en: The ethanol surface reaction over CeO₂ nanooctahedra (NO) and nanocubes (NC), which mainly expose (111) and (100) surfaces, respectively, was studied by means of infrared spectroscopy (TPSR-IR), mass spectrometry (TPSR-MS) and density functional theory (DFT) calculations. TPSR-MS results show that the production of H₂ is 2.4 times higher on CeO₂ -NC than on -NO, which is rationalized starting from the different types of adsorbed ethoxy species controlled by the shape of the ceria particles. Over the CeO₂(111) surface, monodentate type I and II ethoxy species with the alkyl chain perpendicular or parallel to the surface, respectively, were identified. Whereas on the CeO₂(100) surface, bidentate and monodentate type III ethoxy species on the checkerboard O-terminated and on a pyramid of the reconstructed (100) surface, respectively, are found. The more labile surface ethoxy species on each ceria nanoshape, which are the monodentate type I or III ethoxy on CeO₂ -NO and -NC, respectively, react on the surface to give acetate species that decomposes to CO₂ and CH₄, while H₂ is formed via the recombination of hydroxyl species. In addition, the more stable monodentate type II and bidentate ethoxy species on CeO₂ -NO and -NC, respectively, give an ethylenedioxy intermediate the binding of which is facet dependent. On the (111) facet, the less strongly bound ethylenedioxy desorbs as ethylene, whereas on the (100) facet, the more strongly bound intermediate also produces CO₂ and H₂ via formate species. Thus, on the (100) facet an additional pathway towards H₂ formation is found. ESR activity measurements show an enhanced H₂ production on the nanocubes.

en: Shape-controlled pathways in the hydrogen production from ethanol steam reforming over ceria nanoparticles

ceria-nanostructures

display_name: ceria-nanostructures

ethanol-decomposition

display_name: ethanol-decomposition

ethylenedioxy-species

display_name: ethylenedioxy-species

facet-dependent-activity

display_name: facet-dependent-activity

green-hydrogen

display_name: green-hydrogen


$oid: OBSOLETE

en:
Two different heat-transport mechanisms are discussed in solids: in crystals, heat carriers propagate and scatter like particles, as described by Peierls' formulation of Boltzmann transport equation for phonon wavepackets; in glasses, instead, carriers behave wave-like, diffusing via a Zener-like tunneling between quasi-degenerate vibrational eigenstates, as described by the Allen-Feldman equation. Recently, it has been shown that these two conduction mechanisms emerge as limiting cases from a unified transport equation, which describes on an equal footing solids ranging from crystals to glasses; moreover, in materials with intermediate characteristics the two conduction mechanisms coexist, and it is crucial to account for both. Here, we discuss the theoretical foundations of such transport equation as is derived from the Wigner phase-space formulation of quantum mechanics, elucidating how the interplay between disorder, anharmonicity, and the quantum Bose-Einstein statistics of atomic vibrations determines thermal conductivity. This Wigner formulation argues for a preferential phase convention for the dynamical matrix in the reciprocal Bloch representation and related off-diagonal velocity operator's elements; such convention is the only one yielding a conductivity which is invariant with respect to the non-unique choice of the crystal's unit cell and is size-consistent. We rationalize the conditions determining the crossover from particle-like to wave-like heat conduction, showing that phonons below the Ioffe-Regel limit (i.e. with a mean free path shorter than the interatomic spacing) contribute to heat transport due to their wave-like capability to interfere and tunnel. Finally, we show that the present approach overcomes the failures of the Peierls-Boltzmann formulation for materials with ultralow or glass-like thermal conductivity, with case studies of materials for thermal barrier coatings and thermoelectric energy conversion.

Here, we release the crystal structures, interatomic force constants, and computational parameters needed to reproduce the results discussed in the article "Wigner formulation of thermal transport in solids" (arXiv preprint, 2021) by M. Simoncelli, N. Marzari, and F. Mauri.

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).

en: Materials Cloud

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

anharmonicity

display_name: anharmonicity

phonons

display_name: phonons

thermal-conductivity

display_name: thermal-conductivity

wigner-heat-transport-equation

display_name: wigner-heat-transport-equation


en: This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.

en: E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

density-functional-theory

display_name: density-functional-theory

machine-learning

display_name: machine-learning

molecular-dynamics

display_name: molecular-dynamics


en: Topological crystalline insulators (TCIs) are insulating electronic states with nontrivial topology protected by crystalline symmetries. Recently, theory has proposed new classes of TCIs protected by rotation symmetries Ĉ_n, which have surface rotation anomaly evading the fermion doubling theorem, i.e., n instead of 2n Dirac cones on the surface preserving the rotation symmetry. Here, we report the first realization of the Ĉ_2 rotation anomaly in a binary compound SrPb. Our first-principles calculations reveal two massless Dirac fermions protected by the combination of time-reversal symmetry T̂ and Ĉ_2y on the (010) surface. Using angle-resolved photoemission spectroscopy, we identify two Dirac surface states inside the bulk band gap of SrPb, confirming the Ĉ_2 rotation anomaly in the new classes of TCIs. The findings enrich the classification of topological phases, which pave the way for exploring exotic behavior of the new classes of TCIs.

en: Discovery of Ĉ₂ rotation anomaly in topological crystalline insulator SrPb

electronic-structure

display_name: electronic-structure

snsf

display_name: snsf

topological-materials

display_name: topological-materials


en:
The application of machine learning to the modeling of materials and molecules has proven to be extremely successful in accelerating the understanding, design, and characterization of materials. A major factor in this success has been the development of representations of atomic structures that reflect physics-based symmetries of the underlying interactions. Most of the descriptions of atomic properties or even global observables rely on decompositions into atomic contributions that are subsequently learnt in an atom-centered framework. However, many quantities associated with quantum mechanical calculations, such as the single-particle Hamiltonian matrices written in an atomic-orbital basis, are associated with multiple atom-centers.
Following the introduction of equivariant N-center structural descriptors, in the reference below, that generalize the very successful atom-centered density correlation features to the problem of learning properties indexed by N atoms, we present benchmarks showing how the construction can be applied to efficiently learn the matrix elements of the (effective) single-particle Hamiltonian in an atom-centered orbital basis.
In this record, we include the dataset comprising the Fock and overlap matrices in the def2-SVP of 1000 distorted water molecules, up to 4500 ethanol structures, and a subset of QM7-CHNO molecules. We also provide scripts to generate the two-center representations and fit linear and sparse kernel models for the Hamiltonians.

en: Equivariant representations for molecular Hamiltonians

epfl

display_name: epfl

equivariant-representations

display_name: equivariant-representations

erc

display_name: erc

hamiltonian-learning

display_name: hamiltonian-learning

machine-learning-hamiltonians

display_name: machine-learning-hamiltonians

marvel

display_name: marvel

n-center-representations

display_name: n-center-representations


en: High Entropy Alloys (HEAs) are a new broad class of near-random solid solution alloys that can possess some impressive mechanical and physical properties including high stability against grain growth (i.e. low grain boundary (GB) mobility). Here, it is shown that an initially flat GB in an HEA can become spontaneously rough, driven by natural local compositional fluctuations. Roughening lowers the total GB energy and thus can inhibit migration. A parameter-free theoretical framework is developed to demonstrate the energetics and size scales of the roughening in terms of solute/GB interaction energies and GB disconnection energies. Above a critical level of solute/GB interactions, a planar GB is predicted to roughen down to the scale of the GB periodic unit. A similar theory for 1D GBs (minimum periodic length in one direction) is also developed since such geometries are common in atomistic simulations. Specific predictions are made for the symmetric tilt boundaries Σ17 [100] (530) and Σ5 [100] (310) in a model CoCuFeNi alloy and atomistic simulations demonstrate roughening consistent with the theory. Analysis of the stresses needed to drive migration shows how migration can be inhibited or enhanced, rationalizing variations in mobility of GBs in HEAs.

en: Theory of spontaneous grain boundary roughening in high entropy alloys

grain-boundaries

display_name: grain-boundaries

high-entropy-alloys

display_name: high-entropy-alloys

pinning

display_name: pinning

spontaneous-roughening

display_name: spontaneous-roughening


en: The screening arising from many-body excitations is a crucial quantity for describing ab-sorption and inelastic X-ray scattering (IXS) of materials. Similarly, the electron screening plays a critical role in state-of-the-art approaches for determining the fundamental band gap. However, ab initio studies of the screening in liquid water have remained limited. Here, we use a combined analysis based on the Bethe-Salpeter equation and time-dependent density functional theory. We first show that absorption spectra at near-edge energies are insufficient to assess the accuracy by which the screening is described. Next, when the energy range under scrutiny is extended, we instead find that the IXS spectra are highly sensitive and allow for the selection of the optimal theoretical scheme. This leads to good agreement with experiment over a large range of transferred energies and momenta, and enables establishing the elusive fundamental band gap of liquid water at 9.3 eV.

en: Many-body screening effects in liquid water

dft

display_name: dft

mbpt

display_name: mbpt

water

display_name: water


en: The facet-dependent adsorption of CO on oxidized and reduced CeO₂ single crystal surfaces is reviewed, with emphasis on the effect of CO coverage and the ability of state-of-the-art quantum-mechanical methods to provide reliable energies and an accurate description of the IR vibrational frequency of CO. Comparison with detailed, high-resolution experimental IRRAS data performed on single crystal samples allows the assignment of the different CO vibrational bands observed on all three low-index ceria surfaces. Good agreement is achieved with the hybrid DFT approach with the HSE06 functional and with saturation coverage. It is shown that CO is very sensitive to the structure of cerium oxide surfaces and to the presence of oxygen vacancies. The combined theoretical-experimental approach offers new opportunities for a better characterization of ceria nanoparticles and for unraveling changes occurring during reactions involving CO at higher pressures.

en: Vibrational frequencies of CO bound to all three low-index cerium oxide surfaces: a consistent theoretical description of vacancy-induced changes using density functional theory

co-vibrational-frequency

display_name: co-vibrational-frequency

oxidized-and-reduced-ceria

display_name: oxidized-and-reduced-ceria


en: Photochemical reactions on semiconductors are anisotropic, since they occur with different rates on surfaces of different orientation. Understanding the origin of this anisotropy is crucial to engineering more efficient photocatalysts. In this work, we use hybrid density functional theory (DFT) to identify the surfaces associated with the largest number of photo-generated carriers in different semiconductors. For each material we create a spherical heat map of the probability of optical transitions at different wave vectors. These maps allow to identify the directions associated with the majority of the photo-generated carriers and can thus be used to make predictions about the most reactive surfaces for photochemical applications. Results indicate that it is generally possible to correlate the heat maps with the anisotropy of the bands observed in conventional band-structure plots, as previously suggested. However, we also demonstrate that conventional bands-structure plots do not always provide all the informations and that taking into account the contribution of all possible transitions weighted by their transition dipole moments is crucial to obtain a complete picture.

en: Photochemical anisotropy and direction-dependent optical absorption properties in semiconductors

anisotropy

display_name: anisotropy

cscs

display_name: cscs

hybrid-functionals

display_name: hybrid-functionals

optical-properties

display_name: optical-properties

photocatalysis

display_name: photocatalysis

photochemistry

display_name: photochemistry

semiconductors

display_name: semiconductors

snsf

display_name: snsf


en:
Despite governing heat management in any realistic device, the microscopic mechanisms of heat transport in all-solid-state electrolytes are poorly known: existing calculations, all based on simplistic semi-empirical models, are unreliable for superionic conductors and largely overestimate their thermal conductivity. In this work, we deploy a combination of state-of-the-art methods to calculate the thermal conductivity of a prototypical Li-ion conductor, the Li₃ClO antiperovskite. By leveraging ab initio, machine learning, and forcefield descriptions of interatomic forces, we are able to reveal the massive role of anharmonic interactions and diffusive defects on the thermal conductivity and its temperature dependence, and to eventually embed their effects into a simple rationale which is likely applicable to a wide class of ionic conductors.
In this record, we provide data and scripts to generate the plots supporting our findings. We also provide the machine learning model and the dataset to train it.

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).

en: Temperature- and vacancy-concentration-dependence of heat transport in Li₃ClO from multi-method numerical simulations

batteries

display_name: batteries

heat-transport

display_name: heat-transport

max

display_name: max

solid-state-electrolytes

display_name: solid-state-electrolytes

superionics

display_name: superionics


en: Fingerprint distances, which measure the similarity of atomic environments, are commonly calculated from atomic environment fingerprint vectors. In this work, we present the simplex method that can perform the inverse operation, i.e., calculating fingerprint vectors from fingerprint distances. The fingerprint vectors found in this way point to the corners of a simplex. For a large dataset of fingerprints, we can find a particular largest simplex, whose dimension gives the effective dimension of the fingerprint vector space. We show that the corners of this simplex correspond to landmark environments that can be used in a fully automatic way to analyze structures. In this way, we can, for instance, detect atoms in grain boundaries or on edges of carbon flakes without any human input about the expected environment. By projecting fingerprints on the largest simplex, we can also obtain fingerprint vectors that are considerably shorter than the original ones but whose information content is not significantly reduced.

materialscloud

en: Materials Cloud

en: Maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression

envi

en: Environment

atomic-environment-selection

display_name: atomic-environment-selection

atomic-fingerprints

display_name: atomic-fingerprints

classification-of-atomic-environments

display_name: classification-of-atomic-environments

dimensionality-reduction

display_name: dimensionality-reduction

largest-volume-simplex

display_name: largest-volume-simplex

machine-learning

display_name: machine-learning

overlap-matrix-fingerprint

display_name: overlap-matrix-fingerprint

quantum-non-local-effects

display_name: quantum-non-local-effects


en:
The uploaded archive provides a ML-ready data set extracted from the juHemd database (see references) augmented with supplemental data for atomic descriptors. Descriptors provided in this data set include structural, magnetic, atomic quantities as well as derived (summed) quantities. In total, 118 possible descriptors are included of which 12 are DFT generated. For each simulation type (LDA/GGA) there is also a data set cleaned from DFT data available.

After data cleaning and preprocessing we extracted 387 LDA calculated magnetic Heusler structures as well as 408 GGA structures which have a full structural and magnetic data set. As we only aim at magnetic compounds, we chose to filter out compounds from the original JuHemd which have at least 0.1 Bohr magneton as total absolute magnetic moment. For each data file there is an existing descriptor file naming all the descriptors included in the data set.

en: ML-ready Curie temperatures and descriptors extracted from the JuHemd database

critical-temperature

display_name: critical-temperature

curie-temperature

display_name: curie-temperature

density-functional-theory

display_name: density-functional-theory

electronic-structure

display_name: electronic-structure

heusler-alloy

display_name: heusler-alloy

judft

display_name: judft

kkr

display_name: kkr

magnetic

display_name: magnetic

magnetic-phase-transition

display_name: magnetic-phase-transition

magnetic-structure

display_name: magnetic-structure

monte-carlo

display_name: monte-carlo


en: We demonstrate the importance of addressing the 𝚪 vertex and thus going beyond the GW approximation for achieving the energy levels of liquid water in many- body perturbation theory. In particular, we consider an effective vertex function in both the polarizability and the self-energy, which does not produce any computational overhead compared with the GW approximation. We yield the band gap, the ionization potential, and the electron affinity in good agreement with experiment and with a hybrid functional description. The achieved electronic structure and dielectric screening further lead to a good description of the optical absorption spectrum, as obtained through the solution of the Bethe–Salpeter equation. In particular, the experimental peak position of the exciton is accurately reproduced.

en: Absolute energy levels of liquid water from many-body perturbation theory with effective vertex corrections

gw

display_name: gw

ionization-potential

display_name: ionization-potential

many-body-perturbation-theory

display_name: many-body-perturbation-theory

vertex

display_name: vertex

water

display_name: water


en: Chiral ligands are important components in asymmetric homogeneous catalysis, but their synthesis and screening can be both time-consuming and resource-intensive. Data-driven approaches, in contrast to screening procedures based on intuition, have the potential to reduce the time and resources needed for reaction optimization by more rapidly identifying an ideal catalyst. These approaches, however, are often non-transferable and cannot be applied across different reactions. To overcome this drawback, we introduce a general featurization strategy for bidentate ligands that is coupled with an automated feature selection pipeline and Bayesian ridge regression to perform multivariate linear regression modeling. This approach, which is applicable to any reaction, incorporates electronic, steric, and topological features (rigidity/flexibility, branching, geometry, constitution) and is well-suited for early-stage ligand optimization. Using only a limited number of points per dataset, our workflow capably predicts the enantioselectivity of four metal-catalyzed asymmetric reactions. Uncertainty estimates provided by Bayesian ridge regression permit the use of Bayesian optimization to efficiently explore pools of prospective new ligands. Using this procedure, a new library of 312 chiral bidentate ligands was screened to identify promising ligand candidates for a challenging asymmetric oxy-alkynylation reaction.

en: Reaction-agnostic featurization of bidentate ligands for Bayesian ridge regression of enantioselectivity

bidentate-ligands

display_name: bidentate-ligands

catalysis

display_name: catalysis

epfl

display_name: epfl

homogenous-catalysis

display_name: homogenous-catalysis

ligands

display_name: ligands

nccr-catalysis

display_name: nccr-catalysis


en: In this study, we benchmarked various interatomic potentials and force fields in comparison to an ab initio dataset for bulk amorphous alumina. We investigated a comprehensive set of fixed-charge and variable-charge potentials tailored for alumina. We also train a machine learning interatomic potential, using the NequIP framework. Results highlight that the fixed-charge potential by Matsui provides an ideal blend of computational speed and alignment with ab initio findings for stoichiometric alumina. For non-stoichiometric variants, the variable charge potentials, especially ReaxFF, align remarkably well with DFT outcomes. The NequIP ML potential, while superior in some instances and adaptable, might not be the best fit for specific tasks.

en:
Do we really need machine learning interatomic potentials for modeling amorphous metal oxides?
Case study on amorphous alumina by recycling an existing ab-initio database.

machine-learning

display_name: machine-learning

molecular-dynamics

display_name: molecular-dynamics


en: We present ab initio calculations of hidden magnetoelectric multipolar order in Cr₂O₃ and its iron-based analogue, α-Fe₂O₃. We show the presence of hidden antiferroically-ordered magnetoelectric multipoles in both the prototypical magnetoelectric material Cr₂O₃, and centrosymmetric α-Fe₂O₃, which has the same crystal structure as Cr₂O₃, but a different magnetic dipolar ordering. In turn, we predict anti-magnetoelectric effects, in which local magnetic dipole moments are induced in opposite directions under the application of an external electric field, to create an additional antiferromagnetic ordering. We confirm the predicted induced moments using first-principles calculations. Our results demonstrate the existence of hidden magnetoelectric multipoles leading to local linear magnetoelectric responses even in centrosymmetric materials, where a net bulk linear magnetoelectric effect is forbidden by symmetry.  This archive contains input files and some output files necessary for creating all the data discussed in our paper with the title: Hidden orders and (anti-)Magnetoelectric Effects in Cr₂O₃ and α-Fe₂O₃.

en: Hidden orders and (anti-)magnetoelectric effects in Cr₂O₃ and α-Fe₂O₃

erc

display_name: erc

magnetoelectricity

display_name: magnetoelectricity

multipoles

display_name: multipoles


en:
In this paper, we propose an extension to the approach of [Xi, C; et al. J. Chem. Theory Comput. 2022, 18, 6878] to calculate ion solvation free energies from first-principles (FP) molecular dynamics (MD) simulations of a hybrid solvation model. The approach is first re-expressed within the quasi-chemical theory of solvation. Then, to allow for longer simulation times than the original first-principles molecular dynamics approach and thus improve the convergence of statistical averages at a fraction of the original computational cost, a machine-learned (ML) energy function is trained on FP energies and forces and used in the MD simulations. The ML workflow and MD simulation times (≈200 ps) are adjusted to converge the predicted solvation energies within a chemical accuracy of 0.04 eV. The extension is successfully benchmarked on the same set of alkaline and alkaline-earth ions.
The record includes all molecular-dynamics trajectories, energies and forces used to obtain the solvation energies of alkaline and alkaline-earth ions in water, as reported in Table 2 of referenced paper.

en: Solvation free energies from machine learning molecular dynamics

solvation

display_name: solvation


en: Twinning is an important deformation mode in plastically deformed hexagonal close-packed materials. The extremely high twin growth rates at the nanoscale make atomistic simulations an attractive method for investigating the role of individual twin/matrix interfaces such as twin boundaries and basal-prismatic interfaces in twin growth kinetics. Unfortunately, there is no single framework that allows researchers to differentiate such interfaces automatically, neither in experimental in-situ transmission electron microscopy analysis images nor in atomistic simulations. Moreover, the presence of alloying elements introduces substantial noise to local atomic environments, making it nearly impossible to identify which atoms belong to which interface. Here, with the help of advanced machine learning methods, we provide a proof-of-concept way of using the local stress field distribution as an indicator for the presence of interfaces and for determining their types. We apply such an analysis to the growth of twin embryos in Mg-10 at.% Al alloys under constant stress and constant strain conditions, corresponding to two extremes of high and low strain rates, respectively. We discover that the kinetics of such growth is driven by high-energy basal-prismatic interfaces, in line with our experimental observations for pure Mg. The data provided here allows for reproducing findings from our work.

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).

en: Machine learning of twin/matrix interfaces from local stress field

magnesium-twinning

display_name: magnesium-twinning


en: Antiferromagnets are normally thought of as materials with compensated magnetic sublattices. This adds to their technological advantages but complicates readout of the antiferromagnetic state. We demonstrate theoretically the existence of a Dzyaloshinskii-Moriya interaction (DMI) which is determined by the magnetic symmetry classes of Cr₂O₃ surfaces with an in-plane magnetic easy axis. The DMI explains a previously predicted out-of-plane magnetization at the nominally compensated surfaces of chromia, leading to a surface-localized canted ferrimagnetism. This is in agreement with magnetotransport measurements and with density functional theory predictions which further allow us to quantify the strength of DMI. The temperature dependence of the transversal resistance for these planes shows distinct behavior in comparison with that of the Cr₂O₃  plane, which we attribute to the influence of DMI. Our work provides a framework to analyze surface-driven phenomena in antiferromagnets, and motivates the use of nominally compensated chromia surfaces for antiferomagnetic spintronics and magnonics. Here, we provide the data for density functional theory and Monte Carlo calculations. Micromagnetic and experimental data for this joint experiment-theory collaboration are archived separately according to EU regulations.

materialscloud

en: Materials Cloud

en: Surface-symmetry-driven Dzyaloshinskii-Moriya interaction and canted ferrimagnetism in collinear magnetoelectric antiferromagnet Cr₂O₃

density-functional-theory

display_name: density-functional-theory

dzyaloshinskii-moriya-interactions

display_name: dzyaloshinskii-moriya-interactions

magnetoelectrics

display_name: magnetoelectrics

magnetotransport

display_name: magnetotransport

micromagnetics

display_name: micromagnetics

surface-magnetization

display_name: surface-magnetization


en: We present a first-principles study of the low-temperature rhombohedral phase of BaTiO₃ using Hubbard-corrected density-functional theory. By employing density-functional perturbation theory, we compute the onsite Hubbard U for Ti(3d) states and the intersite Hubbard V between Ti(3d) and O(2p) states. We show that applying the onsite Hubbard U correction alone to Ti(3d) states proves detrimental, as it suppresses the Ti(3d)-O(2p) hybridization and drives the system towards a cubic phase. Conversely, when both onsite U and intersite V are considered, the localized character of the Ti(3d) states is maintained, while also preserving the Ti(3d)-O(2p) hybridization, restoring the rhombohedral phase of BaTiO₃. The generalized PBEsol+U+V functional yields good agreement with experimental results for the band gap and dielectric constant, while the optimized geometry is slightly less accurate compared to PBEsol. Zone-center phonon frequencies and Raman spectra are found to be significantly influenced by the underlying geometry. PBEsol and PBEsol+U+V provide satisfactory agreement with the experimental Raman spectrum when the PBEsol geometry is used, while PBEsol+U Raman spectrum diverges strongly from experimental data highlighting the adverse impact of the U correction alone in BaTiO₃. Our findings underscore the promise of the extended Hubbard PBEsol+U+V functional with first-principles U and V for the investigation of other ferroelectric perovskites with mixed ionic-covalent interactions.

en: Understanding the role of Hubbard corrections in the rhombohedral phase of BaTiO₃

envi

en: Environment

cscs

display_name: cscs

dftu

display_name: dftu

dftuv

display_name: dftuv

phonons

display_name: phonons

raman-spectra

display_name: raman-spectra


en: The mechanisms underlying the reaction between carbon monoxide (CO) and activated dioxygen on metal oxide substrates to produce CO₂ remain poorly understood, particularly regarding the role of oxygen vacancies and the nature of the activated O₂ adsorbate. In this study, we present experimental findings from infrared reflection-absorption spectroscopy on a model system of bulk monocrystalline CeO₂(111). Contrary to expectations, exposing the reduced surface to dioxygen (O₂) at 80 K does not yield activated oxygen species, such as superoxo or peroxo. Notably, in the presence of adsorbed CO, an unexpected low-temperature oxidation reaction occurs, consuming CO while oxidizing the CeO₂ substrate. Since a direct reaction between impinging O₂ and adsorbed CO is unlikely at these low temperatures, a novel mechanism is proposed. Extensive spin-polarized density functional theory (DFT) calculations reveal that oxygen vacancies play a critical role in this low-temperature CO oxidation. Initially located in the subsurface region (Vss), these vacancies migrate to the surface (Vs) via a concerted interaction with coadsorbed CO and O₂, leading to O₂ activation and the formation of superoxo or peroxo species. Detailed analysis identifies key reaction intermediates and quantifies their adsorption energies and activation barriers. Our findings suggest that the peroxo-mediated pathway, with its lower activation barrier, is more favorable for CO oxidation at low temperatures compared to the carbonate pathway. This study provides valuable insights into the dynamic role of subsurface oxygen vacancies in the activation of gaseous O₂ and CO oxidation mechanisms on CeO₂.

en: Synergistic effects in low-temperature CO oxidation on cerium oxide surfaces

ceria

display_name: ceria

co-oxidation

display_name: co-oxidation

dft

display_name: dft

peroxo-and-superoxo

display_name: peroxo-and-superoxo


en: An extensive dataset consisting of adsorption energies of pernicious impurities present in biomass upgrading processes on common catalysts and support materials has been generated. This work aims to inform catalyst and process development for the conversion of biomass-derived feedstocks to fuels and chemicals. A high-throughput workflow was developed to execute density functional theory calculations for a diverse set of atomic (Al, B, Ca, Cl, Fe, K, Mg, Mn, N, Na, P, S, Si, Zn) and molecular (COS, H₂S, HCl, HCN, K₂O, KCl, NH₃) species on 35 unique surfaces for transition-metal (Ag, Au, Co, Cu, Fe, Ir, Ni, Pd, Pt, Re, Rh, Ru) and metal-oxide (Al₂O₃, MgO, anatase-TiO₂, rutile-TiO₂, ZnO, ZrO₂) catalysts and supports. Approximately 3,000 unique adsorption geometries were obtained. The data record includes structure and calculation output files for each unique adsorbate geometry on each surface.

en: High-throughput dataset of impurity adsorption on common catalysts in biomass upgrading applications

high-throughput

display_name: high-throughput

vasp

display_name: vasp


en: Predicting the electrical properties of organic molecular crystals (OMCs) is challenging due to their complex crystal structures and electron-phonon (e-ph) interactions. Charge transport in OMCs is conventionally categorized into two limiting regimes – band transport, characterized by weak e-ph interactions, and charge hopping due to localized polarons formed by strong e-ph interactions. However, between these two limiting cases there is a less well understood intermediate regime where polarons are present but transport does not occur via hopping. Here we show a many-body first-principles approach that can accurately predict the carrier mobility in OMCs in the intermediate regime and shed light on its microscopic origin. Our approach combines a finite-temperature cumulant method to describe strong e-ph interactions with Green-Kubo transport calculations. We apply this parameter-free framework to naphthalene crystal, demonstrating electron mobility predictions within a factor of 1.5–2 of experiment between 100–300 K. Our analysis reveals that electrons couple strongly with both inter- and intramolecular phonons in the intermediate regime, as evidenced by the formation of a broad polaron satellite peak in the electron spectral function and the failure of the Boltzmann transport equation (BTE). Our study advances quantitative modeling of charge transport in complex organic crystals. This dataset contains input and output files required to reproduce the BTE results presented in our work.

en: Intermediate polaronic charge transport in organic crystals from a many-body first-principles approach

boltzmann-equation

display_name: boltzmann-equation

carrier-mobility

display_name: carrier-mobility

charge-transport

display_name: charge-transport

electron-phonon-coupling

display_name: electron-phonon-coupling

electron-phonon-scattering-rate

display_name: electron-phonon-scattering-rate

first-principles-calculations

display_name: first-principles-calculations

molecular-crystal

display_name: molecular-crystal

naphthalene

display_name: naphthalene

organic-crystal

display_name: organic-crystal

polaron

display_name: polaron


en:
We developed a general framework for hybrid quantum-classical computing of molecular and periodic embedding calculations based on an orbital space separation of the fragment and environment degrees of freedom. We show its potential by presenting a specific implementation of periodic range-separated DFT coupled to a quantum circuit ansatz, whereby the variational quantum eigensolver and the quantum equation-of-motion approach are used to obtain the low-lying spectrum of the embedded fragment Hamiltonian.
Application of this scheme to study strongly correlated molecular systems and localized electronic states in materials is showcased through the accurate prediction of the optical properties for the neutral oxygen vacancy in magnesium oxide (MgO). Despite some discrepancies in absorption predictions, the method demonstrates competitive performance with state-of-the-art ab initio approaches, particularly evidenced by the accurate prediction of the photoluminescence emission peak.

en: A general framework for active space embedding methods: applications in quantum computing

marvelqs

display_name: marvelqs

qiskit-nature

display_name: qiskit-nature

quantum-embedding

display_name: quantum-embedding

quantum-simulation

display_name: quantum-simulation


en: An ab initio  study of the coupled electrons and phonon transport properties of MoS2-hBN devices. A comparison of the device characteristics when hBN is treated as a perfectly insulating, non vibrating layer and one where it is included in the DFT together with MoS2.

en: Influence of the hBN dielectric layers on the quantum transport properties of MoS2 transistors.

ab-initio

display_name: ab-initio

quantum-transport

display_name: quantum-transport

thermal-properties

display_name: thermal-properties


en:
Resonant inelastic X-ray scattering (RIXS) can probe localized excitations at selected atoms in materials, including particle-hole transitions from valence to the conduction bands. These transitions are governed by fundamental prop- erties of the corresponding Bloch wave-functions, including orbital and mag- netic degrees of freedom, and quantum geometric properties such as the Berry curvature. In particular, orbital angular momentum (OAM), which is closely linked to the Berry curvature, can exhibit a nontrivial momentum dependence. We demonstrate how information on such OAM textures can be extracted from the circular dichroism in RIXS. Based on accurate modeling with first-principles treatment of the key ingredient – the light-matter interaction – we simulate dichroic RIXS spectra for the prototypical transition metal dichalcogenide MoSe₂ and the two-dimensional topological insulator 1T′-MoS₂ . Guided by an in- tuitive picture for the optical selection rules, we discuss how the momentum- dependent OAM manifests itself in the dichroic RIXS signal if one controls the momentum transfer. Our calculations are performed for typical experimental geometries and parameter regimes, and demonstrate the possibility of observ- ing the predicted circular dichroism in forthcoming experiments. Thus, our work establishes a new avenue to observing Berry curvature and topological states in quantum materials.

The simulated RIXS spectra and the scripts to generate the plots in this work are available in this archive.

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).

en: Probing magnetic orbitals and Berry curvature with circular dichroism in resonant inelastic X-ray scattering

marvel

display_name: marvel

quantum-espresso

display_name: quantum-espresso

two-dimensional

display_name: two-dimensional

wannier-functions

display_name: wannier-functions


en: This dataset includes the full computational workflows of a proof-of-concept study of various possible mechanisms (standard and bifunctional ones) for oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) on exfoliated NiOOH as electrocatalyst, including active edge sites (M5) and hydrogen acceptor sites in the same model system. Furthermore, explicit water is included in the model to describe the equilibration of the M-OOH species to M-OOH/eq, a crucial step that enables a bifunctional route to be operative. Additionally, different single Fe-dopant positions (M1, M2, M3, M4, M5, M6 and M7) are considered and four different reaction schemes (S1, S2, S3 and S4) are studied for the OER and the reverse ORR process. The results are relevant in alkaline conditions, where the studied model systems are stable. Certain Fe-dopant positions result in active Ni-edge sites with very low overpotentials provided that water is present within the model system.

materialscloud

en: Materials Cloud

en: Oxygen evolution and reduction on Fe‑doped NiOOH

electrochemical-overpotential

display_name: electrochemical-overpotential

mixed-metal-oxyhydroxides

display_name: mixed-metal-oxyhydroxides

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction

oxygen-reduction-reaction

display_name: oxygen-reduction-reaction


en: The electronic properties and optical response of ice and water are intricately shaped by their molecular structure, including the quantum mechanical nature of hydrogen atoms. Despite numerous former studies, a comprehensive understanding of nuclear quantum effects (NQE) on the electronic structure of water and ice at finite temperatures remains elusive. Here, we utilize molecular simulations that harness efficient machine-learning potentials and many-body perturbation theory to assess how NQEs impact the electronic bands of water and hexagonal ice. By comparing path-integral and classical simulations, we find that NQEs lead to a larger renormalization of the fundamental gap of ice, compared to that of water, ultimately yielding similar bandgaps in the two systems, consistent with experimental estimates. Our calculations suggest that the increased quantum mechanical delocalization of protons in ice, relative to water, is a key factor leading to the enhancement of NQEs on the electronic structure of ice.

en: Nuclear quantum effects on the electronic structure of water and ice

envi

en: Environment

electronic-structure

display_name: electronic-structure

molecular-dynamics

display_name: molecular-dynamics

nuclear-quantum-effects

display_name: nuclear-quantum-effects

water

display_name: water


en: This work presents an application of the strain-fluctuation method, exploiting the fluctuations of the strain from extensive first-principles molecular dynamics simulations in the isobaric-isothermal ensemble, to the study of the elastic tensors of superionic materials. As the superionic materials for solid-state electrolyte applications usually do not have well-defined ground-state configurations, it is challenging to apply the static methods to calculate the elastic tensors of these materials. Instead, the strain-fluctuation method captures the dynamical nature of the elastic response of these materials and is a promising approach to studying their elastic properties. In this work: a protocol is presented and documented to extract the elastic the elastic moduli and their statistical errors from the molecular dynamics trajectories (open-source code available at https://github.com/materzanini); results for two benchmark superionic materials (Li₁₀GeP₂S₁₂ and Li₁₀GeP₂O₁₂) are given; for these superionic materials, a comparison to static methods is also provided, showing that static methods overestimate the moduli with respect to the correct dynamical treatment by ~25-50%.

en: Solids that are also liquids: elastic tensors of superionic materials

big-map

display_name: big-map

cscs

display_name: cscs

elastic-constants

display_name: elastic-constants

first-principles-molecular-dynamics

display_name: first-principles-molecular-dynamics

lgpo

display_name: lgpo

lgps

display_name: lgps

snsf

display_name: snsf

solid-state-electrolytes

display_name: solid-state-electrolytes

statistical-sampling

display_name: statistical-sampling

stress-strain

display_name: stress-strain

superionics

display_name: superionics


en: The study of magnetism in transition metals is a cornerstone in understanding complex electronic and magnetic interactions in condensed matter systems. Among transition metal elements, body-centered cubic (bcc) chromium stands out because of its spin-density wave (SDW) ground state, posing a long-standing challenge for density functional theory (DFT). Conventional functionals, such as the generalized-gradient approximation (GGA) and the local-density approximation (LDA), fail to predict this experimentally observed incommensurate SDW as the ground state. In this study, we present a comprehensive DFT analysis of bcc Cr employing GGA and a variety of meta-GGA functionals. We evaluated total energies, structural parameters, and magnetic properties across a wide range of SDW wave vectors. Our results show that all meta-GGA functionals overestimate the local magnetic moments and enhance the nodal magnetic frustration, destabilizing the SDW state relative to the commensurate antiferromagnetic (AF) configuration. Tao-Perdew-Staroverov-Scuseria (TPSS) yields results closest to those of the GGA, thus providing the most adequate description of bcc Cr among the meta-GGA functionals. These results emphasize the need for the further development of non-local or hybrid functionals tailored for complex magnetic systems.

en: A first-principles study of bcc chromium beyond the generalized gradient approximation (GGA)

chromium

display_name: chromium

dft

display_name: dft

magnetism

display_name: magnetism

spin-density-waves

display_name: spin-density-waves


en: This dataset includes the full computational workflows of a density functional theory based thermodynamics model for the overpotential of the oxygen evolution reaction (OER) on an oxidized palladium surface. The model assumes an oxygen bridge vacancy as an active site on the 110 surface of the tetragonal PdO2 (rutile type structure). The critical OER potential has been computed with variation of the Fe modifyer type, either dopant or adsorbate, and modifyer position. Furthermore, an alternative bifunctional pathway of OER has been considered by adding an H atom to an auxiliary O-bridge site from which the proton−electron pair for second OER reaction step is released rather than from the hydroxylated active site. The computed OER overpotential on the Fe-free surface via this bifunctional route is 0.42 V. A substitution of Pd with Fe directly at this active site further reduces the calculated OER overpotential, over the same route, to 0.35 V. This 70 mV decrease in overpotential is in good agreement with the experimentally measured decrease of 60 mV in the onset potential.

en: Oxygen evolution reaction by a palladium foil in the presence of iron

electrochemical-overpotential

display_name: electrochemical-overpotential

iron-dopant

display_name: iron-dopant

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction

palladium-dioxide

display_name: palladium-dioxide


en:
Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident estimates in the context of overparametrized neural networks. Several methods, ranging from temperature scaling to different Bayesian treatments of neural networks, have been proposed to mitigate overconfidence, most often supported by the numerical observation that they yield better calibrated uncertainty measures. In this work, we provide a sharp comparison between popular uncertainty measures for binary classification in a mathematically tractable model for overparametrized neural networks: the random features model. We discuss a trade-off between classification accuracy and calibration, unveiling a double descent like behavior in the calibration curve of optimally regularized estimators as a function of overparametrization. This is in contrast with the empirical Bayes method, which we show to be well calibrated in our setting despite the higher generalization error and overparametrization.
This record provides the code to reproduce the numerical experiments of the related paper "On double-descent in uncertainty quantification in overparametrized models".

en: On double-descent in uncertainty quantification in overparametrized models (code)

neural-networks

display_name: neural-networks

numerical-simulation

display_name: numerical-simulation

uncertainty-quantification

display_name: uncertainty-quantification


en: This work explores hydrogen-doped samarium nickelate from first-principles calculations. At a concentration of 1/4 hydrogen per formula unit we find a number of polar states due to the presence of the interstitial hydrogen. Physically, the polarization of the material arises from the localization of the hydrogen's valence electron on a nearby nickel-oxygen octahedron leading to a local dipole. Due to the inherent tilt pattern present in samarium nickelate, a perovskite with an a-a-c+ tilt pattern, there is an insurmountable energy barrier to switch a given polar state the structure related by inversion symmetry. Instead, we use an in-plane epitaxial constraint to tune the total energy of two structures to be equal. These two structures, unrelated by a cell-symmetry operation, have similar a similar position of the interstitial hydrogen atom, but the valence electron localizes on a different nickel-oxygen octahedron leading to different polarizations. We find that there is a surmountable energy barrier to switch between these two structures.

en: "Fraternal-twin" ferroelectricity:  competing polar states in hydrogen-doped samarium nickelate from first principles

charge-localization

display_name: charge-localization

ferroelectricty

display_name: ferroelectricty

rare-earth-nickelates

display_name: rare-earth-nickelates


en: The determination of transport coefficients through the time-honoured Green-Kubo theory of linear response and equilibrium molecular dynamics requires significantly longer simulation times than those of equilibrium properties, while being further hindered by the lack of well-established data-analysis techniques to evaluate the statistical accuracy of the results. Leveraging recent advances in the spectral analysis of the current time series associated to molecular trajectories, we introduce a new method to estimate the full (diagonal as well as off-diagonal) Onsager matrix of transport coefficients from a single statistical model. This approach, based on the knowledge of the statistical distribution of the Onsager-matrix samples in the frequency domain, unifies the evaluation of diagonal (conductivities and viscosities) and off-diagonal (e.g., thermoelectric) transport coefficients within a comprehensive framework, significantly improving the reliability of transport coefficient estimation for materials ranging from molten salts to solid-state electrolytes. We validate the accuracy of this method against existing approaches using benchmark data on molten cesium fluoride and liquid water, and conclude our presentation with the computation of various transport coefficients of the Li₃PS₄ solid-state electrolyte.

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).

en: Transport coefficients from equilibrium molecular dynamics

molecular-dynamics

display_name: molecular-dynamics

solid-state-electrolytes

display_name: solid-state-electrolytes

thermoelectricity

display_name: thermoelectricity

transport

display_name: transport


en: This study combines machine learning (ML) and high-throughput calculations to uncover new ternary electrides in the A₂BC₂ family of compounds with the P4/mbm space group. Starting from a library of 214 known A₂BC₂ phases, density-functional theory calculations were used to compute the maximum value of the electron localization function, indicating that 42 are potential electrides. A model was then trained on this dataset and used to predict the electride behaviour of 14,437 hypothetical compounds generated by structural prototyping. Then, the stability and electride features of the 1254 electride candidates predicted by the model were carefully checked by high-throughput calculations.

materialscloud

en: Materials Cloud

en: Machine learning-accelerated discovery of A₂BC₂ ternary electrides with diverse anionic electron densities

density-functional-theory

display_name: density-functional-theory

electrides

display_name: electrides

machine-learning

display_name: machine-learning


en:
A comprehensive study of the electronic and structural phase transition from 1T` to Td in the bulk Weyl semimetal Mo1-xWxTe2 at different doping concentrations has been carried out using time-of-flight momentum microscopy (including circular and linear dichroism), X-ray photoelectron spectroscopy, X-ray photoelectron diffraction, X-ray diffraction (XRD), angle-resolved Raman spectroscopy, transport measurements (including longitudinal elastoresistance), density functional theory (DFT) and Kikuchi pattern calculations. High-resolution, angle-resolved photoemission spectroscopy at 20 K reveals surface electronic states, which are indicative for topological Fermi arcs. Their dispersion agrees with the position of Weyl points predicted by DFT calculations based on the precise crystal structure of our samples obtained from XRD measurements. Raman spectroscopy confirms the inversion symmetry breaking for the Td-phase, which is a necessary condition for the emergence of topological states. Transport measurements show that increasing the doping concentration from 2 to 9 % leads to an increase in the temperature of the phase transition from 1T` to Td from 230 K to 270 K. Magnetoresistance and longitudinal elastoresistance show significantly increased values in the Td-phase.
This dataset contains the raw data discussed in the manuscript with the same title.

en: Doping-Induced Electronic and Structural Phase Transition in the Bulk Weyl semimetal Mo1-xWxTe2

envi

en: Environment

alloy

display_name: alloy

cpa

display_name: cpa

dft

display_name: dft

kkr

display_name: kkr

tmdc

display_name: tmdc

weyl-semimetal

display_name: weyl-semimetal


en: Two-dimensional (2D) materials are among the most promising candidates for beyond silicon electronic and optoelectronic applications. Recently, their recognized importance, sparked a race to discover and characterize new 2D materials. Within few years the number of experimentally exfoliated or synthesized 2D materials went from a couple of dozens to few hundreds while the number theoretically predicted compounds reached a few thousands. In 2018 we first contributed to this effort with the identification of 1825 compounds that are either easily (1036) or potentially (789) exfoliable from experimentally known 3D compounds. In the present work we report on the new materials recently added to the 2D-portfolio thanks to the extension of the screening to an additional experimental database (MPDS) as well as the most up-to-date versions of the two databases (ICSD and COD) used in our previous work. This expansion led to the discovery of an additional 1252 unique monolayers bringing the total to 3077 compounds and, notably, almost doubling the number of easily exfoliable materials (2004). Moreover, we optimized the structural properties of all the materials (regardless of their binding energy or number of atoms in the unit cell) as isolated mono-layer and explored their electronic band structure. This archive entry contains the database of 2D materials in particular it contains the structural parameters for all the 3077 structures of the global Material Cloud 2D database as extracted from their bulk 3D parent, 2710 optimized 2D structures and 2345 electronic band structure together with the provenance of all data and calculations as stored by AiiDA.

en: The Materials Cloud 2D database (MC2D)

electronic-structure

display_name: electronic-structure

high-throughput

display_name: high-throughput


en: Chiral ligands are important components in asymmetric homogeneous catalysis, but their synthesis and screening can be both time-consuming and resource-intensive. Data-driven approaches, in contrast to screening procedures based on intuition, have the potential to reduce the time and resources needed for reaction optimization by more rapidly identifying an ideal catalyst. These approaches, however, are often non-transferable and cannot be applied across different reactions. To overcome this drawback, we introduce a general featurization strategy for bidentate ligands that is coupled with an automated feature selection pipeline and Bayesian ridge regression to perform multivariate linear regression modeling. This approach, which is applicable to any reaction, incorporates electronic, steric, and topological features (rigidity/flexibility, branching, geometry, constitution) and is well-suited for early-stage ligand optimization. Using only small datasets, our workflow capably predicts the enantioselectivity of four metal-catalyzed asymmetric reactions. Uncertainty estimates provided by Bayesian ridge regression permit the use of Bayesian optimization to efficiently explore pools of prospective new ligands. Finally, we constructed the BDL-Cu-2023 dataset, comprised of 312 bidentate ligands extracted from the CSD, and screened it with this procedure to identify promising ligand candidates for a challenging asymmetric oxy-alkynylation reaction.

en: Reaction-agnostic featurization of bidentate ligands for Bayesian ridge regression of enantioselectivity

bidentate-ligands

display_name: bidentate-ligands

catalysis

display_name: catalysis

epfl

display_name: epfl

homogenous-catalysis

display_name: homogenous-catalysis

ligands

display_name: ligands

nccr-catalysis

display_name: nccr-catalysis


en: The C-F activation is one of the important processes in chemical synthesis. Here, we studied the hydrodefluorination of PhCF3 with SiMe2Ph-H catalyzed by Ni(0) complexes. The mechanisms involve three main steps: C-F bond cleavage of PhCF3 on the nickel complex, transmetalation of Ni-F with SiMe2Ph-H to form a nickel hydride complex, and C-H reductive elimination of PhCF2H. We performed density functional calculations on nickel complexes with thirty carbene and phosphine ligands to obtain the relative free energy profiles. Then, linear free energy scaling relationships were determined and molecular volcano plots were constructed. To accurately describe catalytic activity, we found that multiple reference states must be considered.  Thus, the concept of "reference-generalized volcano plots (RGVPs)" was introduced to assist with the selection of the appropriate reference state to determine catalytic activity. Our regression models indicate that electronic properties of ligands significantly influence energy descriptors more than steric size, although steric hindrance affects reference state stability. Optimal ligands are highly electron-donating with small-to-moderate steric sizes. This study highlights the importance of selecting appropriate ligands and metal centers to optimize catalytic activity. Examples of input files for density functional calculations in Gaussian16 to obtain relative free energies for all intermediates and transition states, the spreadsheet tables including the calculated relative free energies for generating LFESRs and volcano plots, and all optimized Cartesian coordinates are provided.

en: Computational Design of Transition Metal Catalysts for Hydrodefluorination of Trifluoromethylarenes using Hydrosilane

carbenes

display_name: carbenes

volcano-plot

display_name: volcano-plot


en: The development of spintronic devices demands the existence of materials with some kind of spin splitting (SS). In this work, we have built a database of ab initio calculated SS in 2D materials. More than that, we propose a workflow for materials design integrating an inverse design approach and a Bayesian inference optimization.  We use the prediction of SS prototypes for spintronic applications as an illustrative example of the proposed workflow. The prediction process starts with the establishment of the design principles (the physical mechanism behind the target properties), that are used as filters for materials screening, and followed by density functional theory (DFT) calculations. Applying this process to the C2DB database, we identify and classify 315 2D materials according to SS type at the valence and/or conduction bands. The Bayesian optimization captures trends that are used for the rationalized design of 2D materials with the ideal conditions of band gap and SS for potential spintronics applications. This repository then contains the main source of data generated in this work, which encompasses full information regarding the materials structure and band structure calculations results, and a database of all the identified spin splittings for these compounds, available in multiple formats.

en: High throughput inverse design and Bayesian optimization of functionalities: spin splitting in two-dimensional compounds

bayesian-optimization

display_name: bayesian-optimization

spin-splitting

display_name: spin-splitting

spintronics

display_name: spintronics


en: Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we describe a Bayesian active learning framework for autonomous ``on-the-fly'' training of fast and accurate reactive many-body force fields during molecular dynamics simulations. At each time step, predictive uncertainties of a sparse Gaussian process are evaluated to automatically determine whether additional ab initio training data are needed. We introduce a general method for mapping trained kernel models onto equivalent polynomial models whose prediction cost is much lower and independent of the training set size. As a demonstration, we perform direct two-phase simulations of heterogeneous H2 turnover on the Pt(111) catalyst surface at chemical accuracy. The model trains itself in three days and performs at twice the speed of a ReaxFF model, while maintaining much higher fidelity to DFT and excellent agreement with experiment.

en: Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt

density-functional-theory

display_name: density-functional-theory

machine-learning

display_name: machine-learning

molecular-dynamics

display_name: molecular-dynamics


en:
The effects of the finite size of the simulation box in equilibrium molecular dynamics simulations are investigated for prototypical superionic conductors of different types, namely the fluorite-structure materials PbF2, CaF2, and UO2 (type II), and the alpha phase of AgI (type I). Largely validated empirical force-fields are employed to run ns-long simulations and extract general trends for several properties, at increasing size and in a wide temperature range.
This work shows that, for the considered type-II superionic conductors, the diffusivity dramatically depend on the system size and that the superionic regime is shifted to larger temperatures in smaller cells. Furthermore, only simulations of several hundred atoms are able to capture the experimentally-observed, characteristic change in the activation energy of the diffusion process, occurring at the order-disorder transition to the superionic regime. Finite-size effects on ion diffusion are instead much weaker in alpha-AgI. The thermal conductivity is found generally smaller for smaller cells, where the temperature-independent (Allen-Feldman) regime is also reached at significantly lower temperatures. The finite-size effects on the thermal motion of the non-mobile ions composing the solid matrix follow the simple law which holds for solids.
In this record, I collect the input files employed to run the simulations, the data obtained from the latter and the Jupiter Notebook used to analyze the data.

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).

en: Investigating finite-size effects in computer simulations of superionic materials

marie-curie-fellowship

display_name: marie-curie-fellowship

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

transport

display_name: transport


en: We present a density-functional theory (DFT) study of the structural, electronic, and chemical bonding behavior in germanium (Ge)-doped vanadium dioxide (VO2). Our motivation is to explain the reported increase of the metal-insulator transition temperature under Ge doping and to understand how much of the fundamental physics and chemistry behind it can be captured at the conventional DFT level. We model doping using a supercell approach, with various concentrations and different spatial distributions of Ge atoms in VO2. Our results suggest that the addition of Ge atoms strongly perturbs the high-symmetry metallic rutile phase and induces structural distortions that partially resemble the dimerization of the experimental insulating structure. Our work, therefore, hints at a possible explanation of the observed increase in transition temperature under Ge doping, motivating further studies into understanding the interplay of structural and electronic transitions in VO2.

materialscloud

en: Materials Cloud

en: Influence of germanium substitution on the structural and electronic stability of the competing vanadium dioxide phases

dft

display_name: dft

doping

display_name: doping


en: This dataset includes the computational workflows of a density functional theory based study of the oxygen evolution reaction (OER) on a manganese oxide catalyst in presence and absence of iron dopant. The thermodynamic OER overpotential has been computed by using a surface slab model based on a layered birnessite bulk structure of MnO₂ considering supercells with two and four MnO₂ units and by varying the intercalation with KOH, the amount of Fe dopant and the dopant positions. In addition, the dependence of the oxidation state of the active site atoms (either Mn or Fe) on the directly bound OER intermediate species, has been investigated. The results suggest a decrease of up to 310 mV in the thermodynamic OER overpotential upon doping the considered model structures with Fe that is consistent with the experimentally measured total overpotential decrease of 190 mV.

en: Reactivity of layered manganese oxide toward water oxidation under alkaline conditions in presence and in absence of iron

envi

en: Environment

electrochemical-overpotential

display_name: electrochemical-overpotential

iron-dopant

display_name: iron-dopant

manganese-oxides

display_name: manganese-oxides

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction


en:
We show that, contrary to popular assumptions, predictions from machine learning potentials built upon high-dimensional atom-density representations almost exclusively occur in regions of the representation space which lie outside the convex hull defined by the training set points. We then propose a perspective to rationalise the domain of robust extrapolation and accurate prediction of atomistic machine learning potentials in terms of the probability density induced by training points in the representation space.
The data here contained can be used to reproduce all results and graphs shown in the article. We also include the trajectory files for the Au13 dataset we generate by running molecular dynamics simulations of an Au nanoparticle containing 13 atoms at temperatures of 50K, 100K, 200K, 300K, and 400K. Details regarding the generation of such dataset can be found in the supplementary information file for the article.

en: On the robust extrapolation of high-dimensional machine learning potentials


en: We develop a computationally efficient scheme to accurately determine finite-temperature band gaps. We here focus on materials belonging to the class ABX3 (A = Rb, Cs; B = Ge, Sn, Pb; and X = F, Cl, Br, I), which includes halide perovskites. First, an initial estimate of the band gap is provided for the ideal crystalline structure through the use of a range-separated hybrid functional, in which the parameters are determined nonempirically from the electron density and the high-frequency dielectric constant. Next, we consider two kinds of band-gap corrections to account for spin-orbit coupling and thermal vibrations including zero-point motions. In particular, the latter effect is accounted for through the special displacement method, which consists in using a single distorted configuration obtained from the vibrational frequencies and eigenmodes, thereby avoiding lengthy molecular dynamics. The sequential consideration of both corrections systematically improves the band gaps, reaching a mean absolute error of 0.17 eV with respect to experimental values. The computational efficiency of our scheme stems from the fact that only a single calculation at the hybrid-functional level is required and that it is sufficient to evaluate the corrections at the semilocal level of theory. Our scheme is particularly convenient for large-size systems and for the screening of large databases of materials. This entry provides the ideal atomic structures and the distorted atomic structures at certain temperature including zero-point motions, generated by special displacement method.

en: Accurate and efficient band-gap predictions for metal halide perovskites at finite temperature

band-gap

display_name: band-gap

nuclear-quantum-effects

display_name: nuclear-quantum-effects

perovskites

display_name: perovskites

phonon

display_name: phonon

special-displacement-method

display_name: special-displacement-method

temperature

display_name: temperature


en: We developed and implemented a multi-target multi-fidelity workflow to explore the chemical space of antiperovskite materials with general formula X3BA (X = Li, Na, Mg) and PM-3m space group, searching for stable high-performance solid state electrolytes for all-solid state batteries. The workflow is based on the calculation of thermodynamic and kinetic properties, which include phase and electrochemical stability, semiconducting behaviour, and ionic diffusivity. To accelerate the calculation of the kinetic properties, we use a surrogate model able to predict the transition state structure during ionic diffusion. This reduces the calculation cost by more than one order of magnitude while keeping the mean error within 73 meV compared to the more accurate nudged elastic band method. This method allows us identify 14 materials that agree with the experimentally reported results as some of the best solid state electrolytes. Moreover, this approach is general and chemistry neutral, so can be applied to other battery chemistries and crystal prototypes. We report here the data produced in this work. These results are collected as ASE database. It contains a markdown file that explains the usage and most important data.

en: Accelerated workflow for antiperovskite-based solid state electrolytes

antiperovskites

display_name: antiperovskites

autonomous-workflow

display_name: autonomous-workflow

high-throughput-screening

display_name: high-throughput-screening

neb-calculations

display_name: neb-calculations

solid-state-electrolytes

display_name: solid-state-electrolytes

surrogate-models

display_name: surrogate-models


en: The chalcopyrite Cu(In,Ga)S2 has gained renewed interest in recent years due to its potential application in tandem solar cells. In this contribution, a combined theoretical and experimental approach is applied to investigate stable and metastable phases forming in sputtered CuInS2 (CIS) thin films. Ab initio calculations are performed to obtain formation energies, X-ray diffraction patterns, and Raman spectra of various CIS polytypes and related compounds. Multiple low-energy CIS structures with zinc-blende and wurtzite-derived lattices are identified and their XRD/Raman patterns are shown to contain many overlapping features, which could lead to misidentification unless the techniques are duly combined and analyzed. The results are verified against experimental XRD/Raman spectra measured on a series of CIS films with different compositions and treated at different temperatures, revealing the formation of several CIS polymorphs and secondary phases. The characteristic features and the mechanisms behind the formation of different phases are discussed with the focus on the thin-film photovoltaic application of CIS. The dataset contains structures and VASP output files used to derive the discussed trends.

en: Experimental and theoretical study of stable and metastable phases in sputtered CuInS2

disorder

display_name: disorder

polymorphs

display_name: polymorphs

polytypes

display_name: polytypes

simulated-powder-x-ray-diffraction

display_name: simulated-powder-x-ray-diffraction

simulated-raman-spectra

display_name: simulated-raman-spectra

wurtzite

display_name: wurtzite

zinc-blende

display_name: zinc-blende


en:
Dynamical perturbations modify the states of classical systems in surprising ways and give rise to important applications in science and technology. For example, Floquet engineering exploits the possibility of band formation in the frequency domain when a strong, periodic variation is imposed on parameters such as spring constants. We describe here Kapitza engineering, where a drive field oscillating at a frequency much higher than the characteristic frequencies for the linear response of a system changes the potential energy surface so much that maxima found at equilibrium become local minima, in precise analogy to the celebrated Kapitza pendulum where the unstable inverted configuration, with the mass above rather than below the fulcrum, actually becomes stable. Our starting point is a quantum field theory of the Ginzburg-Devonshire type, suitable for many condensed matter systems, including particularly ferroelectrics and quantum paralectrics such as the common substrate (for oxide electronics) strontium titanate (STO). We show that an off-resonance oscillatory electric field generated by a laser-driven THz source can induce ferroelectric order in the quantum-critical limit. Heating effects are estimated to be manageable using pulsed radiation; "hidden" radiation-induced order can persist to low temperatures without further pumping due to stabilization by strain. We estimate the Ginzburg-Devonshire free-energy coefficients in STO using density functional theory (DFT) and the stochastic self-consistent harmonic approximation accelerated by a machine learned force field. Although we find that STO is not an optimal choice for Kapitza stabilization, we show that scanning for further candidate materials can be performed at the computationally convenient density functional theory level. We suggest second-harmonic-generation, soft-mode-spectroscopy, and X-ray-diffraction experiments to characterize the induced order.

This Materials Cloud entry contains the DFT calculations, the force field and results from SSCHA calculations used in the article.

en: Kapitza stabilization of quantum critical order

dft

display_name: dft

quantum-critical-order

display_name: quantum-critical-order

sscha

display_name: sscha

swissuniversities

display_name: swissuniversities


en: Using density functional theory calculations with spin-orbit coupling (SOC), we report on the temperature-dependent thermodynamical properties of Pb: electrical resistivity, thermal expansion (TE), heat capacity, bulk modulus and its pressure derivative. For the former, we employed the state-of-the-art ab initio Boltzmann Transport Equation formalism, and we calculated the effect of TE. In accordance with previous work, we show that SOC improves the description of the phonon dispersion and the resistivity. We argue that this is caused by a joint mutual effect of an increase in the electronic nesting and an increase in the electron-phonon coupling. Interestingly, including TE incorporates non-linearity into the resistivity at high temperatures, whose magnitude depends on whether SOC is included or not. We suggest that mechanisms beyond the quasi-harmonic approximation should be considered to get a better description of Pb with SOC at high temperatures.

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).

en: Effects of spin-orbit coupling and thermal expansion on the phonon-limited resistivity of Pb from first principles

ab-initio

display_name: ab-initio

density-functional-theory

display_name: density-functional-theory

electron-phonon-coupling

display_name: electron-phonon-coupling

electronic-structure

display_name: electronic-structure

first-principles

display_name: first-principles

phonons

display_name: phonons

quantum-espresso

display_name: quantum-espresso

transport

display_name: transport


en:
Machine-learning potentials are usually trained on the ground-state, Born-Oppenheimer energy surface, which depends exclusively on the atomic positions and not on the simulation temperature. This disregards the effect of thermally excited electrons, that is important in metals, and essential to the description of warm dense matter. An accurate physical description of these effects requires that the nuclei move on a temperature-dependent electronic free energy. We propose a method to obtain machine-learning predictions of this free energy at an arbitrary electron temperature using exclusively training data from ground-state calculations, avoiding the need to train temperature-dependent potentials, and benchmark it on metallic liquid hydrogen at the conditions of the core of gas giants and brown dwarfs. This Letter demonstrates the advantages of hybrid schemes that use physical consideration to combine machine-learning predictions, providing a blueprint for the development of similar approaches that extend the reach of atomistic modeling by removing the barrier between physics and data-driven methodologies.

This record contains the raw outputs of the DFT calculations done on the training set. These files are denoted by the "training set folder #*" description. The record also contains a minimal working example showing the ML workflow for training the data and how to run the MD simulations. We include the training data in the format of XYZ files for the structures and NumPy arrays for the DFT energies, forces and DOS. We also provide a Chemiscope visualisation file of the training set.

materialscloud

en: Materials Cloud

en: Predicting hot-electron free energies from ground-state data

hot-electrons

display_name: hot-electrons

liquid-hydrogen

display_name: liquid-hydrogen

machine-learning

display_name: machine-learning

marie-curie-fellowship

display_name: marie-curie-fellowship

marvel

display_name: marvel

snsf

display_name: snsf

warm-dense-matter

display_name: warm-dense-matter


en: AiiDA calculations for the publication Giant hardening response in AlMgZn(Cu) alloys. This study presents a thermomechanical processing concept which is capable of exploiting the full indus- trial application potential of recently introduced AlMgZn(Cu) alloys. The beneficial linkage of alloy design and processing allows not only to satisfy the long-standing trade-off between high mechanical strength in use and good formability during processing but also addresses the need for economically feasible processing times. After an only 3-hour short pre-aging treatment at 100 °C, the two investigated alloys, based on commercial EN AW-5182 and modified with additions of Zn and Zn + Cu respectively, show high formability due to increased work-hardening. Then, these alloys exhibit a giant hardening response of up to 184 MPa to reach a yield strength of 410 MPa after a 20-minute short final heat treatment at 185 °C, i.e. paint-baking. This rapid hardening response strongly depends on the number density, size distribution and constitution of precursors acting as preferential nucleation sites for T-phase precursor precipitation during the final high-temperature aging treatment and is significantly increased by the addition of Cu. Minor deformation (2%) after pre-aging and before final heat treatment further enhances the development of hardening precipitates additionally by activating dislocation-supported nucleation and growth. Tensile testing, quantitative and analytical electron-microscopy methods, atom probe analysis and DFT calculations were used to characterize the alloys investigated in this work over the thermomechanical processing route. The influence of pre-strain on the hardening response and the role of Cu additions in early-stage cluster nucleation are discussed in detail and supported by in-situ STEM experiments and first-principles calculations.

en: DFT data for giant hardening response in AlMgZn(Cu) alloys

envi

en: Environment

aluminum

display_name: aluminum

metallurgy

display_name: metallurgy


en: Carbon is often used as a conductive additive in catalyst layers to increase conductivity and catalytic activity. However, the effect of carbon addition to perovskites on the oxygen reduction (ORR) and oxygen evolution (OER) reactions is convoluted. In this work, composites of perovskite Ba0.5Sr0.5Co0.8Fe0.2O3-δ (BSCF) and conductive additives, carbon and indium doped tin oxide are compared. It is found that the conductive additives have differing effects on the ORR and OER activities and cobalt redox behavior, with carbon having a much more significant effect. In order to elucidate further these differences between BSCF and BSCF/Carbon, operando X-ray absorption spectroscopy (XAS) is measured simultaneously with cyclic voltammetry into the ORR and OER regions and the continuous changes in the Co oxidation state are observed with high time resolution. We theorize that carbon is enhancing the Co redox activity and as a result, the ORR and OER activities are likewise improved.

en: Influence of carbon on the dynamic changes in Co oxidation state of Ba0.5Sr0.5Co0.8Fe0.2O3-δ perovskite catalyst during the oxygen reduction and evolution reactions

alkaline

display_name: alkaline

bscf

display_name: bscf

cobalt

display_name: cobalt

electrocatalysis

display_name: electrocatalysis

oer

display_name: oer

orr

display_name: orr

xas

display_name: xas


en: Strengthening by needle-shaped β′′ precipitates is critical in Al–Mg–Si alloys. Here, the strengthening is studied computationally at the peak-aged condition where precipitate shearing and Orowan looping are usually considered to have equal strengths. Pseudo-random precipitate microstructures are constructed based on experimental precipitate dimensions and volume fractions at peak aging. A Discrete Dislocation Dynamics method is then adapted to compute the Critical Resolved Shear Stress (CRSS) for Orowan looping of dislocations moving through the non-shearable precipitate field. The CRSS for Orowan looping is determined by a typical in-situ precipitate spacing that is smaller than the average spacing and by the dislocation core energy within a radius of ≈5b, a factor rarely considered. The matrix misfit stresses, volume fraction, and precipitate shape have small effects on the CRSS. With microstructure and property details introduced as faithfully as possible, the CRSS for Orowan looping using atomistically-calibrated core energies at room temperature is nonetheless ≈33% higher than experiments. This suggests that precipitate shearing controls strength, and analyses of (i) forces acting on the precipitates, (ii) misfit stresses inside the precipitates, (iii) first-principles results for the relevant precipitate fault energies, and (iv) simulations that mimic precipitate shearing indicate a shearing CRSS closer to experiments. Thus, Orowan looping only sets an upper bound for the CRSS even at peak aging, and further quantitative progress requires detailed modeling of precipitate shearing.

en: Modeling peak-aged precipitate strengthening in Al-Mg-Si alloys

discrete-dislocation-dynamics

display_name: discrete-dislocation-dynamics

orowan-mechanism

display_name: orowan-mechanism

precipitation-strengthening

display_name: precipitation-strengthening


en: Structure determination of amorphous materials remains challenging, owing to the disorder inherent to these materials. Nuclear magnetic resonance (NMR) powder crystallography is a powerful method to determine the structure of molecular solids, but disorder leads to both a high degree of overlap between measured signals, resulting in challenges for spectral assignment, and prevents the unambiguous identification of a single modelled periodic structure as representative of the whole material. Here, we determine the atomic-level ensemble structure of the amorphous form of the drug AZD4625 by combining solid-state NMR experiments with molecular dynamics (MD) simulations and machine-learned chemical shifts. By considering the combined shifts of all 1H and 13C atomic sites in the molecule, we determine the structure of the amorphous form by identifying an ensemble of local molecular environments that are in agreement with experiment. We then extract preferred conformations and intermolecular interactions in the amorphous sample, and analyze the structure in terms of the hydrogen bonding and conformational factors that stabilize the amorphous form of the drug.

en: Atomic-level structure determination of amorphous molecular solids by NMR

amorphous-solids

display_name: amorphous-solids

machine-learning

display_name: machine-learning

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

nmr

display_name: nmr

structure-determination

display_name: structure-determination


en: We present a large dataset of density functional theory calculations of thiophene (C4H4S) adsorbed on metallic and bimetallic (100) surfaces performed in Quantum Espresso.  In total 1131 different surfaces were explored resulting in 2235 total calculations.  Slabs were built from 37 different possible elements in the stoichiometry of A3B, Ll2 structures.  The 37 single element slabs, A1 structures, were included as well.  Slabs were cleaved in the (100) direction.  Two hollow adsorption sites were chosen to be explored over bimetallic surfaces and one hollow configuration for the single metal surfaces.   Bader charge analysis files are also presented.

en: High-throughput thiophene adsorption calculations on bimetallic surfaces

chemisorption

display_name: chemisorption

dft

display_name: dft

heterogenous-catalysis

display_name: heterogenous-catalysis

surfaces

display_name: surfaces

thiophene

display_name: thiophene


en:
Alloys composed of several elements in roughly equimolar composition, often referred to as high-entropy alloys, have long been of interest for their thermodynamics and peculiar mechanical properties, and more recently for their potential application in catalysis. They are a considerable challenge to traditional atomistic modeling, and also to data-driven potentials that for the most part have memory footprint, computational effort and data requirements which scale poorly with the number of elements included. We apply a recently proposed scheme to compress chemical information in a lower-dimensional space, which reduces dramatically the cost of the model with negligible loss of accuracy, to build a potential that can describe 25 d-block transition metals. The model shows semi-quantitative accuracy for prototypical alloys and is remarkably stable when extrapolating to structures outside its training set.
In this record, we provide a dataset containing 25,000 structures utilized for fitting the aforementioned potential, with a focus on 25 d-block transition metals, excluding Tc, Cd, Re, Os and Hg.

en: Modeling high-entropy transition-metal alloys with alchemical compression: dataset HEA25

alchemical-compression

display_name: alchemical-compression

high-entropy-alloys

display_name: high-entropy-alloys

marvel

display_name: marvel

neural-network-potential

display_name: neural-network-potential


en: We perform a large scale study of conventional superconducting materials using a machine-learning accelerated high-throughput workflow. We start by creating a comprehensive dataset of around 7000 electron-phonon calculations performed with reasonable convergence parameters. This dataset is then used to train a robust machine learning model capable of predicting the electron-phonon and superconducting properties based on structural, compositional, and electronic ground-state properties. Using this machine, we evaluate the transition temperature (Tc) of approximately 200000 metallic compounds, all of which on the convex hull of thermodynamic stability (or close to it) to maximize the probability of synthesizability. Compounds predicted to have Tc values exceeding 5 K are further validated using density-functional perturbation theory. As a result, we identify 541 compounds with Tc values surpassing 10 K, encompassing a variety of crystal structures and chemical compositions. This work is complemented with a detailed examination of several interesting materials, including nitrides, hydrides, and intermetallic compounds. Particularly noteworthy is LiMoN2, which we predict to be superconducting in the stoichiometric trigonal phase, with a Tc exceeding 38 K. LiMoN2 has been previously synthesized in this phase, further heightening its potential for practical applications.

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).

en: Sampling the materials space for conventional superconducting compounds

density-functional-perturbation-theory

display_name: density-functional-perturbation-theory

electron-phonon-coupling

display_name: electron-phonon-coupling

high-throughput

display_name: high-throughput

superconductivity

display_name: superconductivity


en:
We present an orbital-resolved extension of the Hubbard U correction to density-functional theory (DFT). Compared to the conventional shell-averaged approach, the prediction of energetic, electronic and structural properties is strongly improved, particularly for compounds characterized by both localized and hybridized states in the Hubbard manifold. The numerical values of all Hubbard parameters are readily obtained from linear-response calculations. The relevance of this more refined approach is showcased by its application to bulk solids pyrite (FeS₂) and pyrolusite (β-MnO₂), as well as to six Fe(II) molecular complexes. Our findings indicate that a careful definition of Hubbard manifolds is indispensable for extending the applicability of DFT+U beyond its current boundaries. The present orbital-resolved scheme aims to provide a computationally undemanding yet accurate tool for electronic structure calculations of charge-transfer insulators, transition-metal (TM) complexes and other compounds displaying significant orbital hybridization.
This dataset contains all Quantum ESPRESSO input and output files as well as all pseudopotentials that were used to generate the results of this study. Moreover, an ``EXAMPLES'' folder provides guidance on how to apply the LR-cDFT approach to evaluate orbital-resolved DFT+U parameters in practise.

materialscloud

en: Materials Cloud

en: Orbital-resolved DFT+U for molecules and solids

cdft

display_name: cdft

constrained-dft

display_name: constrained-dft

dftu

display_name: dftu

dftuv

display_name: dftuv

hubbard

display_name: hubbard

linear-response

display_name: linear-response

lr-cdft

display_name: lr-cdft

orbital-resolved

display_name: orbital-resolved

quantum-espresso

display_name: quantum-espresso


en: We use first-principles density functional theory to investigate how the polar distortion is affected by doping in multiferroic hexagonal yttrium manganite, h-YMnO₃. While the introduction of charge carriers tends to suppress the polar distortion in conventional ferroelectrics, the behavior in improper geometric ferroelectrics, of which h-YMnO₃ is the prototype, has not been studied to date. Using both background charge doping and atomic substitution, we find an approximately linear dependence of the polar distortion on doping concentration, with hole doping reducing and electron doping enhancing it. We show that this behavior is a direct consequence of the improper geometric nature of the ferroelectricity. In addition to its doping effect, atomic substitution can further suppress or enhance the polar distortion through changes in the local chemistry and geometry.

en: Interplay between ferroelectricity and metallicity in hexagonal YMnO₃

envi

en: Environment

erc

display_name: erc

ethz

display_name: ethz