical picture of the electronic bands and delivers the best comparison with experiments.

en: On-site and inter-site Hubbard corrections in magnetic monolayers: The case of FePS₃ and CrI₃

cscs

display_name: cscs

dft

display_name: dft

dftu

display_name: dftu

dftuv

display_name: dftuv

epfl

display_name: epfl

first-principles

display_name: first-principles

hubbard

display_name: hubbard

quantum-espresso

display_name: quantum-espresso


en: Neural networks (NNs) often assign high confidence to their predictions, even for points far out-of-distribution, making uncertainty quantification (UQ) a challenge. When they are employed to model interatomic potentials in materials systems, this problem leads to unphysical structures that disrupt simulations, or to biased statistics and dynamics that do not reflect the true physics. Differentiable UQ techniques can find new informative data and drive active learning loops for robust potentials. However, a variety of UQ techniques, including newly developed ones, exist for atomistic simulations and there are no clear guidelines for which are most effective or suitable for a given case. In this work, we examine multiple UQ schemes for improving the robustness of NN interatomic potentials (NNIPs) through active learning. In particular, we compare incumbent ensemble-based methods against strategies that use single, deterministic NNs: mean-variance estimation, deep evidential regression, and Gaussian mixture models. We explore three datasets ranging from in-domain interpolative learning to more extrapolative out-of-domain generalization challenges: rMD17, ammonia inversion, and bulk silica glass. Performance is measured across multiple metrics relating model error to uncertainty. Our experiments show that none of the methods consistently outperformed each other across the various metrics. Ensembling remained better at generalization and for NNIP robustness;  MVE only proved effective for in-domain interpolation, while GMM was better out-of-domain; and evidential regression, despite its promise, was not the preferable alternative in any of the cases. More broadly, cost-effective, single deterministic models cannot yet consistently match or outperform ensembling for uncertainty quantification in NNIPs.

en: Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles

adversarial-sampling

display_name: adversarial-sampling

ammonia

display_name: ammonia

neural-network-interatomic-potentials

display_name: neural-network-interatomic-potentials

silica-glass

display_name: silica-glass

single-deterministic-neural-networks

display_name: single-deterministic-neural-networks

uncertainty-quantification

display_name: uncertainty-quantification


en: Oxides containing metals or semimetals from the p-block of the periodic table, e.g., indium oxide or antimony oxide, are of interest as transparent conductors and light absorbers for solar energy conversion due to the tunability of their electronic conductivity and optical absorption. Comparatively, these oxides have found limited applications in solar-to-hydrogen photocatalysis primarily due to their high electronegativity, which impedes electron transfer for converting protons into hydrogen. We have shown recently that inserting s-block metal cations into p-block oxides is effective at lowering electronegativities while affording further control of band gaps. Here, we explain the origins of this dual tunability by demonstrating the mediator role of s-block metal cations in modulating orbital hybridization while not contributing to frontier electronic states. From this result, we carry out a comprehensive computational study of 109 ternary oxides of s- and p-block metal elements as candidate photocatalysts for solar hydrogen generation. We downselect the most desirable materials using band gaps and band edges obtained from Hubbard-corrected density-functional theory with Hubbard parameters computed entirely from first principles, evaluate the stability of these oxides in aqueous conditions, and characterize experimentally four of the remaining materials, synthesized with high phase uniformity, to critically assess the accuracy of the computational models. We thus propose nine oxide semiconductors, including CsIn₃O₅, Sr₂In₂O₅, and KSbO₂ which, to the extent of our literature review, have not been previously considered as water-splitting photocatalysts. This record contains the data for the simulations discussed in our manuscript.

en: Ternary oxides of s- and p-block metals for photocatalytic solar-to-hydrogen conversion

photocatalysis

display_name: photocatalysis


en: We study the electronic and optical properties of the hydrogen boride sheet by using the many-body perturbation theory with the perturbative GW (G₀W₀) approximation. It was found that the hydrogen boride sheet shows a semimetallic electronic structure, supporting the previous theoretical study based on the semilocal density functional theory calculations. It was also found that the optical spectrum calculated based on the quasiparticle energies agrees well with the experiments. This work suggests that G₀W₀ approximation may be useful for predicting precise electronic and optical properties of the hydrogen boride sheet and its derivatives.

en: Electronic and optical properties of the hydrogen boride sheet from the many-body perturbation theory

density-functional-theory

display_name: density-functional-theory

hydrogen-boride

display_name: hydrogen-boride

many-body-perturbation-theory

display_name: many-body-perturbation-theory

nanosheet

display_name: nanosheet

two-dimensional

display_name: two-dimensional


en: One key bottleneck of solid-state NMR spectroscopy is that ¹H NMR spectra of organic solids are often very broad due to the presence of a strong network of dipolar couplings. We have recently suggested a new approach to tackle this problem. More specifically, we parametrically mapped errors leading to residual dipolar broadening into a second dimension and removed them in a correlation experiment. In this way pure isotropic proton (PIP) spectra were obtained that contain only isotropic shifts and provide the highest ¹H NMR resolution available today in rigid solids. Here, using a deep-learning method, we extend the PIP approach to a second dimension, and for samples of L-tyrosine hydrochloride and ampicillin we obtain high resolution ¹H-¹H double-quantum/single-quantum dipolar correlation and spin-diffusion spectra with significantly higher resolution than the corresponding spectra at 100 kHz MAS, allowing the identification of previously overlapped isotropic correlation peaks.

en: Two-dimensional pure isotropic proton solid state NMR

machine-learning

display_name: machine-learning

nmr

display_name: nmr

resolution

display_name: resolution


en: Existing, classical interatomic potentials for bcc iron predict contradicting crack-tip mechanisms (i.e. cleavage, dislocation emission, phase transition) for the same crack systems, thus leaving the crack propagation mechanism in bcc iron unclear. In this work, we develop a Gaussian approximation potential (GAP) by extending a DFT database for ferromagnetic bcc iron to include highly distorted primitive bcc cells and surface separation, along with small crack-tip configurations that are identified by means of a fully automated active learning workflow. Our GAP (referred to as Fe-GAP22) predicts crack propagation within 8 meV/atom accuracy. The fully automated, active learning workflow is made publicly available on GitHub. With the newly developed Fe-GAP22, we find that in absence of other defects around the crack tip (e.g. nanovoids, dislocations), the static (T=0K) crack-tip mechanism is cleavage, thus settling the contradictions in the literature. Our work also highlights the need for multi-scale modelling to predict fracture at finite temperatures and finite strain rates.

en: Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential

bcc-iron

display_name: bcc-iron

fracture

display_name: fracture

gaussian-approximation-potential

display_name: gaussian-approximation-potential


en: AuNi is a classic long-studied fcc alloy combining a very "large" atom (Au) and a very "small" atom (Ni), and the large atomic size misfits suggest very high strengthening. Here, AuNi is used as a model alloy for the testing of new strengthening theories in random alloys that include the effects of both size misfits and solute–solute interactions. Experimentally, AuNi samples are fabricated, characterized, and tested, and show no segregation after annealing at 900 °C and a very high yield strength of 769 MPa. Theoretically, the main inputs to the theory (alloy lattice and elastic constants, solute misfit volumes, energy fluctuations associated with slip in the presence of solute–solute interactions) are extracted from experiments or computed using first-principles DFT. The parameter-free prediction of the yield strength is 809 MPa, in very good agreement with experiments. Solute–solute interactions enhance the strength only moderately (13%), demonstrating that the strengthening is dominated by the solute misfit contribution. Various aspects of the full theory are discussed, the general methodology is presented in an easy-to-apply analytic framework, and a new analysis for strengthening in alloys with zero misfits but non-zero solute–solute interactions is presented. These results provide support for the theories and point toward applications to many fcc complex concentrated alloys.

en: Solute misfit and solute interaction effects on strengthening: A case study in AuNi

materialscloud

en: Materials Cloud

auni

display_name: auni

marvel

display_name: marvel

misfit-volume

display_name: misfit-volume

solute-strengthening-theory

display_name: solute-strengthening-theory

stacking-fault-energy

display_name: stacking-fault-energy

yield-strength

display_name: yield-strength


en: The temperature-dependent properties of the aqueous electron have been extensively studied using mixed quantum-classical simulations in a wide range of thermodynamic conditions based on one-electron pseudopotentials. While the cavity model appears to explain most of the physical properties of the aqueous electron, only a non-cavity model has so far been successful in accounting for the temperature dependence of the absorption spectrum. Here, we present an accurate and efficient description of the aqueous electron under various thermodynamic conditions by combining hybrid functional-based molecular dynamics, machine learning techniques, and multiple time-step methods. Our advanced simulations accurately describe the temperature dependence of the absorption maximum in the presence of cavity formation. Specifically, our work reveals that the red shift of the absorption maximum results from an increasing gyration radius with temperature, rather than from global density variations as previously suggested.

en: Temperature dependent properties of the aqueous electron

materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

ab-initio-molecular-dynamics

display_name: ab-initio-molecular-dynamics

aqueous-electron

display_name: aqueous-electron

hybrid-functional

display_name: hybrid-functional


en: The GW approach produces highly accurate quasiparticle energies, but its application to large systems is computationally challenging, which can be largely attributed to the difficulty in computing the inverse dielectric matrix. To address this challenge, we develop a machine learning approach to efficiently predict density-density response functions (DDRF) in materials. For this, an atomic decomposition of the DDRF is introduced as well as the neighbourhood density-matrix descriptor both of which transform in the same way under rotations. The resulting DDRFs are then used to evaluate quasiparticle energies via the GW approach. This technique is called the ML-GW approach. To assess the accuracy of this method, we apply it to hydrogenated silicon clusters and find that it reliably reproduces HOMO-LUMO gaps and quasiparticle energy levels. The accuracy of the predictions deteriorates when the approach is applied to larger clusters than those included in the training set. These advances pave the way towards GW calculations of complex systems, such as disordered materials, liquids, interfaces and nanoparticles.

en: Accelerating GW calculations through machine learned dielectric matrices

envi

en: Environment

ab-initio

display_name: ab-initio

dft

display_name: dft


en: Topological data analysis based on persistent homology has been applied to the molecular dynamics simulation for the fast ion-conducting phase (α-phase) of AgI, to show its effectiveness on the ion-migration mechanism analysis. Time-averaged persistence diagrams of α-AgI, which quantitatively records the shape and size of the ring structures in the given atomic configurations, clearly showed the emergence of the four-membered rings formed by two Ag and two I ions at high temperatures. They were identified as common structures during the Ag ion migration. The averaged potential energy change due to the deformation of four-membered ring agrees well with the activation energy calculated from the conductivity Arrhenius plot. The concerted motion of two Ag ions via the four-membered ring was also successfully extracted from molecular dynamics simulations by our approach, providing the new insight into the specific mechanism of the concerted motion.

en: Topological data analysis of superionic conductor silver iodide

ion-migration

display_name: ion-migration

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

persistence-diagram

display_name: persistence-diagram


en: We present an approach for the calculation of the Z2 topological invariant in non-crystalline two-dimensional quantum spin Hall insulators. While topological invariants were originally mathematically introduced for crystalline periodic systems, and crucially hinge on tracking the evolution of occupied states through the Brillouin zone, the introduction of disorder or dynamical effects can break the translational symmetry and imply the use of larger simulation cells, where the k-point sampling is typically reduced to the single Γ-point. Here, we introduce a single-point formula for the spin Chern number that enables to adopt the supercell framework, where a single Hamiltonian diagonalisation is performed. Our single-point approach allows to calculate the spin Chern number even when the spin operator does not commute with the Hamiltonian, as in the presence of Rashba spin-orbit coupling. This archive entry contains the results of the single-point calculations of the topological invariant on the tight-binding Kane-Mele model for large supercells (up to 7200 sites). Convergence tests as function of the supercell size are reported, both in the pristine case and in presence of Anderson disorder. Single-point calculations of the spin Chern number are carried out over the entire phase diagram of the Kane-Mele model. The study of topological phase transitions due to disorder is performed in terms of single-point spin Chern number by averaging over several realisations of the Anderson disorder in the supercell.

en: Single-point spin Chern number in a supercell framework

anderson-disorder

display_name: anderson-disorder

kane-mele-model

display_name: kane-mele-model

spin-chern-number

display_name: spin-chern-number

supercell-framework

display_name: supercell-framework

topological-insulators

display_name: topological-insulators


en: Recent neutron-diffraction experiments in honeycomb CrI₃ quasi-2D ferromagnets have evinced the existence of a gap at the Dirac point in their spin-wave spectra. The existence of this gap has been attributed to strong in-plane Dzyaloshinskii-Moriya or Kitaev (DM/K) interactions and suggested to set the stage for topologically protected edge states to sustain non-dissipative spin transport. We perform state-of-the-art simulations of the spin-wave spectra in monolayer CrI₃, based on time-dependent density-functional perturbation theory (TDDFpT) and fully accounting for spin-orbit couplings (SOC) from which DM/K interactions ultimately stem. While our results are in qualitative agreement with experiments, the computed TDDFpT magnon gap at the Dirac point is found to be 0.47 meV, roughly six times smaller than the most recent experimental estimates, so questioning that intralayer anisotropies alone can explain the observed gap. Lattice-dynamical calculations, performed within density-functional perturbation theory (DFpT), indicate that a substantial degeneracy and a strong coupling between vibrational and magnetic excitations exist in this system, providing a possible additional gap-opening mechanism in the spin-wave spectra. To pursue this path, we introduce an interacting magnon-phonon Hamiltonian featuring a linear coupling between lattice and spin fluctuations, enabled by the magnetic anisotropy induced by SOC. Upon determination of the relevant interaction constants by DFpT and supercell calculations, this model allows us to propose magnon-phonon interactions as an important microscopic mechanism responsible for the enhancement of the gap in the range of ≈4meV around the Dirac point of the CrI₃ monolayer.

en: Magnon-phonon interactions enhance the gap at the Dirac point in the spin-wave spectra of CrI₃ two-dimensional magnets

dfpt

display_name: dfpt

magnon-phonon-coupling

display_name: magnon-phonon-coupling

magnons

display_name: magnons

marvel

display_name: marvel

phonons

display_name: phonons

quantum-espresso

display_name: quantum-espresso

tddft

display_name: tddft


en: We present an inexpensive first-principles approach for describing quantum paraelectricity that combines density functional theory (DFT) treatment of the electronic subsystem with quantum mechanical treatment of the ions through solution of the single-particle Schrödinger equation with the DFT-calculated potential. Using BaTiO3, SrTiO3, and KTaO3 as model systems, we show that the approach can straightforwardly distinguish between ferroelectric, paraelectric, and quantum paraelectric materials, based on simple quantities extracted from standard density functional and density functional perturbation theories. We calculate the influence of isotope substitution and strain on quantum paraelectric behavior and find that, while complete replacement of oxygen-16 by oxygen-18 has a surprisingly small effect, experimentally accessible strains can induce large changes. Finally, we collect the various choices for the phonon mass that have been introduced in the literature. We ide tify those that are most physically meaningful by comparing them with our results that avoid such a choice through the use of mass-weighted coordinates.

en: Ferroelectric, quantum paraelectric, or paraelectric? Calculating the evolution from BaTiO3 to SrTiO3 to KTaO3 using a single-particle quantum mechanical description of the ions

ferroelectricity

display_name: ferroelectricity

quantum-paraelectricity

display_name: quantum-paraelectricity


en: Metal surfaces have long been known to reconstruct, significantly influencing their structural and catalytic properties. Many key mechanistic aspects of these subtle transformations remain poorly understood due to limitations of previous simulation approaches. Using active learning of Bayesian machine-learned force fields trained from ab initio calculations, we enable large-scale molecular dynamics simulations to describe the thermodynamics and time evolution of the low-index mesoscopic surface reconstructions of Au (e.g., the Au(111)-`Herringbone,' Au(110)-(1x2)-`Missing-Row,' and Au(100)-`Quasi-Hexagonal' reconstructions). This capability yields direct atomistic understanding of the dynamic emergence of these surface states from their initial facets, providing previously inaccessible information such as nucleation kinetics and a complete mechanistic interpretation of reconstruction under the effects of strain and local deviations from the original stoichiometry. We successfully reproduce previous experimental observations of reconstructions on pristine surfaces and provide quantitative predictions of the emergence of spinodal decomposition and localized reconstruction in response to strain at non-ideal stoichiometries. A unified mechanistic explanation is presented of the kinetic and thermodynamic factors driving surface reconstruction. Furthermore, we study surface reconstructions on Au nanoparticles, where characteristic (111) and (100) reconstructions spontaneously appear on a variety of high-symmetry particle morphologies. The training data, MLFF, and subsequent simulations are provided for reproducibility.

en: Low-index mesoscopic surface reconstructions of Au surfaces using Bayesian force fields

density-functional-theory

display_name: density-functional-theory

machine-learning

display_name: machine-learning


en: Cobalt-based oxides are active electrocatalysts for the oxygen evolution reaction (OER) in alkaline electrolytes. However, their OER activity at near-neutral pHs is generally low and more active OER catalysts at neutral pHs are desired for practical applications. Herein, La0.2Sr0.8CoO3–δ, La0.2Sr0.8Co0.8Fe0.2O3–δ, La0.5Sr1.5CoO4–δ and CoOx catalysts were prepared by the flame spray synthesis method and their surface was functionalized by a dry phosphate incorporation process. Small amount of P was incorporated on the surface of the catalyst (in the range of ppm). All the samples were characterized by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and soft and hard X-ray absorption spectroscopy (XAS) before and after the functionalization process. The electrocatalytic activity towards OER of the different nanoparticle catalysts was investigated in electrolytes with different pH values (pH = 7, 12.5, 13, and 13.5).

en: The role of phosphate functionalization on the oxygen evolution reaction activity of cobalt-based oxides at different pH values

materialscloud

en: Materials Cloud

alkaline-water-electrolysis

display_name: alkaline-water-electrolysis

experimental

display_name: experimental

neutral-ph

display_name: neutral-ph

phosphate-ion-functionalization

display_name: phosphate-ion-functionalization


en: Fracture dictates the service limits of metallic structures. Damage tolerance of materials may be characterized by fracture toughness rigorously developed from fracture mechanics, or less rigorous yet more easily obtained impact toughness (or impact energy as a variant). Given the promise of high-entropy alloys (HEAs) in structural and damage-tolerance applications, we compiled a dataset of fracture toughness and impact toughness/energy from the literature till mid-2022. The dataset is subdivided into three categories, i.e., fracture toughness, impact toughness, and impact energy, which contain 148, 14, and 78 distinct data records, respectively. On top of the alloy chemistry and measured fracture quantities, each data record also records the factors influential to fracture. Examples are material processing history, phase structure, grain size, uniaxial tensile properties such as yield strength and elongation, and testing conditions.

en: Dataset for fracture and impact toughness of high-entropy alloys

materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

fracture-toughness

display_name: fracture-toughness

high-entropy-alloys

display_name: high-entropy-alloys

impact-energy

display_name: impact-energy

impact-toughness

display_name: impact-toughness


en: Magnet/superconductor hybrids (MSHs) hold the promise to host emergent topological superconducting phases. Both one-dimensional (1D) and two-dimensional (2D) magnetic systems in proximity to s-wave superconductors have shown evidence of gapped topological superconductivity with zero-energy end states and chiral edge modes. Recently, it was proposed that the bulk transition-metal dichalcogenide 4Hb-TaS2 is a gapless topological nodal-point superconductor (TNPSC). However, there has been no experimental realization of a TNPSC in a MSH system yet. In our work we present the discovery of TNPSC in antiferromagnetic (AFM) monolayers on top of an s-wave superconductor. Our calculations show that the topological phase is driven by the AFM order, resulting in the emergence of a gapless time-reversal invariant topological superconducting state. Using low-temperature scanning tunneling microscopy we observe a low-energy edge mode, which separates the topological phase from the trivial one, at the boundaries of antiferromagnetic islands. As predicted by the calculations, we find that the relative spectral weight of the edge mode depends on the edge's atomic configuration. Our results establish the combination of antiferromagnetism and superconductivity as a novel route to design 2D topological quantum phases. This record contains tight binding calculations results and a combination of STM images and tunneling spectroscopy data supporting our work.

en: Antiferromagnetism-driven two-dimensional topological nodal-point superconductivity

envi

en: Environment

nodal-point-superconductivity

display_name: nodal-point-superconductivity

scaning-tunneling-microscopy

display_name: scaning-tunneling-microscopy

topological-quantum-materials

display_name: topological-quantum-materials


en: The search for new high-temperature alloys that can enable higher-efficiency/lower-emissions power generation has accelerated with the discovery of body-centered cubic (bcc) refractory High Entropy Alloys (HEAs). These many-component, non-dilute alloys in the Cr-Mo-W-V-Nb-Ta-Ti-Zr-Hf-Al family hold the potential for combining high strength and thermodynamic stability at high temperature with low density and room-temperature ductility, but searching the immense compositional space is daunting. Here, very recent theories and expanded thermodynamic tools are used to guide the discovery of new alloys satisfying the required suite of properties. We present the dataset we generated in search for such alloys, including 5-component equicomposition alloys, as well as new quinary and quarternary alloys in the Hf-Mo-Nb-Ta-Ti space having even better overall properties (high strength, high strength retention, good ductility, light weight and single phase).

en: Theory-guided design of high-strength, high-melting point, ductile, low-density, single-phase BCC high entropy alloys

epfl

display_name: epfl

marvel

display_name: marvel

materials-selection

display_name: materials-selection


en: Halide perovskites (HPs) are widely viewed as promising photovoltaic and light-emitting materials for their suitable band gaps in the visible spectrum. Density functional theory (DFT) calculations employing (semi)local exchange-correlation functionals usually underestimate the band gaps for these systems. Accurate descriptions of the electronic structures of HPs often demand higher-order levels of theory such as the Heyd-Scuseria-Ernzerhof (HSE) hybrid density functional and GW approximations that are much more computationally expensive than standard DFT. Here, we investigate three representative types of HPs, ABX3 halide perovskites, vacancy-ordered double perovskites (VODPs), and bond disproportionated halide perovskites (BDHPs), using DFT+U+V with onsite U and intersite V Hubbard parameters computed self-consistently without a priori assumption. The inclusion of Hubbard corrections improves the band gap prediction accuracy for all three types of HPs to a similar level of advanced methods. Moreover, the self-consistent Hubbard U is a meaningful indicator of the true local charge state of multivalence metal atoms in HPs. The inclusion of the intersite Hubbard V is crucial to properly capture the hybridization between valence electrons on neighboring atoms in BDHPs that have breathing-mode distortions of halide octahedra. In particular, the simultaneous convergence of both Hubbard parameters and crystal geometry enables a band gap prediction accuracy superior to HSE for BDHPs but at a fraction of the cost. Our work highlights the importance of using self-consistent Hubbard parameters when dealing with HPs that often possess intricate competitions between onsite localization and intersite hybridization.

en: Onsite and intersite electronic correlations in the Hubbard model for halide perovskites

band-gap

display_name: band-gap

dftuv

display_name: dftuv

halide-perovskite

display_name: halide-perovskite


en: We present a potential explanation for the strain-induced metal-insulator transition that has recently been observed in thin films of SrCrO₃ using density-functional theory (DFT) and its extension to DFT+U. Our calculations show that the unstrained system exhibits a C-type antiferromagnetically ordered ground state, which is near to a Jahn-Teller instability, given realistic values of the Hubbard U parameter. However, the significant energy overlap between the higher-lying dxy band and the dxz/dyz band works against the JT distortion's. When epitaxially strained, this overlap is reduced by the lowering of the dxyband relative to dxz/dyz as the system gets closer to the nominal integer filling. The degeneracy between the dxz and dyz orbitals is subsequently lifted by the JT distortion, opening a gap in the electronic band structure.

en: Evidence for Jahn-Teller-driven metal-insulator transition in strained SrCrO₃ from first principles calculations

dftu

display_name: dftu

jahn-teller-effect

display_name: jahn-teller-effect

orbital-order

display_name: orbital-order


en: The concept of order in disordered materials is the key to controlling the mechanical, electrical, and chemical properties of amorphous compounds widely exploited in industrial applications and daily life. Rather, it is far from being understood. Here, we propose a multi-technique numerical approach to study the order/disorder of amorphous materials on both the short- and the medium-range scale. We combine the analysis of the disorder level based on chemical and physical features with their geometrical and topological properties, defining a previously unexplored interplay between the different techniques and the different order scales. We applied this scheme to amorphous GeSe and GeSeTe chalcogenides, showing a modulation of the internal disorder as a function of the stoichiometry and composition: Se-rich systems are less ordered than Ge-rich systems at the short- and medium-range length scales. The present approach can be easily applied to more complex systems containing three or more atom types without any a priori knowledge about the system chemical–physical features, giving a deep insight into the understanding of complex systems.

en: Multi-technique approach to unravel the (dis)order in amorphous materials

amorphous

display_name: amorphous

chalcogenides

display_name: chalcogenides

disorder

display_name: disorder

intersect

display_name: intersect

molecular-dynamics

display_name: molecular-dynamics

openmodel

display_name: openmodel


en: The magnetic, noncollinear parametrization of Dudarev's DFT+U method is generalized to fully-relativistic ultrasoft pseudopotentials. We present the definition of the DFT+U total energy functional, and the calculation of forces and stresses in the case of orthogonalized atomic orbitals defining the localised Hubbard manifold, where additional contributions arising from the derivative of the inverse square root of the overlap matrix appear. We further extend the perturbative calculation of the Hubbard U parameters within density-functional perturbation theory to the noncollinear relativistic case, by exploiting an existing and recently developed theoretical approach that takes advantage of the time-reversal operator to solve a second Sternheimer equation. We validate and apply the new scheme by studying the electronic structure and the thermodynamics of the binary compounds EuX (where X = O, S, Se, Te is a chalcogen atom), as representative simple crystals, and of the pyrochlore Cd2Os2O7, representative of a more structurally complex oxide.

en: Noncollinear DFT+U and Hubbard parameters with fully-relativistic ultrasoft pseudopotentials

first-principles

display_name: first-principles

quantum-espresso

display_name: quantum-espresso


en:
Machine-learned interatomic potentials are fast becoming an indispensable tool in computational materials science. One approach is the ephemeral data-derived potential (EDDP), which was designed to accelerate atomistic structure prediction. The EDDP is simple and cost-efficient. It relies on training data generated in small unit cells and is fit using a lightweight neural network, leading to smooth interactions which exhibit the robust transferability essential for structure prediction. Here, we present a variety of applications of EDDPs, enabled by recent developments of the open-source EDDP software. New features include interfaces to phonon and molecular dynamics codes, as well as deployment of the ensemble deviation for estimating the confidence in EDDP predictions. Through case studies ranging from elemental carbon and lead to the binary scandium hydride and the ternary zinc cyanide, we demonstrate that EDDPs can be trained to cover wide ranges of pressures and stoichiometries, and used to evaluate phonons, phase diagrams, superionicity, and thermal expansion. These developments complement continued success in accelerated structure prediction.
This record provides the data associated with the different case studies demonstrating the uses of ephemeral data-derived potentials in the article "Developments and Further Applications of Ephemeral Data Derived Potentials".

en: Developments and further applications of ephemeral data derived potentials

materialscloud

en: Materials Cloud

hydrides

display_name: hydrides

machine-learned-interatomic-potentials

display_name: machine-learned-interatomic-potentials

metal-organic-frameworks

display_name: metal-organic-frameworks

phase-diagram

display_name: phase-diagram

phonons

display_name: phonons

superionicity

display_name: superionicity


en: The crystal structure of a material is essentially determined by the nature of its chemical bonding. Consequently, the atomic coordination intimately correlates with the degree of ionicity or covalency of the material. Based on this principle, materials with similar chemical compositions can be successfully categorized into different coordination groups. However, counterexamples recently emerged in complex ternary compounds. For instance, strongly covalent IB-IIIA-VIA₂ compounds, such as AgInS₂, prefer tetrahedrally coordinated structure (TCS), while strongly ionic IA-VA-VIA₂ compounds, such as NaBiS₂, would favor octahedrally coordinated structure (OCS). One naturally expects that IB-VA-VIA₂ compounds with intermediate ionicity or covalency, such as AgBiS₂, should then have a mix-coordinated structure (MCS) consisting of covalent AgS₄ tetrahedra and ionic BiS₆ octahedra. Surprisingly, only OCS was observed experimentally for AgBiS₂. To resolve this puzzle, we perform first-principles studies of the phase stabilities of ternary compounds at finite temperatures. We find that AgBiS₂ indeed prefers MCS at the ground state, in agreement with the typical expectation, but under experimental synthesis conditions disordered OCS becomes energetically more favorable because of its low mixing energy and high configurational entropy. This dataset includes structural information for the MCS and OCS configurations of ABX₂ compounds, along with the principal input data used for each calculation.

en: Dataset of disorder-stabilized unfavorable coordination in complex ABX₂ compounds

materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

grand-canonical-monte-carlo

display_name: grand-canonical-monte-carlo

vasp

display_name: vasp


en: Motivated by the recent experimental discovery of strongly surface-plane-dependent superconductivity at surfaces of KTaO3 single crystals, we calculate the electron-phonon coupling strength, λ, of doped KTaO3 along the reciprocal-space high-symmetry directions. Using the Wannier-function approach implemented in the EPW package, we calculate λ across the experimentally covered doping range and compare its mode-resolved distribution along the [001], [110] and [111] reciprocal-space directions. We find that the electron-phonon coupling is strongest in the optical modes around the Γ point, with some distribution to higher k values in the [001] direction. The electron-phonon coupling strength as a function of doping has a dome-like shape in all three directions and its integrated total is largest in the [001] direction and smallest in the [111] direction, in contrast to the experimentally measured trends in critical temperatures. This disagreement points to a non-BCS character of the superconductivity. Instead, the strong localization of λ in the soft optical modes around Γ suggests an importance of ferroelectric soft-mode fluctuations, which is supported by our findings that the mode-resolved λ values are strongly enhanced in polar structures. The inclusion of spin-orbit coupling has negligible influence on our calculated mode-resolved λ values.

en: First-principles calculation of electron-phonon coupling in doped KTaO₃

envi

en: Environment

ab-initio

display_name: ab-initio

electron-phonon-coupling

display_name: electron-phonon-coupling


en: A detailed understanding of how molecules interact with two-dimensional materials, particularly concerning energy level alignment and charge transfer processes, is essential to incorporate functional molecular films into next-generation 2D material-organic hybrid devices. One of the major challenges in integrating molecular films in field-effect transistors is facilitating ambipolar charge transport, which is often hindered by the large electronic gap of the organic layers. In a recent work we compare the adsorption site-dependent energy level alignment of C60, C70, and C84 fullerenes induced by the spatial variation of the electrostatic surface potential of the h-BN/Rh(111) Moiré superstructure. As the size of the fullerenes increases, the HOMO-LUMO gap shrinks. In the case of C84, we find an intrinsic charge transfer from the substrate to the fullerenes adsorbed in the Moiré pore centers, rendering them negatively charged. The electric field effect-induced charging of neutral fullerenes and discharging of intrinsically negatively charged fullerenes are investigated using scanning tunneling spectroscopy, non-contact atomic force microscopy, and Kelvin probe force spectroscopy. Our findings show that on metal-supported h-BN, the LUMO level of C84 is sufficiently close to the Fermi energy that it can be neutral or 1e− negatively charged depending on slight variations of the electrostatic potential. The findings propose a path to make ambipolar charge transfer accessible and efficient by circumventing the need to overcome the fullerenes' electronic gap. This record contain data that support the results discussed in our work.

en: Ambipolar charge transfer of larger fullerenes enabled by the modulated surface potential of h-BN/Rh(111)

ambipolar-charge-transfer

display_name: ambipolar-charge-transfer

cscs

display_name: cscs

fullerenes

display_name: fullerenes

intermolecular-interactions

display_name: intermolecular-interactions

non-contact-atomic-force-microscopy

display_name: non-contact-atomic-force-microscopy

organic-hybrid-electronics

display_name: organic-hybrid-electronics

scanning-tunneling-microscopy

display_name: scanning-tunneling-microscopy

snsf

display_name: snsf


en: In this work, we systematically evaluate the accuracy in band gap prediction of range-separated hybrid functionals on a large set of semiconducting and insulating materials and carry out comparisons with the performance of their global counterparts. We observe that all the range-separated hybrid functionals that correctly describe the long-range dielectric screening significantly improve from standard hybrid functionals such as PBE0 and HSE06. Among this group, the choice of the short-range Fock exchange fraction and the screening length can further reduce the predicted error. We then propose a universal expression for the selection of the inverse screening parameter as a function of the short-range and long-range Fock exchange fractions, which results in a mean absolute error as small as 0.15 eV for band gap prediction.

en: Range-separated hybrid functionals for accurate prediction of band gaps of extended systems

density-functional-theory

display_name: density-functional-theory

hybrid-functional

display_name: hybrid-functional

quantum-espresso

display_name: quantum-espresso


en: This dataset includes all data presented in the figures of the main manuscript: XPS work function changes, ab-initio calculated work functions, O2p band center shifts, Densities of state for LSC and PCO, i-PLD results for LSC, LSF, STF and PCO, Densities of state for adsorbed O2 on differently decorated PCO surfaces. In this work, we investigated surface dipole changes which were induced by modification of mixed conducting surfaces with binary oxides via pulsed laser deposition.

en: Engineering surface dipoles on mixed conducting oxides with ultra-thin oxide decoration layers

oxygen-exchange

display_name: oxygen-exchange

surface-chemistry

display_name: surface-chemistry

work-function

display_name: work-function


en: Accurate first-principles predictions of the structural, electronic, magnetic, and electrochemical properties of cathode materials can be key in the design of novel efficient Li-ion batteries. Spinel-type cathode materials LixMn2O4 and LixMn1.5Ni0.5O4 are promising candidates for Li-ion battery technologies, but they present serious challenges when it comes to their first-principles modeling. Here, we use density-functional theory with extended Hubbard functionals - DFT+U+V with on-site U and inter-site V Hubbard interactions - to study the properties of these transition-metal oxides. The Hubbard parameters are computed from first-principles using density-functional perturbation theory. We show that while U is crucial to obtain the right trends in properties of these materials, V is essential for a quantitative description of the structural and electronic properties, as well as the Li-intercalation voltages. This work paves the way for reliable first-principles studies of other families of cathode materials without relying on empirical fitting or calibration procedures.

en: Unraveling the effects of inter-site Hubbard interactions in spinel Li-ion cathode materials

dftu

display_name: dftu

dftuv

display_name: dftuv

extended-hubbard-functionals

display_name: extended-hubbard-functionals

li-ion-batteries

display_name: li-ion-batteries

lithium-intercalation-voltage

display_name: lithium-intercalation-voltage

marvelosp

display_name: marvelosp

spinel-cathode-materials

display_name: spinel-cathode-materials


en: A high-fidelity neural network-based force field (NN-F³) is developed to cover the space of strain states up to material failure and the non-equilibrium, intermediate nature of fracture. Simulations of fracture in 2D crystals using NN-F³ reveal spatial complexities from lattice-scale kinks to sample-scale patterns. We find that the fracture resistance cannot be captured by the energy densities of relaxed edges as used in the literature. Instead, the fracture patterns, critical stress intensity factors at the kinks, and energy densities of edges in the intermediate, unrelaxed states offer reasonable measures for the fracture toughness and its anisotropy.

en: Non-equilibrium nature of fracture determines the crack path

fracture-anisotropy

display_name: fracture-anisotropy

fracture-mechanics

display_name: fracture-mechanics

graphene

display_name: graphene

machine-learning

display_name: machine-learning

neural-newrok-force-field

display_name: neural-newrok-force-field


en: The recent development of ultra-fast MAS (>100 kHz) provides new opportunities for structural characterization in solids. Here we use NMR crystallography to validate the structure of verinurad, a microcrystalline active pharmaceutical ingredient. To do this, we take advantage of 1H resolution improvement at ultra-fast MAS and use solely 1H-detected experiments and machine learning methods to assign all the experimental proton and carbon chemical shifts. This framework provides a new tool for elucidating chemical information from crystalline samples with limited sample volume and yields remarkably faster acquisition times compared to 13C-detected experiments, without the need to employ dynamic nuclear polarization.

en: Crystal structure validation of verinurad via proton-detected ultra-fast MAS NMR and machine learning

materialscloud

en: Materials Cloud

nmr-crystallography

display_name: nmr-crystallography

solid-state-nmr

display_name: solid-state-nmr

verinurad

display_name: verinurad


en: Since the preliminary work of Anisimov and co-workers, the Hubbard corrected DFT+U functional has been used for predicting properties of correlated materials by applying on-site effective Coulomb interactions to specific orbitals. However, the determination of the Hubbard U parameter has remained under intense discussion despite the multitude of approaches proposed. Here, we define a selection criterion based on the use of polaronic defect states for the enforcement of the piecewise linearity of the total energy upon electron occupation. A good agreement with results from piecewise-linear hybrid functionals is found for the electronic and structural properties of polarons, including the formation energies. The values of U determined in this way are found to give a robust description of the polaron energetics upon variation of the considered state. In particular, we also address a polaron hopping pathway, finding that the determined value of U leads to accurate energetics without requiring a configurational-dependent U. It is emphasized that the selection of U should be based on physical properties directly associated with the orbitals to which U is applied, rather than on more global properties such as band gaps and band widths.  For comparison, we also determine U through a well-established linear-response scheme finding noticeably different values of U and consequently different formation energies. Possible origins of these discrepancies are discussed. As case studies, we consider the self-trapped electron in BiVO₄, the self-trapped hole in MgO, the Li-trapped hole in MgO, and the Al-trapped hole in 𝛼-SiO₂.

en: Hubbard U through polaronic defect states

materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

density-functional-theory

display_name: density-functional-theory

hybrid-functionals

display_name: hybrid-functionals

polarons

display_name: polarons

self-interaction

display_name: self-interaction


en: We establish the sign of the linear magnetoelectric (ME) coefficient, α, in chromia, Cr₂O₃. Cr₂O₃ is the prototypical linear ME material, in which an electric (magnetic) field induces a linearly proportional magnetization (polarization), and a single magnetic domain can be selected by annealing in combined magnetic (H) and electric (E) fields. Opposite antiferromagnetic domains have opposite ME responses, and which antiferromagnetic domain corresponds to which sign of response has previously been unclear. We use density functional theory (DFT) to calculate the magnetic response of a single antiferromagnetic domain of Cr₂O₃ to an applied in-plane electric field at 0 K. We find that the domain with nearest neighbor magnetic moments oriented away from (towards) each other has a negative (positive) in-plane ME coefficient, α⊥, at 0 K. We show that this sign is consistent with all other DFT calculations in the literature that specified the domain orientation, independent of the choice of DFT code or functional, the method used to apply the field, and whether the direct (magnetic field) or inverse (electric field) ME response was calculated. Next, we reanalyze our previously published spherical neutron polarimetry data to determine the antiferromagnetic domain produced by annealing in combined E and H fields oriented along the crystallographic symmetry axis at room temperature. We find that the antiferromagnetic domain with nearest-neighbor magnetic moments oriented away from (towards) each other is produced by annealing in (anti-)parallel E and H fields, corresponding to a positive (negative) axial ME coefficient, α∥, at room temperature. Since α⊥ at 0 K and α∥ at room temperature are known to be of opposite sign, our computational and experimental results are consistent. This dataset contains the input data to reproduce the calculation of the magnetoelectric effect as plotted in Fig. 3 of the manuscript, for Elk, Vasp, and Quantum Espresso.

en: On the sign of the linear magnetoelectric coefficient in Cr₂O₃

envi

en: Environment

dft

display_name: dft

horizon-europe

display_name: horizon-europe

magnetoelectric

display_name: magnetoelectric


en: Structurally and conformationally diverse databases are needed to train accurate neural networks or kernel-based potentials capable of exploring the complex free energy landscape of flexible functional organic molecules. Curating such databases for species beyond "simple" drug-like compounds or molecules comprised of well-defined building blocks (e.g., peptides) is challenging, as it requires thorough chemical space mapping and evaluation of both chemical and conformational diversity. Here, we introduce the OFF–ON (Organic Fragments From Organocatalysts that are Non-modular) database, a repository of 7,869 equilibrium and 67,457 non--equilibrium geometries of organic compounds and dimers aimed at describing conformationally flexible functional organic molecules, with an emphasis on photoswitchable organocatalysts. The relevance of this database is then demonstrated by training a Local Kernel Regression model on a low-cost semiempirical baseline and comparing it with a PBE0-D3 reference for several known catalysts, notably the free energy surfaces of exemplary photoswitchable organocatalysts. Our results demonstrate that the OFF–ON dataset offers reliable predictions for simulating the conformational behavior of virtually any (photoswitchable) organocatalyst or organic compound comprised of H, C, N, O, F, and S atoms, thereby opening a computationally feasible route to explore complex free energy surfaces in order to rationalize and predict catalytic behavior.

en: From organic fragments to photoswitchable catalysts: the off-on structural repository for transferable kernel-based potentials

free-energy

display_name: free-energy

photoswitchable-organocatalyst

display_name: photoswitchable-organocatalyst


en: Antiferromagnets are promising platforms for transduction and transmission of quantum information via magnons — the quanta of spin waves — and they offer advantages over ferromagnets in regard to dissipation, speed of response, and robustness to external fields. Recently, transduction was shown in a van der Waals antiferromagnets, where strong spin-exciton coupling enables readout of the amplitude and phase of coherent magnons by photons of visible light. This discovery shifts the focus of research to transmission, specifically to exploring the nonlocal interactions that enable magnon wave packets to propagate. Here we demonstrate that magnon propagation is mediated by the long-range dipole-dipole interaction. This coupling is an inevitable consequence of fundamental electrodynamics, and as such, will likely mediate the propagation of spin at long wavelengths in the entire class of van der Waals magnets currently under investigation. Successfully identifying the mechanism of spin propagation provides a set of optimization rules, as well as caveats, that are essential for any future applications of these promising systems.

en: Dipolar spin wave packet transport in a van der Waals antiferromagnet

antiferromagnets

display_name: antiferromagnets

magnetostatic-spin-waves

display_name: magnetostatic-spin-waves

spin-transport

display_name: spin-transport

time-resolved-kerr-microscopy

display_name: time-resolved-kerr-microscopy


en: Here we report a resonant x-ray diffraction (RXD) study at the Cu L₃ edge on the multichiral system CsCuCl₃, exhibiting helical magnetic order in a chiral crystal structure. RXD is a powerful technique to disentangle electronic degrees of freedom due to its sensitivity to electric monopoles (charge), magnetic dipoles (spin), and electric quadrupoles (orbital). We characterize electric quadrupole moments around Cu ascribed to the unoccupied Cu 3d orbital, whose quantization axis is off the basal plane. Detailed investigation of magnetic reflections reveals additional sinusoidal modulations along the principal axis superimposed on the reported helical structure, i.e., a longitudinal conical (helical-butterfly) structure. The out-of-plane modulations imply significant spin-orbit interaction despite S=1/2 of Cu²⁺.

en: Conical spin order with chiral quadrupole helix in CsCuCl₃

experimental

display_name: experimental

marvel

display_name: marvel


en:
Tin-lead halide perovskites with a bandgap near 1.2 electron-volt hold great promise for thin-film photovoltaics. However, the film quality of solution-processed Sn-Pb perovskites is compromised by the asynchronous crystallization behavior between Sn and Pb components, where the crystallization of Sn-based perovskites tends to occur faster than that of Pb. Here we show that the rapid crystallization of Sn is rooted in its stereochemically active lone pair, which impedes coordination between the metal ion and Lewis base ligands in the perovskite precursor. From this perspective, we introduce a noncovalent binding agent targeting the open metal site of coordinatively unsaturated Sn(II) solvates, thereby synchronizing crystallization kinetics and homogenizing Sn-Pb alloying. The resultant single-junction Sn-Pb perovskite solar cells achieve a certified power conversion efficiency of 24.13 per cent. The encapsulated device retains 90 per cent of the initial efficiency after 795 hours of maximum power point operation under simulated one-sun illumination.
We conducted DFT and AIMD simulations to study the structure and interactions of metal iodide-solvent ligand complexes. This archive contains the optimized atomic coordinates of PbI2/SnI2-ligands complexes and the initial and final configurations of AIMD trajectories.

en: Synchronized crystallization in tin-lead perovskite solar cells

halide-perovskite

display_name: halide-perovskite

lead-iodide

display_name: lead-iodide

tin-iodide

display_name: tin-iodide


en:
This data record contains the xyz-style files for 2'707 organic molecular crystals and their 3'202 molecular components. The crystals were initially taken directly from the Cambridge Structure Database, and the relaxations were computed and reported by [1]. This record contains an augmentation of a subset of this data, where we have identified the molecular constituents of each crystal, performed geometric relaxations using the same computational parameters, and identified the constituent functional groups. A full detail of methodology and provenance is included in the ESI of [2].
Each crystal and molecule has been relaxed using Quantum Espresso with the following parameters: the PBE exchange-correlation functional, the D2 dispersion correction, ultrasoft pseudopotentials with GIPAW reconstruction, and an equivalent plane-wave energy cutoff of 60 Ryd. We converged the energies within 1E-4 Ryd and forces below 1E-3 Ryd/Bohr, respectively, using MT-decoupling for the molecules to ensure no self-interaction. Furthermore, we compute the binding energy based on the lowest energy conformed represented in the dataset, ensuring comparability between crystals and co-crystals of similar stoichiometries without needing to obtain the global minimum conformation of each molecule.

en: Lattice energies and relaxed geometries for 2'707 organic molecular crystals and their 3'242 molecular components.

snsf

display_name: snsf

trinity-college

display_name: trinity-college


en: Verification and validation of methods and first-principles software are at the core of computational solid-state physics but are too rarely addressed. We compare four first-principles codes: Abinit, Quantum ESPRESSO, EPW, ZG, and three methods: (i) the Allen-Heine-Cardona theory using density functional perturbation theory (DFPT), (ii)  the Allen-Heine-Cardona theory using Wannier function perturbation theory (WFPT), and (iii) an adiabatic non-perturbative frozen-phonon method. For these cases, we compute the real and imaginary parts of the electron-phonon self-energy in diamond and BAs, including dipoles and quadrupoles when interpolating. We find excellent agreement between software that implements the same formalism as well as good agreement between the DFPT and WFPT methods. Importantly, we find that the Deybe-Waller term is momentum dependent which impacts the mass enhancement, yielding approximate results when using the Luttinger approximations. Finally, we compare the electron-phonon spectral functions between Abinit and EPW and find excellent agreement even away from the band edges.

en: Zero-point renormalization of the bandgap, mass enhancement, and spectral functions: Validation of methods and verification of first-principles codes

materialscloud

en: Materials Cloud

ab-initio

display_name: ab-initio

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

verification-and-validation

display_name: verification-and-validation

zero-point-renormalization

display_name: zero-point-renormalization


en:
Multi-band effects in superconducting heterostructures provide a rich playground for unconventional physics. We combine two complementary approaches based on density-functional theory (DFT) and effective low-energy model theory in order to investigate the proximity effect in a gold overlayer on the s-wave superconductor aluminium. We explain both theoretical approaches and intertwine the effective model and DFT analysis. This allows us to predict finite energy superconducting avoided crossings due to the interplay of the Rashba surface state of Au, and hybridization with the electronic structure of superconducting Al. We investigate the nature of the induced superconducting pairing and analyze their mixed singlet-triplet character. Our findings demonstrate the general recipes to explore material systems that exhibit novel finite-energy pairings.

This dataset accompanies a publication where the data is presented and discussed in detail.

en: Proximity-induced Cooper pairing at low and finite energies in the gold Rashba surface state

materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

bogoliubov-de-gennes

display_name: bogoliubov-de-gennes

jukkr

display_name: jukkr

spin-orbit-coupling

display_name: spin-orbit-coupling

superconductivity

display_name: superconductivity


en: Multi-junction solar cells pose a particular modeling challenge, and advanced methods are needed to improve their efficiency and reduce their cost. We present here data about the I-V and P-V characteristics gotten using the electrical parameters obtained through an easy method to extract the electrical parameters of multi-junction solar cells using a single-diode model. The proposed technique is based solely on manufacturer data, and we solve the necessary mathematical equations numerically.

en: Curves data of a simple implicit method for parameters estimation of multi-junction solar cells

envi

en: Environment

iv

display_name: iv

multiple-junction

display_name: multiple-junction

parameter-extraction-methods

display_name: parameter-extraction-methods

solar-energy

display_name: solar-energy


en:
A dataset of 35,608 materials with their topological properties is constructed by combining the density functional theory (DFT) results of Materiae and the Topological Materials Database. Thanks to this, machine-learning approaches are developed to categorize materials into five distinct topological types, with the XGBoost model achieving an impressive 85.2% classification accuracy. By conducting generalization tests on different sub-datasets, differences are identified between the original datasets in terms of topological types, chemical elements, unknown magnetic compounds, and feature space coverage. Their impact on model performance is analyzed.
Turning to the simpler binary classification between trivial insulators and nontrivial topological materials, three different approaches are also tested. Key characteristics influencing material topology are identified, with the maximum packing efficiency and the fraction of p valence electrons being highlighted as critical features.

en: Machine learning on multiple topological materials datasets

database

display_name: database

machine-learning

display_name: machine-learning

topological-materials

display_name: topological-materials


en: Efficient O₂ reduction to H₂O₂, vital for energy conversion and environmental cleanup, relies on precise control of heterogeneous catalysts interacting with reaction species. Through high-throughput density functional theory calculations, consisting of 369 single atom catalysts, we identified the polarized descriptor (electric dipole strength) on two-dimensional carbon materials, revealing insights into the catalytic effect of support polarization. Surprisingly, this descriptor exhibits advanced scaling relationships towards H₂O₂ synthesis, incorporating factors such as active metals, coordination environments, and surface curvatures, highlighting its widespread significance. Furthermore, it demonstrates reliable predictability for O₂ adsorption in dynamic water environments, with optimal reactivity observed within the range of -1.40 to -1.00 e·Å, as confirmed by dynamic and static simulations of the 2e- pathway of O₂ reduction. In essence, these findings offer valuable insights for the rational design of electrocatalysts tailored for selective O₂ reduction.

en: Scaling relations and dynamical predictiveness of electric dipole strength on 2e- ORR catalytic property

density-functional-theory-calculations

display_name: density-functional-theory-calculations

electric-dipole-strength

display_name: electric-dipole-strength

oxygen-reduction-reaction

display_name: oxygen-reduction-reaction

support-polarization

display_name: support-polarization


en:
This study presents the initial implementation of the Fast INtention-Agnostic LEarning Server (FINALES) in a demonstration of a distributed Materials Acceleration Platform (MAP) including experimental and computational methods and a machine learning (ML)-based optimizer. In this demonstration, the optimizer was configured to minimize the density of the electrolyte solutions while maximizing the viscosity by exploiting experimental and computational results. The tenants (the units connected to FINALES in the MAP) are shortly described in the following:

- Autonomous Synthesis and Analysis of Battery electrolytes (ASAB) setup: an experimental tenant providing density and viscosity data using a densimeter of the type DMA 4100M and a viscometer of type Lovis 2000 both by Anton Paar Germany
- Molecular dynamics tenant: a computational tenant capable of providing radial distribution functions, diffusion coefficients, ionic conductivity, transference numbers, heat capacity and density data to the MAP
- Optimizer: the tenant guiding the optimization by processing the available data and generating requests for electrolyte formulations to be tested subsequently

This dataset accompanies the publication:
Vogler, M., Busk, J., Hajiyani, H., Jørgensen, P. B., Safaei, N., Castelli, I. E., Ramirez, F. F., Carlsson, J., Pizzi, G., Clark, S., Hanke, F., Bhowmik, A. & Stein, H. S. Brokering between tenants for an international materials acceleration platform. Matter 6, 2647–2665 (2023). DOI: https://doi.org/10.1016/j.matt.2023.07.016

en: FINALES (06/2022) – Electrolyte Optimization for Minimum Density and Maximum Viscosity

battery-electrolyte

display_name: battery-electrolyte

big-map

display_name: big-map

conductivity

display_name: conductivity

density

display_name: density

materials-acceleration-platform-map

display_name: materials-acceleration-platform-map

polis

display_name: polis

radial-distribution-function-rdf

display_name: radial-distribution-function-rdf

viscosity

display_name: viscosity


en: Proximity-induced superconductivity in hybrid devices of topological insulators and superconductors offers a promising platform for the pursuit of elusive topological superconductivity and its anticipated applications, such as fault-tolerant quantum computing. To study and harness such hybrid devices, a key challenge is the realization of highly functional material interfaces with a suitable superconductor featuring 2e-periodic parity-conserving transport to ensure a superconducting hard-gap free of unpaired electrons, which is important for Majorana physics. A superconductor well-known for this characteristic is Al, however, its direct integration into devices based on tetradymite topological insulators has so far been found to yield non-transparent interfaces. By focusing on Bi₂Te₃-Al heterostructures, this study identifies detrimental interdiffusion processes at the interface through atomically resolved structural and chemical analysis, and showcase their mitigation by leveraging different interlayers – namely Nb, Ti, Pd, and Pt – between Bi₂Te₃ and Al. Through structural transformation of the interlayer materials (X) into their respective tellurides (XTe₂) atomically-sharp epitaxial interfaces are engineered and further characterized in low-temperature transport experiments on Al-X-Bi₂Te₃-X-Al Josephson junctions and in complementary density functional theory calculations. By demonstrating functional interfaces between Bi₂Te₃ and Al, this work provides key insights and paves the way for the next generation of sophisticated topological devices.

en: Engineering epitaxial interfaces for topological insulator – superconductor hybrid devices with Al electrodes

dft

display_name: dft

epitaxial-interfaces

display_name: epitaxial-interfaces

fleur

display_name: fleur

hybrid-devices

display_name: hybrid-devices

interface-engineering

display_name: interface-engineering

josephson-junctions

display_name: josephson-junctions

jukkr

display_name: jukkr

majorana-platform

display_name: majorana-platform

superconductors

display_name: superconductors

tmdc

display_name: tmdc

topological-insulator

display_name: topological-insulator

topological-superconductivity

display_name: topological-superconductivity


en: The nature of the gap observed at the zone border in the spin excitation spectrum of CrI₃ quasi-two-dimensional single crystals is still controversial. We perform first-principles calculations based on time-dependent density functional perturbation theory, which indicate that the observed gap results from a combination of spin-orbit and interlayer interaction effects. The former give rise to the anisotropic spin-spin interactions that are responsible for its very existence, while the latter determine both its displacement from the K point of the Brillouin zone, due to the in-plane lattice distortions induced by them, and an enhancement of its magnitude, in agreement with experiments and previous theoretical work based on a lattice model.

en: First-principles study of the gap in the spin excitation spectrum of the CrI₃ honeycomb ferromagnet

dirac-cone

display_name: dirac-cone

magnons

display_name: magnons

marvel

display_name: marvel

quantum-espresso

display_name: quantum-espresso

tddft

display_name: tddft


en:
Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or be combined explicitly with physically-grounded operations. We present an example of an integrated modeling approach, in which a symmetry-adapted ML model of an effective Hamiltonian is trained to reproduce electronic excitations from a quantum-mechanical calculation. The resulting model can make predictions for molecules that are much larger and more complex than those that it is trained on, and allows for dramatic computational savings by indirectly targeting the outputs of well-converged calculations while using a parameterization corresponding to a minimal atom-centered basis. Our results on a comprehensive dataset of hydrocarbons emphasize the merits of intertwining data-driven techniques with physical approximations, improving the transferability and interpretability of ML models without affecting their accuracy and computational efficiency, and providing a blueprint for developing ML-augmented electronic-structure methods.
Here we include the dataset, accompanying the paper linked below, of hydrocarbons including ethane, ethene, butadiene, hexane, hexatriene, isoprene, styrene, polyalkenes (dodecahexaene, tetradecaheptaene, hexadecaoctaene, octadecanonaene, eicosadecaene), aromatics (benzene, azulene, naphthalene, biphenyl), anthracene, beta-carotene, fullerene. We also provide scripts to generate the Fock and overlap matrices in this dataset. The code for machine learning can be found at the Software reference below.

en: Electronic excited states from physically-constrained machine learning

materialscloud

en: Materials Cloud

epfl

display_name: epfl

erc

display_name: erc

excited-states

display_name: excited-states

fiamma

display_name: fiamma

hamiltonian

display_name: hamiltonian

lifetimes

display_name: lifetimes

machine-learning

display_name: machine-learning


en: We study self-interaction effects in solvated and strongly-correlated cationic molecular clusters, with a focus on the solvated hydroxyl radical. To address the self-interaction issue, we apply the DC-r²SCAN method, with the auxiliary density matrix approach. Validating our method through simulations of bulk liquid water, we demonstrate that DC-r²SCAN maintains the structural accuracy of r²SCAN while effectively addressing spin density localization issues. Extending our analysis to solvated cationic molecular clusters, we find that the hemibonded motif in the [CH₃S∴CH₃SH]⁺ cluster is disrupted in the DC-r²SCAN simulation, in contrast to r²SCAN that preserves the (three-electron-two-center)-bonded motif. Similarly, for the [SH∴SH₂]⁺ cluster, r²SCAN restores the hemibonded motif through spin leakage, while DC-r²SCAN predicts a weaker hemibond formation influenced by solvent-solute interactions. Our findings demonstrate the potential of DC-r²SCAN combined with the auxiliary density matrix method to improve electronic structure calculations, providing insights into the properties of solvated cationic molecular clusters. This work contributes to the advancement of self-interaction corrected electronic structure theory and offers a computational framework for modeling condensed phase systems with intricate correlation effects.

en: Radicals in aqueous solution: Assessment of density-corrected SCAN functional

materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

aqueous-hydroxyl-radical

display_name: aqueous-hydroxyl-radical

aqueous-sulfanyl-radical

display_name: aqueous-sulfanyl-radical

density-corrected-dft

display_name: density-corrected-dft

density-functional-theory

display_name: density-functional-theory

hydrogen-sulfide

display_name: hydrogen-sulfide

methanethiol

display_name: methanethiol


en: The poor ductility of hcp Mg is attributed to the low activity of non-basal slip systems and so activation of prismatic slip can aid ductility in rolled sheets by providing three additional  Burgers vector slip systems.  Experimental studies show that dilute additions of alloying elements such as Zn and Al leads to softening of prismatic slip at low temperatures but strengthening at higher temperatures.  Here, the role of solute strengthening of prismatic edge dislocations is investigated as a possible explanation for the higher-T strengthening.  Mg-Zn is studied using first-principles inputs in a parameter-free solute strengthening theory.  First-principles DFT is necessary to accurately assess the strong solute chemical interaction energies in the core of the compact edge dislocation.   Such calculations are subtle due to motion of the dislocation in the presence of the solute, and methods to obtain reliable results with acceptable computational cost are discussed.   While interaction energies of Zn in the prism edge core can be quite large (+/- 0.34 eV), the edge solute strengthening of prismatic slip in dilute Mg-Zn remains well below experiments at high temperatures.  However, the large difference (0.68 eV) in Zn/dislocation interaction energies across the core of the edge dislocation suggests strengthening by dynamic strain aging as an explanation for the higher-T strengthening.

en: Solute strengthening of prism edge dislocations in Mg alloys

envi

en: Environment

dft

display_name: dft

edge-solute-strengthening

display_name: edge-solute-strengthening

mg-zn

display_name: mg-zn

prismatic-slip

display_name: prismatic-slip

solutedislocation-interaction-energy

display_name: solutedislocation-interaction-energy


en: The reactivity of silicates in aqueous solution is relevant to various chemistries ranging from silicate minerals in geology, to the C-S-H phase in cement, nanoporous zeolite catalysts, or highly porous precipitated silica. While simulations of chemical reactions can provide insight at the molecular level, balancing accuracy and scale in reactive simulations in the condensed phase is a challenge. Here, we demonstrate how a machine-learning reactive interatomic potential can accurately capture silicate-water reactivity. The model was trained on a new dataset comprising 400,000 energies and forces of molecular clusters at the 𝜔-B97XD\def2-TVZP level. To ensure the robustness of the model, we introduce a new and general active learning strategy based on the attribution of the model uncertainty, that automatically isolates uncertain regions of bulk simulations to be calculated as small-sized clusters. Our trained potential is found to reproduce static and dynamic properties of liquid water and solid crystalline silicates, despite having been trained exclusively on cluster data. Furthermore, we utilize enhanced sampling simulations to recover the self-ionization reactivity of water accurately, and the acidity of silicate oligomers, and lastly study the silicate dimerization reaction in a water solution at neutral conditions and find that the reaction occurs through a flanking mechanism.

en: Learning a reactive potential for silica-water through uncertainty attribution

active-learning

display_name: active-learning

chemical-reactions

display_name: chemical-reactions

free-energy

display_name: free-energy

neural-network-potential

display_name: neural-network-potential

silicates

display_name: silicates


en: Low-crosslinked polymer networks have recently been found to behave auxetically when subjected to small tensions, that is, their Poisson's ratio ν becomes negative. In addition, for specific state points, numerical simulations revealed that diamond-like networks reach the limit of mechanical stability, exhibiting values of ν = −1, a condition that we define as hyper-auxeticity. This behavior is interesting per se for its consequences in materials science but is also appealing for fundamental physics because the mechanical instability is accompanied by evidence of criticality. In this work, we deepen our understanding of this phenomenon by performing a large set of equilibrium and stress–strain simulations in combination with phenomenological elasticity theory. The two approaches are found to be in good agreement, confirming the above results. We also extend our investigations to disordered polymer networks and find that the hyper-auxetic behavior also holds in this case, still manifesting a similar critical-like behavior as in the diamond one. Finally, we highlight the role of the number density, which is found to be a relevant control parameter determining the elastic properties of the system. The validity of the results under disordered conditions paves the way for an experimental investigation of this phenomenon in real systems, such as hydrogels.

en: Auxetic polymer networks: The role of crosslinking, density, and disorder

auxetic-materials

display_name: auxetic-materials

elasticity

display_name: elasticity

hydrogels

display_name: hydrogels


en: The anharmonic infrared spectrum of adsorbed CO is simulated using density functional theory (DFT) to gain insight into the nature of Pd nanoparticles (NPs) supported on ceria. The authors systematically determine how the simulated infrared spectra are affected by CO coverage, NP size (0.5–1.5 nm), NP morphology (octahedral, icosahedral), and metal-support contact angle, by exploring a diversity of realistic models inspired by ab initio molecular dynamics.

en: Identifying the structure of supported metal catalysts using vibrational fingerprints from ab initio nanoscale models

carbon-monoxide

display_name: carbon-monoxide

ceria

display_name: ceria

infrarred

display_name: infrarred

palladium

display_name: palladium

spectroscopy

display_name: spectroscopy


en: Transition metal oxides (e.g., cobalt oxides) often undergo a dynamic surface reconstruction under oxygen evolution reaction (OER) conditions to form the active state, which differs in response to the electrolyte pH. The resulting pH-dependency of OER activity is commonly observed but poorly understood. Herein, we demonstrate that operando X-ray absorption spectroscopy (XAS) characterization enables tracking of the Co oxidation transformation at different pH-directed (hydr)oxide/electrolyte interfaces. Combined with in situ electrochemical analyses, correlations between Co redox dynamics, flat band potential and Co oxidation transformations are established to explain the pH-dependency of OER activity. In alkaline environments, the low flat band potential allows a low-potential Co redox transformation, which in turns favors surface reconstruction. In neutral and acidic environments, an anodic shift of the Co redox transformation increases the OER overpotential, particularly in an acidic environment. The largest OER overpotential, in a neutral environment, is further attributable to the poor polarizability of Co atoms and the slowest Co oxidation transformation with respect to the change in applied potential (or OER current). These findings reveal that the Co oxidation transformation at the interface is the factor directly determining the pH-dependency of OER activity, therefore providing insight into designing efficient OER catalysts in different pH environments.

en: Oxidation of interfacial cobalt controls the pH-dependence of the oxygen evolution reaction

cobalt-oxides

display_name: cobalt-oxides

electrocatalytic-interface

display_name: electrocatalytic-interface

operando-x-ray-adsorption-spectroscopy

display_name: operando-x-ray-adsorption-spectroscopy

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction


en:
We performed a computational study using Density Functional Theory calculations on a copper-graphene system. A global minima search was performed using the Minima Hopping algorithm to find multiple graphene isomers that can appear on different substrates. As a model of copper substrate, we used a 112 copper atom cluster and as a model of graphene flake, we used 40 carbon atoms on top of the copper surface. The system was placed in vacuum to guarantee no interaction between images.

The dataset contains coordinates of graphene flake isomers on crystalline and amorphous Cu substrates.

en: Comparative study of defects in graphene flake grown on amorphous and crystalline copper surfaces

copper

display_name: copper

density-functional-theory

display_name: density-functional-theory

graphene

display_name: graphene

graphene-growth

display_name: graphene-growth

metal-substrate

display_name: metal-substrate


en: Determining the atomic structure of a surface is essential for reliable simulations and in-depth exploration of chemical and atomic-scale physical processes. Using Ge(110) c(8×10) as a case study, this work employs Density Functional Theory (DFT) calculations to examine the role of vibrational entropy in surface reconstruction stability. The Ge(110) c(8×10) unit cell consists of interstitial-based pentamers (Universal Building Block model, UBB) interspersed with regions appearing in STM images as unreconstructed. DFT calculations predict that adding more pentamers lowers the surface energy, contradicting experimental findings. This discrepancy is resolved when vibrational entropy is accounted for and sur-face divacancies are introduced in addition to the UBB pentamers. These divacancies are similar to those proposed earlier in the Tetramer-Heptagonal and Tetragonal Ring (THTR) reconstruction model. The nearest neighbors of the vacancy sites are rebonded as on monatomic step edges. The differences in the vibrational entropy contributed by pentamers, divacancies, and unreconstructed surface stabilize Ge(110) c(8×10) reconstructions with the pentamer density observed experimentally. The presence of divacancies is conceptually consistent with the presence of monatomic steps in Ge(110) "16×2", the most stable reconstruction of this surface.

en: Ge(110) c(8×10) reconstructions stabilized by vibrations

materialscloud

en: Materials Cloud

dft

display_name: dft

pbe

display_name: pbe


en:
The presence of spin-orbit coupling or non-collinear magnetic spin states can have dramatic effects on the ground-state and spectral properties of materials, in particular on the band structure. Here, we develop non-collinear Koopmans-compliant functionals based on Wannier functions and density-functional perturbation theory, targeting accurate spectral properties in the quasiparticle approximation. Our non-collinear Koopmans-compliant theory involves functionals of four-component orbitals densities, that can be obtained from the charge and spin-vector densities of Wannier functions. We validate our approach on four emblematic non-magnetic and magnetic semiconductors where the effect of spin-orbit coupling goes from small to very large: the III-IV semiconductor GaAs, the transition-metal dichalcogenide WSe₂, the cubic perovskite CsPbBr₃, and the ferromagnetic semiconductor CrI₃.
The predicted band gaps are comparable in accuracy to state-of-the-art many-body perturbation theory and include spin-dependent interactions and screening effects that are missing in standard diagrammatic approaches based on the random phase approximation. While the inclusion of orbital- and spin-dependent interactions in many-body perturbation theory requires self-screening or vertex corrections, they emerge naturally in the Koopmans-functionals framework.

en: Spin-dependent interactions in orbital-density-dependent functionals: non-collinear Koopmans spectral functionals

materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

metal-halide-perovskite

display_name: metal-halide-perovskite

non-collinear-magnetism

display_name: non-collinear-magnetism

snsf

display_name: snsf

spectral-functionals

display_name: spectral-functionals

spin-dependent-interactions

display_name: spin-dependent-interactions

spin-orbit-coupling

display_name: spin-orbit-coupling

spin-torque

display_name: spin-torque

transition-metal-dichalcogenide

display_name: transition-metal-dichalcogenide


en: In the pursuit of selective conversion of methane directly to methanol in the liquid phase, a common challenge is the concurrent formation of undesirable liquid oxygenates or combustion byproducts. However, we demonstrate that monometallic Pd-CeO2 catalysts, modified by carbon, created by a simple mechanochemical synthesis method exhibit 100% selectivity towards methanol at 75°C, using hydrogen peroxide as oxidizing agent. The solvent-free synthesis yields a distinctive Pd-iC-CeO2 interface, where interfacial carbon (iC) modulates metal-oxide interactions and facilitates tandem methane activation and peroxide decomposition, thus resulting in an exclusive methanol selectivity of 100% with a rate of 117 µmol/gcat at 75°C. Notably, solvent interactions of H2O2 (aq) were found to be critical for methanol selectivity through a DFT-simulated Eley-Rideal-like mechanism. This mechanism uniquely enables the direct conversion of methane into methanol via a solid-liquid-gas process.

en: From Methane to Methanol: Pd-iC-CeO2 Catalysts Engineered for High Selectivity via Mechano-Chemical Synthesis

envi

en: Environment

eley-rideal-mechanism

display_name: eley-rideal-mechanism


en:
This record contains three datasets and the scripts used to generate figures in "Expanding density-correlation machine learning representations for anisotropic coarse-grained particles." This paper explores the theory and implementation of machine-learning descriptors for ellipsoidal bodies, extending the popular "Smooth Overlap of Atomic Positions" (SOAP) formalism. These case studies serve to demonstrate the different use cases of this technology.  The three datasets are:
- Generated configurations of nematic and smectic liquid crystal systems, with a range of orientational order, characterized by the nematic order parameter
- Dimers of (1, 1.5, 2) ellipsoids at different interaction cutoffs and rotations, with computed Gay-Berne type energies
- Crystalline configurations of planar benzene molecules, with energetics computed  using QuantumEspresso v7.046 using Perdew–Burke–Ernzerhof (PBE) pseudopotentials and cutoff parameters reported by Prandini et al.,  Grimme D3-dispersion correction, and a 3 × 3 Monkhorst–Pack k-point grid.

benzene

display_name: benzene

liquid-crystals

display_name: liquid-crystals

machine-learning

display_name: machine-learning

methods

display_name: methods

quantum-espresso

display_name: quantum-espresso