en:
The electronic structure, interatomic bonding, and mechanical properties of two supercell models of Ni-based super alloys are calculated by using ab initio density functional theory methods. The alloys are in the face centered cubic lattice having 864 atoms and eleven elements: Haynes282 (Ni₄₄₄Cr₂₀₂Co₇₉Al₅₆Ti₂₅Mo₂₅Fe₁₂Mn₃Si₅C₁₂B) and Inconel740 (Ni₃₇₃Cr₂₄₆Co₁₅₃Al₃₃Ti₂₁Nb₆MoFe₆
Mn₃Si₁₇C₅). These multi-component alloys have very complex electronic structure, bonding and partial charge distributions depending on the composition and strength of local bonding environment. We employ the novel concept of total bond order density (TBOD) and its partial components (PBOD) to ascertain the internal cohesion that controls the intricate balance between the propensity of metallic bonding between Ni, Cr and Co, and the strong bonds with C and Al. We find Inconel740 has slightly stronger mechanical properties than Haynes282. Comparison with more conventional high entropy alloys with equal components are discussed.

en: Ab initio simulation of structure and properties in Ni-based superalloys Haynes282 and Inconel740

effective-charge

display_name: effective-charge


$oid: OBSOLETE

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


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: In industrial metal forming processes, the generation of datasets for inline and optical quality assessment is expensive and time-consuming. Within the research project SimKI, conventional metal forming plants were digitalized under use of perception-based sensors in combination with a completely redesigned forming tool. The integration of optical quality observation methods connected with a retrofitting approach of the press tool provides the opportunity to generate an information-feedback loop that predicts part defects prior to their occurrence. The SimKI-method additionally combines conventional statistical measurement methods with AI-based defect detection algorithms that are trained by a) generic datasets of a finite-element simulation, b) real component images of a 3D imaging device, and c) a combination of both. The generated datasets are used to accelerate the training of a DNN-based algorithm in order to identify the position and deviation from the agreed quality. The high degree of innovation is based on obtaining real-time component quality information under use of AI-based optical quality assessment, which in turn provides information to the control algorithm of the smart forming tool.

en: Recursive quality optimization of a smart forming tool under use of perception based hybrid datasets for training of a deep neural network

artificial-intelligence

display_name: artificial-intelligence

defect-detection

display_name: defect-detection

gocator

display_name: gocator


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: In this record we provide the data to support our recent finding on surface catalyzed cycloaromatization. Immobilization of organic building blocks on metal surfaces and their coupling via thermally induced C-C bond formations are developing as an important addition to the toolbox of organic and polymer synthesis. Additional advantages of this technique are the in situ monitoring of the reaction by scanning probe methods and the accessibility of insoluble and reactive carbon nanostructures. The diversity of conceivable products, however, sensitively depends on the number of available on-surface reactions. In the manuscript where the results are discussed, we introduce an unprecedented example, the intermolecular oxidative coupling of isopropyl substituents of arenes. With a new phenylene ring being formed, this [3+3] dimerization can be regarded as a formal cycloaromatization. The synthetic value of this novel reaction is proven by the synthesis of polyarylenes and co-polyarylenes, which we demonstrate by synthesizing poly(2,7-pyrenylene-1,4-phenylene). Scanning tunneling microscopy and non-contact atomic force microscopy studies complemented by density functional theory calculations are employed to obtain mechanistic insights into the title reaction.

en: On-surface polyarylene synthesis by cycloaromatization of isopropyl substituents

ab-initio

display_name: ab-initio

catalysis

display_name: catalysis

on-surface-synthesis

display_name: on-surface-synthesis


snsf

display_name: snsf


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

epfl

display_name: epfl

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


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: 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: Materials Cloud

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


envi

en: Environment


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.

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


snsf

display_name: snsf


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.

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

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


max

display_name: max


en: CeO₂ greatly enhances the electrocatalytic oxygen evolution reaction (OER) activity of CoOx, though the enhancement mechanism beyond this synergy is yet to be understood. Here, operando hard X-ray adsorption spectroscopy (hXAS) is applied to monitor the Co K edge and Ce L₃ edge in CoOx/CeO₂ to shed light on the evolution of the Co and Ce oxidation states during OER. In addition, ex situ soft XAS (sXAS) characterizations provide information on the irreversible surface-specific transformations of the Co L₃ edge as well as of the O K edge. Combining the operando and ex situ spectroscopic characterizations with comprehensive electrochemical analyses, we confirm CeO₂ is not the active center for the OER. However, coupling CeO₂ with CoOx introduces significant modifications in the Co and O species at the CoOx surface and alters the flat band potential (Efb), leading to more favorable Co oxidation state transformations during OER and possibly modifying the preferential reaction pathway. This work establishes the connections between electronic structures, Co oxidation state and the OER reaction mechanism for CoOx/CeO₂ composites electrodes.

en: Operando tracking the interactions between CoOx and CeO₂ during oxygen evolution reaction

co-oxidation

display_name: co-oxidation

co-redox

display_name: co-redox

operando-xas

display_name: operando-xas

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction


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


electronic-structure

display_name: electronic-structure


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


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: 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


materialscloud

en: Materials Cloud


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

envi

en: Environment

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.

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.

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

cscs

display_name: cscs

dftu

display_name: dftu

dftuv

display_name: dftuv

phonons

display_name: phonons

raman-spectra

display_name: raman-spectra


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: 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.

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: 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


materialscloud

en: Materials Cloud


density-functional-theory

display_name: density-functional-theory


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


envi

en: Environment


en:
Metal-organic frameworks (MOF) and covalent organic frameworks (COFs) are promising nanocarriers for targeted drug delivery. For their uptake and release, non-covalent interactions between the framework and the drugs play a fundamental role. However, an in-depth understanding of how different functional groups affect these interactions is still lacking. Using a multilevel approach combining molecular docking and density functional theory, we show in the publication associated with this data how computational modeling can be exploited to gain information on the interplay between functionalization and drug delivery features. We find that functional groups significantly impact the strength of the host-guest interactions. Moreover, the interaction strength qualitatively correlates with drug release data from experimental literature, providing a link between framework structure and release properties. The results of our study facilitate the customization of non-covalent interactions between MOFs/COFs and drugs and give direction on how to rationally design nanocarriers for sustaining the drug release in the body over longer times.
The data deposited on the Materials Cloud Archive contains the inputs and outputs of the molecular docking and DFT simulations.

en: Engineering host-guest interactions in organic framework materials for drug delivery

covalent-organic-frameworks

display_name: covalent-organic-frameworks

drug-delivery

display_name: drug-delivery

metal-organic-frameworks

display_name: metal-organic-frameworks


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.

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: 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: 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.

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


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: 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: Materials Cloud

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

electronic-structure

display_name: electronic-structure

molecular-dynamics

display_name: molecular-dynamics

nuclear-quantum-effects

display_name: nuclear-quantum-effects

water

display_name: water


envi

en: Environment


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.

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


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: 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.

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


materialscloud

en: Materials Cloud


en: We present evidence of a strong circular photon drag effect (PDE) in topological insulators (TIs) through the observation of threefold rotationally symmetric helicity-dependent topological photocurrents using THz spectroscopy in epitaxially-grown Bi₂Se₃ with reduced crystallographic twinning. We establish how twinned domains introduce competing nonlinear optical (NLO) responses inherent to the crystal structure that obscure geometry-sensitive optical processes through the introduction of a spurious mirror symmetry. Minimizing the twinning defect reveals strong NLO response currents whose magnitude and direction depend on the alignment of the excitation to the crystal axes and follow the threefold rotational symmetry of the crystal. Notably, photocurrents arising from helical light reverse direction for left/right circular polarizations and maintain a strong azimuthal dependence—a result uniquely attributable to the circular PDE, where the photon momentum acts as an applied in-plane field stationary in the laboratory frame. Our results demonstrate new levels of control over the magnitude and direction of photocurrents in TIs and that the study of single-domain films is crucial to reveal hidden phenomena that couple topological order and crystal symmetries.

en: Emergence of threefold symmetric helical photocurrents in epitaxial low twinned Bi₂Se₃

envi

en: Environment

electronic-structure

display_name: electronic-structure

experimental

display_name: experimental

photon-drag-effect

display_name: photon-drag-effect

spectroscopy

display_name: spectroscopy

terahertz

display_name: terahertz

topological-materials

display_name: topological-materials


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

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)

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: A catalyst possessing a broad substrate scope, in terms of both turnover and enantioselectivity, is sometimes called "general". Despite their great utility in asymmetric synthesis, truly general catalysts are difficult or expensive to discover via traditional high-throughput screening and are, therefore, rare. Existing computational tools accelerate the evaluation of reaction conditions from a pre-defined set of experiments to identify the most general ones, but cannot generate entirely new catalysts with enhanced substrate breadth. For these reasons, we report an inverse design strategy based on the open-source genetic algorithm NaviCatGA and on the OSCAR database of organocatalysts to simultaneously probe the catalyst and substrate scope and optimize generality as primary target. We apply this strategy to the Pictet–Spengler condensation, for which we curate a database of 820 reactions, used to train statistical models of selectivity and activity. Starting from OSCAR, we define a combinatorial space of millions of catalyst possibilities, and perform evolutionary experiments on a diverse substrate scope that is representative of the whole chemical space of tetrahydro-β-carboline products. While privileged catalysts emerge, we show how genetic optimization can address the broader question of generality in asymmetric synthesis, extracting structure–performance relationships from the challenging areas of chemical space.

en: A genetic optimization strategy with generality in asymmetric organocatalysis as primary target

enantioselectivity

display_name: enantioselectivity

generality

display_name: generality

genetic-optimization

display_name: genetic-optimization

machine-learning

display_name: machine-learning

organic-molecules

display_name: organic-molecules

organocatalysis

display_name: organocatalysis


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

electronic-structure

display_name: electronic-structure

volcano-plot

display_name: volcano-plot


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: 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


materialscloud

en: Materials Cloud

high-throughput

display_name: high-throughput


en: We develop a convolutional neural network (NN) approach able to predict the elastic contribution to chemical potential μₑ at the surface of a 2D strained film given its profile h(x). Arbitrary h(x) profiles are obtained by using a Perlin Noise generator and the corresponding μₑ profiles are calculated either by a Green's function approximation (GA) or by Finite Element Method (FEM). First, a large dataset is produced by exploiting the GA method and it is then used for the training of the NN model. The performance of the trained NN is extensively examined, demonstrating its ability to predict μₑ looking both to the training/validation set and to an additional testing set containing different profiles, including sinusoids, gaussians and sharp peaks never considered in the NN training. The NN is then applied to simulate the morphological evolution of strained Ge films, where the predicted μₑ at each integration timestep plays the role of driving force for material redistribution in competition with a surface energy term (proportional to local curvature) and eventually a wetting energy contribution. Both surface diffusion and evaporation/condensation dynamics are considered and the proposed NN approach is shown to well match the evolution expected by using GA. On this basis, a smaller dataset is built with μₑ profiles calculated by FEM and a new NN model is trained on it. Once again the trained NN-model returns reliable prediction of the FEM μₑ. The findings suggest that the proposed NN-based strategy can be used in replacement of the computationally intensive FEM calculations, enabling the simulation of larger scales and longer time scales untreatable by direct FEM calculation.

en: Deep learning of surface elastic chemical potential in strained films: from statics to dynamics

deep-learning

display_name: deep-learning

elastic-chemical-potential

display_name: elastic-chemical-potential

surface-evolution

display_name: surface-evolution

thin-films

display_name: thin-films


en: Raman spectroscopy is a widely-used non-destructive material characterization method, which provides information about the vibrational modes of the material and therefore of its atomic structure and chemical composition. Raman spectra can be simulated using atomistic first-principles methods but these are computationally demanding and thus the existing databases of computational Raman spectra are fairly small. We developed an optimized workflow to efficiently calculate the Raman tensors, from which the Raman spectra can be straightforwardly simulated. The workflow was benchmarked and validated by comparison to experiments and previous computational methods for select technologically relevant material systems. Using the workflow, we performed high-throughput calculations for a large set of materials (5099) belonging to many different material classes, and collected the results to a database.

en: High-throughput computation of Raman spectra from first principles

envi

en: Environment

density-functional-theory

display_name: density-functional-theory

raman-tensors

display_name: raman-tensors


en: Physics-inspired molecular representations are the cornerstone of similarity-based learning applied to solve chemical problems. Despite their conceptual and mathematical diversity, this class of descriptors shares a common underlying philosophy: they all rely on the molecular information that determines the form of the electronic Schrödinger equation. Existing representations take the most varied forms, from non-linear functions of atom types and positions to atom densities and potential, up to complex quantum chemical objects directly injected into the ML architecture. In this work, we present the Spectrum of Approximated Hamiltonian Matrices (SPAᴴM) as an alternative pathway to construct quantum machine learning representations through leveraging the foundation of the electronic Schrödinger equation itself: the electronic Hamiltonian. As the Hamiltonian encodes all quantum chemical information at once, SPAᴴM representations not only distinguish different molecules and conformations, but also different spin, charge, and electronic states. As a proof of concept, we focus here on efficient SPAᴴM representations built from the eigenvalues of a hierarchy of well-established and readily-evaluated "guess" Hamiltonians. These SPAᴴM representations are particularly compact and efficient for kernel evaluation and their complexity is independent of the number of different atom types in the database

en: SPAᴴM: the spectrum of approximated hamiltonian matrices representations

epfl

display_name: epfl

erc

display_name: erc

guess-hamiltonians

display_name: guess-hamiltonians

molecular-representation

display_name: molecular-representation

snsf

display_name: snsf

spahm

display_name: spahm


machine-learning

display_name: machine-learning


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

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.

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.

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


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: 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

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


materialscloud

en: Materials Cloud


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

density-functional-theory

display_name: density-functional-theory


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: corresponding atomic structures at the certain 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: Accurate and efficient band-gap predictions for metal halide perovskites at finite temperature

envi

en: Environment


en: We address the many-body self-interaction in relation to polarons in density functional theory. Our study provides (i) a unified theoretical framework encompassing many-body and one-body forms of self-interaction and (ii) an efficient semilocal scheme for charge localization. Our theoretical formulation establishes a quantitative connection between the many-body and one-body forms of self-interaction in terms of electron screening, thereby conferring superiority to the concept of many-body self-interaction. Our semilocal methodology involves the use of a weak localized potential and applies equally to electron and hole polarons. We find that polarons free from many-body self-interaction have formation energies that are robust with respect to the functional adopted.

en: Many-body self-interaction and polarons

electron-screening

display_name: electron-screening

many-body-self-interaction

display_name: many-body-self-interaction

mgo

display_name: mgo

one-body-self-interaction

display_name: one-body-self-interaction

polaron-localization

display_name: polaron-localization

polarons

display_name: polarons

self-interaction

display_name: self-interaction


many-body-self-interaction-and-polarons

title_for_slug: many-body-self-interaction-and-polarons


en: The conversion of semimetallic suspended graphene (Gr) to a large-gap semiconducting phase is realized by controlled adsorption of atomic hydrogen (deuterium) on free-standing Gr veils in nanoporous graphene. The effects of local rehybridization from sp² to sp³ chemical bonding are investigated by combining X-ray photoelectron spectroscopy and high-resolution electron energy-loss spectroscopy (HREELS) with ab-initio based modelling. The hydrogen adatoms on the C sites induce a stretching frequency, clearly identified in vibrational spectra thanks to the use of the D isotope, which is compatible with the predicted fingerprints of adsorption on both sides of Gr corresponding to the graphane configuration. HREELS of the deuterated samples shows a wide opening of the optical band gap, consistent with the modified spectral density observed in the valence band photoemission. The results are in agreement with ab-initio calculations by GW and Bethe-Salpeter equation approaches, showing a large quasiparticle gap opening and huge excitonic binding energy

en: Dielectric response and excitations of hydrogenated free-standing graphene

big-map

display_name: big-map

bse

display_name: bse

first-principles

display_name: first-principles

hreels

display_name: hreels

hydrogenated-graphene

display_name: hydrogenated-graphene

max

display_name: max

nanoporous-graphene

display_name: nanoporous-graphene

yambo-code

display_name: yambo-code


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


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:
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


materialscloud

en: Materials Cloud


en: We present a sulfur K-edge X-ray absorption near-edge structure (XANES) database of 18 crystalline and 48 amorphous Lithium-Phosphorous-Sulfur (LPS) compounds. The database contains a total of 2681 XANES spectra of symmetrically inequivalent absorbing S sites. Structures were taken from Materials Cloud entry 2022.17 (archive.materialscloud.org/record/2022.17) and were originally generated by systematically removing Li, P and S atoms from known crystal structures using an evolutionary algorithm and an artificial neural network based interatomic potential. The details of this procedure can be found in Guo et al. (see references below). From this data set, low-energy structures were selected for spectral simulations. The excited electron and core hole method as implemented in VASP 6.2.1 was used to compute the XANES spectra for each symmetrically inequivalent Sulfur atom. The details of the VASP simulations can be found in the associated manuscript.

en: Simulated sulfur K-edge X-ray absorption spectroscopy database of lithium thiophosphate solid electrolytes

amorphous-lps

display_name: amorphous-lps

lps

display_name: lps

vasp

display_name: vasp

x-ray-absorption-spectroscopy

display_name: x-ray-absorption-spectroscopy