en: We investigate the structural, electronic, vibrational, power, and transport properties of the β allotrope of Ga2O3 from first principles. We find phonon frequencies and elastic constants that reproduce the correct band ordering, in agreement with experiment. We use the Boltzmann transport equation to compute the intrinsic electron and hole drift mobility and obtain room-temperature values of 258 and 1.2 cm2/Vs, respectively, as well as 6300 and 13 cm2/Vs at 100 K. Through a spectral decomposition of the scattering contribution to the inverse mobility, we find that multiple longitudinal-optical modes of Bu symmetry are responsible for the electron mobility of β-Ga2O3 but that many acoustic modes also contribute, making it essential to include all scattering processes in the calculations. Using the von Hippel low-energy criterion, we computed the breakdown field to be 5.8 MV/cm at room temperature, yielding a Baliga figure of merit of 1250 with respect to silicon, ideal for high-power electronics. This work presents a general framework to predictively investigate novel high-power electronic materials.

en: Structural, electronic, elastic, power, and transport properties of β-Ga2O3 from first principles

ab-initio

display_name: ab-initio

breakdown-field

display_name: breakdown-field

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

marie-curie-fellowship

display_name: marie-curie-fellowship

prace

display_name: prace

transport

display_name: transport


$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: Osmium complexes with osmium in different oxidation states (II, III, IV, VI) have been reported to exhibit antiproliferative activity in cancer cell lines. Herein, we demonstrate unexplored opportunities offered by 187Os nuclear forward scattering (NFS) and nuclear inelastic scattering (NIS) of synchrotron radiation for characterization of hyperfine interactions and lattice dynamics in a benchmark Os(VI) complex, K₂[OsO₂(OH)₄]. The isomer shift (𝛿 = 3.3(1) mm/s) relative to [OsIVCl₆]2– and quadrupole splitting (𝚫EQ = 12.0(2) mm/s) were determined with NFS. The Lamb-Mössbauer factor (0.80(4)) is estimated, the density of phonon states (DOS) is extracted, and a thermodynamics characterization was carried out using the NIS data combined with first principles calculations. Overall, this study provides evidence that 187Os nuclear resonance scattering is a reliable technique for the investigation of hyperfine interactions and Os specific vibrations in osmium(VI) species, and is thus applicable for such measurements in osmium complexes of other oxidation states, including those with anticancer activity such as Os(III) and Os(IV).

en: 187Os nuclear resonance scattering to explore hyperfine interactions and lattice dynamics for biological applications

hyperfine-interactions

display_name: hyperfine-interactions

lattice-dynamics

display_name: lattice-dynamics

nuclear-resonance-scattering

display_name: nuclear-resonance-scattering


en: Recently, a process has been proposed for generating negatively-curved carbon schwarzites via zeolite-templating (Braun et al., 2018). However, the proposed process leads to atomistic models which are not very symmetric and often rather defective. In the present work, an improved generation approach is developed, by imposing symmetry constraints, which systematically leads to defect-free, hence more stable, schwarzites. The stability of the newly predicted symmetric schwarzites is also compared to that of other carbon nanostructures (in particular carbon nanotubes - CNTs), which could also be accommodated within the same templates. Our results suggest that only a few of these (such as FAU, SBT and SBS) can fit schwarzites more stable than CNTs. Our predictions could help experimentalists in the crucial choice of the template for the challenging synthesis of schwarzites. Furthermore, being highly symmetric and stable phases, the models could also be synthesized by means of other experimental procedures.

en: Modeling symmetric and defect-free carbon schwarzites into various zeolite templates

carbon-nanotubes

display_name: carbon-nanotubes

random-symmetric-generation

display_name: random-symmetric-generation

schwarzites

display_name: schwarzites

zeolite-templated-nanostructures

display_name: zeolite-templated-nanostructures


en:
The electronic density of states (DOS) provides information regarding the distribution of electronic states in a material, and can be used to approximate its optical and electronic properties and therefore guide computational material design. Given its usefulness and relative simplicity, it has been one of the first electronic properties used as target for machine-learning approaches going beyond interatomic potentials. A subtle but important point, well-appreciated in the condensed matter community but usually overlooked in the construction of data-driven models, is that for bulk configurations the absolute energy reference of single-particle energy levels is ill-defined. Only energy differences matter, and quantities derived from the DOS are typically independent on the absolute alignment. We introduce an adaptive scheme that optimizes the energy reference of each structure as part of training, and show that it consistently improves the quality of ML models compared to traditional choices of energy reference, for different classes of materials and different model architectures. On a practical level, we trace the improved performance to the ability of this self-aligning scheme to match the most prominent features in the DOS. More broadly, we believe that this work highlights the importance of incorporating insights into the nature of the physical target into the definition of the architecture and of the appropriate figures of merit for machine-learning models, that translate in better transferability and overall performance.
This record contains all the necessary data files and scripts to support the results presented in the paper with the same title.

en: Adaptive energy reference for machine-learning models of the electronic density of states

density-of-states

display_name: density-of-states

electronic-structure

display_name: electronic-structure

epfl

display_name: epfl

erc

display_name: erc

machine-learning

display_name: machine-learning

marvel

display_name: marvel

snsf

display_name: snsf


en:
A recently developed bottom-up synthesis strategy enables the fabrication of graphene nanoribbons with well-defined width and non-trivial edge structures from dedicated molecular precursors. Here we discuss the synthesis and properties of zigzag nanoribbons (ZGNRs) modified with periodic cove-cape-cove units along their edges. Contrary to pristine ZGNRs, which show antiferromagnetic correlation of their edge states, the edge-modified ZGNRs exhibit a finite single particle band gap without localized edge states. We report the on-surface synthesis of such edge-modified ZGNRs and discuss tunneling conductance dI/dV spectra and dI/dV spatial maps that reveal a noticeable localization of electronic states at the cape units and the
opening of a band gap without presence of edge states of magnetic origin. A thorough ab initio investigation of the electronic structure identifies the conditions under which antiferromagnetically coupled, edge-localized states reappear in the electronic structure. Further modifications of the ribbon structure are proposed that lead to an enhancement of such features, which could find application in nanoelectronics and spintronics.
The record contains input files to reproduce the calculations discussed in the manuscript and the raw data of the experimental images discussed.

en: Graphene nanoribbons with mixed cove-cape-zigzag edge structure

density-functional-theory

display_name: density-functional-theory

graphene-nanoribbons

display_name: graphene-nanoribbons

surface-chemistry

display_name: surface-chemistry


en: Recent theory proposes that edge dislocations in random body-centered cubic (BCC) high entropy alloys have high barriers for motion, conveying high strengths up to high temperatures. Here, the energy barriers for edge motion are computed for two model alloys, NbTaV and MoNbTaW as represented by interatomic potentials, using the Nudged Elastic Band method and compared to theoretical predictions. The average magnitude of the barriers and the average spacing of the barriers along the glide direction agree well with the analytical theory, with no adjustable parameters. The evolution of the barriers versus applied stress is modeled, and the mean strength is in reasonable agreement with the predicted zero-temperature strength. These findings validate the analytic theory. A reduced analytic model based on solute misfit volumes is then applied to Hf-Mo-Nb-Ta-Ti-Zr and Mo-Nb-Ta-Ti-V-W alloys, rationalizing the observed significant strength increases at room temperature and 1000 ∘C upon addition of solutes with large misfit into a base alloy. The analytic theory for edge motion is thus a powerful validated tool for guiding alloy selection.

en: High energy barriers for edge dislocation motion in body-centered cubic high entropy alloys

atomistic-models

display_name: atomistic-models

high-entropy-alloys

display_name: high-entropy-alloys

solute-strengthening

display_name: solute-strengthening


en: Superelasticity, being a reversible nonlinear strain response to stress stimuli beyond the linear elastic regime, is always associated with phase transformations in its host materials, mostly metals or polymers. Theoretical rationale indicates that inorganic materials with covalent/ionic bonding normally have large energy barriers for reversible structural transitions and thus host less opportunity to achieve superelasticity. Here, we demonstrate a directional tensile superelasticity in ceramic crystal GeSe through an unconventional reversible shuffle twinning mechanism instead of martensitic phase transition. We observed, with in-situ mechanical transmission electron microscopy, an evolution in stress‒strain curve from the linear elastic behavior to a nonlinear superelastic plateau, and confirmed that such superelasticity appears simultaneously together with the generation of stripy-shaped twin domains along  orientation. Theoretical calculations revealed that the shuffle twinning process from "Z-shaped" to "anti-Z-shaped" bond-configuration leads to the release of elastic potential energy, being responsible for the emergence of tensile superelasticity therein. Note that such a highly-directional superelasticity prefers to emerge at angles near the zigzag direction owing to the anisotropic Young's modulus and Poisson's ratio in GeSe, and has never been reported in superelastic materials. Our observation provides a novel strategy to exploit tensile superelasticity and nonlinear mechanics for advanced mechanical and flexible electronics.

en: A directional tensile superelasticity in ceramic crystal via reversible shuffle twinning

in-situ-tem

display_name: in-situ-tem

shuffle-twinning

display_name: shuffle-twinning

superelasticity

display_name: superelasticity


en: The electronic density of states (DOS) provides information regarding the distribution of electronic energy levels in a material, and can be used to approximate its optical and electronic properties and therefore guide computational material design. Given its usefulness and relative simplicity, it has been one of the first electronic properties used as target for machine-learning approaches going beyond interatomic potentials. A subtle but important point, well-appreciated in the condensed matter community but usually overlooked in the construction of data-driven models, is that for bulk configurations the absolute energy reference of single-particle energy levels is ill-defined. Only energy differences matter, and quantities derived from the DOS are typically independent on the absolute alignment. We introduce an adaptive scheme that optimizes the energy reference of each structure as part of the training process, and show that it consistently improves the quality of ML models compared to traditional choices of energy reference, for different classes of materials and different model architectures. On a practical level, we trace the improved performance to the ability of this self-aligning scheme to match the most prominent features in the DOS. More broadly, we believe that this work highlights the importance of incorporating insights on the nature of the physical target into the definition of the architecture and of the appropriate figures of merit for machine-learning models, that translate in better transferability and overall performance.


en: Understanding the structure of glassy materials represents a tremendous challenge for both experiments and computations. Despite decades of scientific research, for instance, the structural origin of the density anomaly in silica glasses is still not well understood. Atomistic simulations based on molecular dynamics (MD) produce atomically resolved structure, but extracting insights about the role of disorder in the density anomaly is challenging. Here, we propose to quantify the topological differences between structural arrangements from MD trajectories using a graph-theoretical approach, such that structural differences in silica glasses that exhibit density anomaly can be captured. To balance the accuracy and speed of the MD simulations, we utilized force matching potentials to generate the silica glass structures. This approach involves casting all-atom glass configurations as networks, and subsequently applying a graph-similarity metric (D-measure). Calculated D-measure values are then taken as the topological distances between two configurations. By measuring the topological distances of configurations in silica glass simulated structures across a range of temperatures, distinct structural features could be observed at temperatures higher than the fictive temperature. In addition to quantifying structural changes in the full simulation box, we compared topological distances between local atomic environments in the glass and crystalline silica phases. Evidence from this approach suggests that more coesite-like local structures, which are less symmetric, emerge in silica glasses when density is at a minimum during the heating process.

en: Graph theory-based structural analysis on density anomaly of silica glass

force-matching-potential

display_name: force-matching-potential

graph-theory

display_name: graph-theory

molecular-dynamics

display_name: molecular-dynamics

silica-glass

display_name: silica-glass


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: Materials Cloud


en:
Photocatalytic water splitting has attracted considerable attention for renewable energy production. Since the first reported photocatalytic water splitting by titanium dioxide, this material remains one of the most promising photocatalysts, due to its suitable band gap and band-edge positions. However, predicting both of these properties is a challenging task for existing computational methods. Here we show how Koopmans spectral functionals can accurately predict the band structure and level alignment of rutile, anatase, and brookite TiO₂ using a computationally efficient workflow that only requires (a) a DFT calculation of the photocatalyst/vacuum interface and (b) a Koopmans spectral functional calculation of the bulk photocatalyst. The success of this approach for TiO₂ suggests that this strategy could be deployed for assessing the suitability of novel photocatalyst candidates.
This record contains for each polymorph: (a) a calculation of a slab; (b) KI and pKIPZ band structure calculation of the bulk system.

en: Predicting the suitability of photocatalysts for water splitting using Koopmans spectral functionals: the case of TiO₂ polymorphs

envi

en: Environment

electronic-structure

display_name: electronic-structure

koopmans-spectral--functionals

display_name: koopmans-spectral--functionals

orbital-density-dependent-functionals

display_name: orbital-density-dependent-functionals

photocatalysis

display_name: photocatalysis

water-splitting

display_name: water-splitting


en: Atomistic simulations provide insights into structure-property relations on an atomic size and length scale that are complementary to the macroscopic observables that can be obtained from experiments.  Quantitative predictions, however, are usually hindered by the need to strike a balance between the accuracy of the calculation of the interatomic potential and the modelling of realistic thermodynamic conditions.  Machine-learning techniques make it possible to efficiently approximate the outcome of accurate electronic-structure calculations that can, therefore, be combined with extensive thermodynamic sampling.  We take elemental nickel as a prototypical material, whose alloys have applications from cryogenic temperatures up to close to their melting point and use it to demonstrate how a combination of machine-learning models of electronic properties and statistical sampling methods makes it possible to compute accurate finite-temperature properties at an affordable cost.  We demonstrate the calculation of a broad array of bulk, interfacial, and defect properties over a temperature range from 100 to 2500 K, modelling also, when needed, the impact of nuclear quantum fluctuations and electronic entropy.  The framework we demonstrate here can be easily generalized to more complex alloys and different classes of materials.

en: Finite-temperature materials modeling from the quantum nuclei to the hot electrons regime

cosmo

display_name: cosmo

electronic-density-of-states

display_name: electronic-density-of-states

epfl

display_name: epfl

machine-learning

display_name: machine-learning

materials-science

display_name: materials-science

metal-alloys

display_name: metal-alloys

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

neural-network-potentials

display_name: neural-network-potentials

snsf

display_name: snsf


en:
Modeling ferroelectric materials from first principles is one of the successes of density-functional theory, and the driver of much development effort, requiring an accurate description of the electronic processes and the thermodynamic equilibrium that drive the spontaneous symmetry breaking and the emergence of macroscopic polarization. We demonstrate the development and application of an integrated machine learning (ML) model that describes on the same footing structural, energetic and functional properties of barium titanate (BaTiO₃), a prototypical ferroelectric. The model uses ab initio calculations as reference and achieves accurate yet inexpensive predictions of energy and polarization on time and length scales that are not accessible to direct ab initio modeling. The ML model allows us to thoroughly probe the static and dynamical behavior of BaTiO₃ across its phase diagram, without the need to introduce a coarse-grained description of the ferroelectric transition. Furthermore, we apply the polarization model to calculate dielectric response properties of the material in a fully ab-initio manner.
This archive provides all the relevant data and input files that were used to fit the ML interatomic potential and the polarization model used in this work, along with the relevant Density-Functional Theory calculations that were used for the training set construction and the validation of the ML model. Furthermore, it provides input files and first few snapshots of all the molecular dynamics trajectories needed to investigate the thermodynamics and dielectric properties of BaTiO₃.

en: Thermodynamics and dielectric response of BaTiO₃ by data-driven modeling

dielectric-response

display_name: dielectric-response

ferroelectricity

display_name: ferroelectricity

marvel

display_name: marvel

max

display_name: max

samsung-institute-of-technology-sait

display_name: samsung-institute-of-technology-sait

swissuniversities

display_name: swissuniversities

thermodynamics

display_name: thermodynamics


en: Metal halide perovskites (MHPs) demonstrate an exceptional combination of properties. Rapid progress has extended their application beyond solar cells, light-emitting diodes, photodetectors, and lasers to include memristors, artificial synapse devices, and pressure induced emission. In particular, the vacancy-ordered double perovskite Cs₂TiBr₆ has been identified as a promising material. The effective characterization of MHPs requires accurate and efficient methods for the calculation of electronic structure. Koopmans compliant (KC) functionals are an accurate and computationally efficient alternative to many-body perturbation theory using the GW approximation but have yet only been validated on a small number of simple materials. In this work, KC functionals were applied to the more complex case of Cs₂TiBr₆ and gave a zero-temperature fundamental gap of 4.28 eV, in close agreement with the value of 4.44 eV obtained using the accurate, but more computationally expensive, evGW₀ approach. The temperature-dependent renormalization of the bandgap has also been investigated and found to be significant. Agreement with the experimental optical bandgaps of 1.76–2.0 eV would also require the inclusion of exciton binding energy.

en: Accurate and efficient computation of the fundamental bandgap of the vacancy-ordered double perovskite Cs₂TiBr₆

koopmans-functionals

display_name: koopmans-functionals

metal-halide-perovskites

display_name: metal-halide-perovskites

quasi-particle-self-consistent-gw

display_name: quasi-particle-self-consistent-gw


en: The Fe(CoB)/MgO interface is vital to spintronics as it exhibits the tunneling magnetoresistance (TMR) effect and interfacial perpendicular magnetic anisotropy (PMA) simultaneously. To further enhance TMR and PMA for the development of high-density magnetoresistive random access memory, it is essential to clarify the behavior of Fe atoms interfaced with MgO. This study reveals that the spin and orbital magnetic moments of Fe are enhanced at the Fe/MgO interface. The enhancement in the orbital magnetic moment is much more significant than that predicted by the standard density functional theory. Theoretical calculations based on the orbital polarization correction reproduce this enhancement, the origin of which is attributed to the electron-electron correlation resulting from electron localization at the Fe/MgO interface. The present findings highlight the importance of electro--electron correlation at ferromagnet/oxide interfaces, which has often been disregarded in spintronics, and provide a new perspective in materials design.

en: Electron correlation enhances orbital polarization at a ferromagnetic metal/insulator interface

experimental

display_name: experimental

femgo

display_name: femgo

interface

display_name: interface

magnetic-anisotropy

display_name: magnetic-anisotropy

spintronics

display_name: spintronics


en: With the recent advances in machine learning for quantum chemistry, it is now possible to predict the chemical properties of compounds and to generate novel molecules. Existing generative models mostly use a string- or graph-based representation, but the precise three-dimensional coordinates of the atoms are usually not encoded. First attempts in this direction have been proposed, where autoregressive or GAN-based models generate atom coordinates. Those either lack a latent space in the autoregressive setting, such that a smooth exploration of the compound space is not possible, or cannot generalize to varying chemical compositions. We propose a new approach to efficiently generate molecular structures that are not restricted to a fixed size or composition. Our model is based on the variational autoencoder which learns a translation-, rotation-, and permutation-invariant low-dimensional representation of molecules. Our experiments yield a mean reconstruction error below 0.05 Angstrom, outperforming the current state-of-the-art methods by a factor of four, and which is even lower than the spatial quantization error of most chemical descriptors. The compositional and structural validity of newly generated molecules has been confirmed by quantum chemical methods in a set of experiments.

en: 3DMolNet: a generative network for molecular structures

generative-model

display_name: generative-model

molecules-discovery

display_name: molecules-discovery

three-dimensional-structures

display_name: three-dimensional-structures


snsf

display_name: snsf


en: The precise introduction of nonplanar pores in the backbone of graphene nanoribbon represents a great challenge. In a recent work, we explore a synthetic strategy toward the preparation of nonplanar porous graphene nanoribbon from a predesigned dibromohexabenzotetracene monomer bearing four cove-edges. Successive thermal annealing steps of the monomers indicate that the dehalogenative aryl-aryl homocoupling yields a twisted polymer precursor on a gold surface and the subsequent cyclodehydrogenation leads to a defective porous graphene nanoribbon containing nonplanar [14]annulene pores and five-membered rings as characterized by scanning tunneling microscopy and noncontact atomic force microscopy. Although the C–C bonds producing [14]annulene pores are not achieved with high yield, our results provide new synthetic perspectives for the on-surface growth of nonplanar porous graphene nanoribbons. The record contains data to support the results of our work

en: On-surface synthesis of porous graphene nanoribbons containing nonplanar [14]annulene pores

dft

display_name: dft

graphene-nanoribbons

display_name: graphene-nanoribbons

on-surface-synthesis

display_name: on-surface-synthesis

stm

display_name: stm


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: We unveil the nature of the structural disorder in bottom-up zigzag graphene nanoribbons along with its effect on the magnetism and electronic transport on the basis of scanning probe microscopies and first-principles calculations. We find that edge-missing m-xylene units emerging during the cyclodehydrogenation step of the on-surface synthesis are the most common point defects. These "bite" defects act as spin-1 paramagnetic centers, severely disrupt the conductance spectrum around the band extrema, and give rise to spin-polarized charge transport. We further show that the electronic conductance across graphene nanoribbons is more sensitive to "bite" defects forming at the zigzag edges than at the armchair ones. Our work establishes a comprehensive understanding of the low-energy electronic properties of disordered bottom-up graphene nanoribbons.

en: Materials Cloud

en: Edge disorder in bottom-up zigzag graphene nanoribbons: implications for magnetism and quantum electronic transport

cscs

display_name: cscs

defects

display_name: defects

electronic-transport

display_name: electronic-transport

gnr

display_name: gnr

magnetism

display_name: magnetism

marvel

display_name: marvel

onr

display_name: onr


envi

en: Environment


en: We present an ab initio modeling framework to simulate Majorana transport in 2D semiconducting materials, paving the way for topological qubits based on 2D nanoribbons. By combining density-functional-theory and quantum transport calculations, we show that the signature of Majorana bound states (MBSs) can be found in 2D material systems as zero-energy modes with peaks in the local density-of-states. The influence of spin-orbit coupling and external magnetic fields on Majorana transport is studied for two relevant 2D materials, WSe2 and PbI2. To illustrate the capabilities of the proposed ab initio platform, a device structure capable of hosting MBSs is created from a PbI2 nanoribbon and carefully investigated. These results are compared to InSb nanowires and used to provide design guidelines for 2D topological qubits.

en: Ab initio modeling framework for Majorana transport in 2D materials: towards topological quantum computing

ab-initio

display_name: ab-initio

majorana

display_name: majorana

quantum-transport

display_name: quantum-transport


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

en: Common workflows for computing material properties using different quantum engines

aiida

display_name: aiida

automated

display_name: automated

common-interfaces

display_name: common-interfaces

intersect

display_name: intersect

marvelosp

display_name: marvelosp

max

display_name: max

verification

display_name: verification

workflows

display_name: workflows


en: On-surface synthesis has recently emerged as an effective route towards the atomically precise fabrication of graphene nanoribbons (GNRs) of controlled topologies and widths. However, whether and to what degree structural disorder occurs in the resulting samples is a crucial issue for prospective applications that remains to be explored. Here, we experimentally visualize ubiquitous missing benzene rings at the edges of 9-atom wide armchair nanoribbons that form upon cleavage of phenyl groups in the precursor molecules. These defects are referred to as 'bite' defects. First, we address their density and spatial distribution on the basis of scanning tunnelling microscopy and find that they exhibit a strong tendency to aggregate. Next, we explore their effect on the quantum charge transport from first-principles calculations, revealing that such imperfections substantially disrupt the conduction properties at the band edges. Finally, we generalize our theoretical findings to wider nanoribbons in a systematic manner, hence establishing practical guidelines to minimize the detrimental role of such defects on the charge transport. Overall, our work portrays a detailed picture of 'bite' defects in bottom-up armchair GNRs and assesses their effect on the performance of carbon-based nanoelectronic devices.

en: Quantum electronic transport across 'bite' defects in graphene nanoribbons

cscs

display_name: cscs

defects

display_name: defects

graphene-nanoribbons

display_name: graphene-nanoribbons

snsf

display_name: snsf


electronic-transport

display_name: electronic-transport

gnr

display_name: gnr

marvel

display_name: marvel


en: Electrochemical reduction of CO(2) to value-added chemicals and fuels is a promising strategy to sustain pressing renewable energy demands and address climate change issues. Direct observation of reaction intermediates during the CO(2) reduction reaction will contribute to mechanistic understandings and thus promote the design of catalysts with the desired activity, selectivity, and stability. Herein, we combined in situ electrochemical shell-isolated nanoparticle-enhanced Raman spectroscopy and ab initio molecular dynamics calculations to investigate the CORR process on Cu single-crystal surfaces in various electrolytes. Competing redox pathways and coexistent intermediates of CO adsorption dimerization, oxidation, and hydrogenation, as well as Cu-Oad/Cu-OHad species at Cu-electrolyte interfaces, were simultaneously identified using in situ spectroscopy and further confirmed with isotope-labeling experiments. With AIMD simulations, we report accurate vibrational frequency assignments of these intermediates based on the calculated vibrational density of states and reveal the corresponding species in the electrochemical CO redox landscape on Cu surfaces. Our findings provide direct insights into key intermediates during the reduction of CO(2) and offer a full-spectroscopic tool (40–4,000 cm⁻¹) for future mechanistic studies.

en: In situ spectroelectrochemical probing of CO redox landscape on copper single-crystal surfaces

aimd

display_name: aimd

electrocatalysis

display_name: electrocatalysis

in-situ-raman

display_name: in-situ-raman


aiida

display_name: aiida

automated

display_name: automated

dft

display_name: dft


common-interfaces

display_name: common-interfaces

intersect

display_name: intersect

marvelosp

display_name: marvelosp


en: Crystalline defects can modify quantum interactions in solids, causing unintuitive, even favourable, properties such as quantum Hall effect or superconducting vortex pinning. Here we present another example of this notion—an unexpected unidirectional Kondo scattering in single crystals of 2H-NbS2. This manifests as a pronounced low-temperature enhancement in the out-of-plane resistivity and thermopower below 40 K, hidden for the in-plane charge transport. The anomaly can be suppressed by the c-axis-oriented magnetic field, but is unaffected by field applied along the planes. The magnetic moments originate from layers of 1T-NbS2, which inevitably form during the growth, undergoing a charge-density-wave reconstruction with each superlattice cell (David-star-shaped cluster of Nb atoms) hosting a localized spin. Our results demonstrate the unique and highly anisotropic response of a spontaneously formed Kondo-lattice heterostructure, intercalated in a layered conductor.

en: Unidirectional Kondo scattering in layered NbS2

first-principles-calculation

display_name: first-principles-calculation

vasp

display_name: vasp


en: Charge neutrality and their expected itinerant nature makes excitons potential transmitters of information. However, exciton mobility remains inaccessible to traditional optical experiments that only create and detect excitons with negligible momentum. Here, using angle-resolved photoemission spectroscopy, we detect dispersing excitons in quasi-one-dimensional metallic trichalcogenide, TaSe₃. The low density of conduction electrons and low dimensionality in TaSe₃ combined with a polaronic renormalization of the conduction band and the poorly screened interaction between these polarons and photo-induced valence holes leads to various excitonic bound states that we interpret as intrachain and interchain excitons, and possibly trions. The thresholds for the formation of a photo-hole together with an exciton appear as side valence bands with dispersions nearly parallel to the main valence band, but shifted to lower excitation energies. The energy separation between side and main valence bands can be controlled by surface doping, enabling the tuning of certain exciton properties. This document contains the raw data of this project.

en: Multiple mobile excitons manifested as sidebands in quasi-one-dimensional metallic TaSe₃

arpes

display_name: arpes

dispersive-bands-from-excitons

display_name: dispersive-bands-from-excitons

interchain-excitons-and-trions

display_name: interchain-excitons-and-trions

mobile-excitons-in-metal

display_name: mobile-excitons-in-metal


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: Time-dependent density functional theory has become state-of-the-art for describing photophysical and photochemical processes in extended materials due to its affordable cost. The inclusion of exact exchange was shown to be essential for the correct description of the long-range asymptotics of electronic interactions and thus a well-balanced description of valence, Rydberg and charge-transfer excitations. Several approaches for an efficient treatment of exact exchange have been established for the ground state, while implementations for excited-state properties are rare. Furthermore, the high computational costs required for excited-state properties in comparison to ground-state computations often hinder large-scale applications on periodic systems with hybrid functional accuracy. We therefore propose two approximate schemes for improving computational efficiency for the treatment of exact exchange. Within the auxiliary density matrix method (ADMM), exact exchange is estimated using a relatively small auxiliary basis and the introduced basis-set incompleteness error is compensated by an exchange density functional correction term. Benchmark results for a test set of 35 molecules demonstrate that the mean absolute error introduced by ADMM is smaller than 0.3 pm for excited-state bond lengths and in the range of 0.02 - 0.07 eV for vertical excitation, adiabatic excitation and fluorescence energies. Computational timings for a series of covalent-organic frameworks demonstrate that a speed-up of at least one order of magnitude can be achieved for ES geometry optimizations in comparison to conventional hybrid functionals. The second method is to use a semi-empirical tight binding approximation for both Coulomb and exchange contributions to the excited-state kernel. This simplified Tamm-Dancoff approximation (sTDA) achieves an accuracy comparable to approximated hybrid density functional theory when referring to highly accurate coupled-cluster reference data. We find that excited-state bond lengths deviate by 1.1 pm on average and mean absolute errors in vertical excitation, adiabatic excitation and fluorescence energies are in the range of 0.2 - 0.5 eV. In comparison to ADMM-approximated hybrid functional theory, sTDA accelerates the computation of broad-band excitation spectra by one order of magnitude, suggesting its potential use for large-scale screening purposes.

en: Excited-state properties for extended systems: efficient hybrid density functional methods

excited-state-properties

display_name: excited-state-properties

hybrid-density-functional-theory

display_name: hybrid-density-functional-theory

marie-curie-fellowship

display_name: marie-curie-fellowship

theoretical-spectroscopy

display_name: theoretical-spectroscopy


materialscloud

en: Materials Cloud

marvel

display_name: marvel


en: The design of open-shell carbon-based nanomaterials is at the vanguard of materials science, steered by their beneficial magnetic properties like weaker spin–orbit coupling than that of transition metal atoms and larger spin delocalization, which are of potential relevance for future spintronics and quantum technologies. A key parameter in magnetic materials is the magnetic exchange coupling (MEC) between unpaired spins, which should be large enough to allow device operation at practical temperatures. In a recent work, we theoretically and experimentally explore three distinct families of nanographenes (NGs) (A, B, and C) featuring majority zigzag peripheries. Through many-body calculations, we identify a transition from a closed-shell ground state to an open-shell ground state upon an increase of the molecular size. Our predictions indicate that the largest MEC for open-shell NGs occurs in proximity to the transition between closed-shell and open-shell states. Such predictions are corroborated by the on-surface syntheses and structural, electronic, and magnetic characterizations of three NGs (A[3,5], B[4,5], and C[4,3]), which are the smallest open-shell systems in their respective chemical families and are thus located the closest to the transition boundary. Notably, two of the NGs (B[4,5] and C[4,3]) feature record values of MEC (close to 200 meV) measured on the Au(111) surface. Our strategy for maximizing the MEC provides perspectives for designing carbon nanomaterials with robust magnetic ground states. This record contains the data for the simulations discussed in our manuscript.

en: Steering large magnetic exchange coupling in nanographenes near the closed-shell to open-shell transition

envi

en: Environment

nanographenes

display_name: nanographenes

open-shell

display_name: open-shell

snsf

display_name: snsf

spectroscopy

display_name: spectroscopy


dft

display_name: dft


en: Computational searches for new materials are naturally turning from binary systems, to ternary and other multicomponent systems, and beyond. Here, we select the industrially-relevant metals Ti and Al and report the results of an extensive structure prediction study on the ternary titanium-carbon-oxygen (Ti-C-O) and aluminium-carbon-oxygen (Al-C-O) systems. We map out for the first time the full phase stability of Ti-C-O and Al-C-O compounds using first-principles calculations, through simple, effcient and highly parallel random structure searching in conjunction with techniques based on complex network theory. This record contains the crystal structures used to generate our ternary convex hulls for these systems. We provide input files for the CASTEP density-functional theory code, crystallographic '.res' files of the relaxed structures, and CASTEP output files showing atomic positions and forces calculated during relaxation.

en: Navigating the Ti-C-O and Al-C-O ternary systems through theory-driven discovery

carbonate-minerals

display_name: carbonate-minerals

density-functional-theory

display_name: density-functional-theory

structure-prediction

display_name: structure-prediction

ternary-systems

display_name: ternary-systems


en: The properties of molecules and materials containing light nuclei are affected by their quantum mechanical nature. Accurate modeling of these quantum nuclear effects requires computationally demanding path integral techniques. Considerable success has been achieved in reducing the cost of such simulations by using generalized Langevin dynamics to induce frequency-dependent fluctuations. Path integral generalized Langevin equation methods, however, have this far been limited to the study of static, thermodynamic properties due to the large perturbation to the system's dynamics induced by the aggressive thermostatting. Here, we introduce a post-processing scheme, based on analytical estimates of the dynamical perturbation induced by the generalized Langevin dynamics, which makes it possible to recover meaningful time correlation properties from a thermostatted trajectory. We show that this approach yields spectroscopic observables for model and realistic systems that have an accuracy comparable to much more demanding approximate quantum dynamics techniques based on full path integral simulations.

en: Inexpensive modeling of quantum dynamics using path integral generalized Langevin equation thermostats

epfl

display_name: epfl

erc

display_name: erc

molecular-dynamics

display_name: molecular-dynamics

nuclear-quantum-effects

display_name: nuclear-quantum-effects

vibrational-spectra

display_name: vibrational-spectra


en: Landau-Lifshitz-Gilbert (LLG) spin-dynamics calculations based on the extended Heisenberg Hamiltonian is an important tool in computational materials science involving magnetic materials. LLG simulations allow to bridge the gap from expensive quantum mechanical calculations with small unit cells to large supercells where the collective behavior of millions of spins can be studied. In this work we present the AiiDA-Spirit plugin that connects the spin-dynamics code Spirit to the AiiDA framework. AiiDA provides a Python interface that facilitates performing high-throughput calculations while automatically augmenting the calculations with metadata describing the data provenance between calculations in a directed acyclic graph. The AiiDA-Spirit interface thus provides an easy way for high-throughput spin-dynamics calculations. The interface to the AiiDA infrastructure furthermore has the advantage that input parameters for the extended Heisenberg model can be extracted from high-throughput first-principles calculations including a proper treatment of the data provenance that ensures reproducibility of the calculation results in accordance to the FAIR principles. We describe the layout of the AiiDA-Spirit plugin and demonstrate its capabilities using selected examples for LLG spin-dynamics and Monte Carlo calculations. Furthermore, the integration with first-principles calculations through AiiDA is demonstrated at the example of gamma-Fe, where the complex spin-spiral ground state is investigated.

en: The AiiDA-Spirit plugin for automated spin-dynamics simulations and multi-scale modelling based on first-principles calculations

landau-lifshitz-gilbert-equation

display_name: landau-lifshitz-gilbert-equation

llg

display_name: llg

monte-carlo

display_name: monte-carlo

skyrmion

display_name: skyrmion

spin-dynamics

display_name: spin-dynamics


en:
A semi-automated protocol largely based on AiiDA has been developed and exploited to explore the yet-unsolved crystal structure of the recently-discovered AsTe₃ material with potential thermoelectric applications due to its ultralow thermal conductivity. The workflow involves different steps of increasing precision and computational cost, in a sequential approach to generate, refine and evaluate model AsTe₃ structures based on supercells of elemental-Te from the stand point of As/Te chemical disorder. Our studies shed light on the structural complexity that has prevented, to this day, a direct structure determination of the recently-discovered crystalline AsTe₃ material, despite the new synthesis protocol that has led to its obtention in a pure and fully crystalline phase, by congruent crystallization from the parent AsTe₃ glass. Structural models of crystalline AsTe₃ reveal a composition based on an intergrowth of subnanometric As₂Te₃ and Te domains with potentially defected interfaces that intimately interfere with the electronic properties of this material.
This repository contains data generated in the course of the structure-construction-and-evaluation protocol for subsets of AsTe₃ structural models obtained at different steps of the workflow, with the full provenance included, in the form of an AiiDA archive. Scripts and workchains developped specifically for this study are also privided, along with the atomic structures of the most relevant AsTe₃ models identified, in common file formats for researchers not familiar with AiiDA databases.

en: Insights into the structural complexity and local disorder of crystalline AsTe₃ from semi-automated first-principles modelling

automation

display_name: automation

chemical-disorder

display_name: chemical-disorder

structural-complexity

display_name: structural-complexity

structural-models

display_name: structural-models

thermoelectrics

display_name: thermoelectrics


en: Due to their high accuracy, Double-Hybrid Density functionals emerged to important methods for molecular electronic-structure calculations. The high computational costs of double-hybrid calculations in condensed phase and the lack of efficient gradient implementations thereof inhibit a wide applicability for periodic systems. We present an implementation of gradients for Double-Hybrid functional theory into CP2K. The Auxiliary Density Matrix Method (ADMM) reduces the overhead from the Hartree-Fock calculations providing an efficient and accurate methodology to tackle condensed phase systems. First applications to water containing systems of different densities and molecular crystals pave the way for advanced studies. We present large benchmark systems to discuss the efficiency of our methodology on modern super computing hardware.

en: Double-Hybrid Density functionals for the condensed phase: gradients, stress tensor, and Auxiliary-Density Matrix Method acceleration

analytical-gradients

display_name: analytical-gradients

auxiliary-density-matrix-method

display_name: auxiliary-density-matrix-method

benchmark

display_name: benchmark

condensed-matters

display_name: condensed-matters

cscs

display_name: cscs

density-functional-theory

display_name: density-functional-theory

double-hybrid-functional

display_name: double-hybrid-functional

electronic-structure-method

display_name: electronic-structure-method

molecular-crystals

display_name: molecular-crystals

snsf

display_name: snsf


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: Singlet fission (SF) is a promising multiexciton-generating process. Its demanding energy splitting criterion - that the S1 energy must be at least twice that of T1 - has limited the range of materials capable of SF. We propose heteroatom oxidation as a robust strategy to achieve sufficient S1/T1 splitting, and demonstrate the potential of this approach for intramolecular SF.

en: Heteroatom oxidation controls singlet-triplet energy splitting in singlet fission building blocks

conjugated-polymers

display_name: conjugated-polymers

donor-acceptor-systems

display_name: donor-acceptor-systems

epfl

display_name: epfl

excited-states

display_name: excited-states

singlet-fission

display_name: singlet-fission


materialscloud

en: Materials Cloud


en: The efficient activation of methane and simultaneous water dissociation are crucial in many catalytic reactions on oxide-supported transition metal catalysts. On very low-loaded Ni/CeO₂ surfaces, methane easily fully decomposes, CH₄ -> C + 4H,  and water dissociates, H₂O -> OH + H. However, in important reactions such as the direct oxidation of methane to methanol (MTM), where complex interplay exists between reactants (CH₄, O₂), it is desirable to avoid the complete dehydrogenation of methane to carbon.  Remarkably, the barrier for the activation of C-H bonds in CHx (x= 1-3) species on Ni/CeO₂ surfaces can be manipulated by adding Cu, forming bimetallic NiCu clusters, whereas the ease for cleavage of O-H bonds in water, is not affected by ensemble effects, as obtained from density functional theory-based calculations. CH4 activation occurs only on Ni sites and H₂O activation on both Ni and Cu sites. The MTM reaction pathway for the example of the Ni₃Cu₁/CeO₂ model catalyst predict higher selectivity and a lower activation barrier for methanol production, compared with that for Ni₄-CeO₂. These findings point toward a possible strategy to design active and stable catalysts which can be employed for methane activation and conversions.

en: Tuning selectivity in the direct conversion of methane to methanol: bimetallic synergistic effects on the cleavage of C-H and O-H bonds over NiCu/CeO₂ catalysts

bimetallic

display_name: bimetallic

ceria

display_name: ceria

methane-activation

display_name: methane-activation

water-activation

display_name: water-activation


envi

en: Environment


erc

display_name: erc

marvel

display_name: marvel

molecular-dynamics

display_name: molecular-dynamics

nuclear-quantum-effects

display_name: nuclear-quantum-effects

vibrational-spectra

display_name: vibrational-spectra


en: Why are materials with specific characteristics more abundant than others? This is a fundamental question in materials science and one that is traditionally difficult to tackle, given the vastness of compositional and configurational space. We highlight here the anomalous abundance of  inorganic compounds whose primitive unit cell contains a number of atoms that is a multiple of four. This occurrence - named the 'Rule of Four' - has to our knowledge not previously been reported or studied rigorously. Here, we first highlight the rule's existence, especially notable when restricting oneself to experimentally known compounds, and explore its possible relationship with established descriptors of  crystal structures, from symmetries to energies. We then investigate this relative abundance by looking at structural descriptors, both of global (packing configurations) and local (the smooth overlap of atomic positions) nature. Contrary to intuition, this overabundance does not correlate with low-energy or high-symmetry structures; in fact, structures which obey the 'Rule of Four' are characterized by low symmetries and loosely packed arrangements maximizing the free volume.  We are able to correlate this abundance with local structural symmetries, and visualize the results using a hybrid supervised-unsupervised machine learning method.

en: The rule of four: anomalous stoichiometries of inorganic compounds

classification

display_name: classification

inorganic-databases

display_name: inorganic-databases

local-symmetries

display_name: local-symmetries

machine-learning

display_name: machine-learning

soap-vectors

display_name: soap-vectors


en:
Why are materials with specific characteristics more abundant than others?
This is a fundamental question in materials science and one that is traditionally difficult to tackle, given the vastness of compositional and configurational space. We highlight here the anomalous abundance of  inorganic compounds whose primitive unit cell contains a number of atoms that is a multiple of four. This occurrence - named here the 'rule of four' - has to our knowledge not previously been reported or studied. Here, we first highlight the rule's existence, especially notable when restricting oneself to experimentally known compounds, and explore its possible relationship with established descriptors of  crystal structures, from symmetries to energies. We then investigate this relative abundance by looking at structural descriptors, both of global (packing configurations) and local (the smooth overlap of atomic positions) nature. Contrary to intuition, the overabundance does not correlate with low-energy or high-symmetry structures; in fact, structures which obey the 'rule of four' are characterized by low symmetries and loosely packed arrangements maximizing the free volume. We are able to correlate this abundance with local structural symmetries, and visualize the results using a hybrid supervised-unsupervised machine learning method.


en: We present a self-consistent method based on first-principles calculations to determine the magnetic ground state of materials, regardless of their dimensionality. Our methodology is founded on satisfying the stability conditions derived from the linear spin wave theory (LSWT) by optimizing the magnetic structure iteratively. We demonstrate the effectiveness of our method by successfully predicting the experimental magnetic structures of NiO, FePS₃, FeP, MnF₂, FeCl₂, and CuO. In each case, we compared our results with available experimental data and existing theoretical calculations reported in the literature. Finally, we discuss the validity of the method and the possible extensions.

en: Systematic determination of a material's magnetic ground state from first principles

dftu

display_name: dftu

linear-spin-wave-theory

display_name: linear-spin-wave-theory

magnetic-structure

display_name: magnetic-structure


en: It is well known that interfaces in nanomaterials can act as ultra-fast short-circuit diffusion paths, as originating from local structural, chemical and/or electronic modifications at the interface. For example, the interface diffusivity of Cu in Cu/AlN nanomultilayers can be up to two orders of magnitude higher as compared to the bulk, which may promote interfacial premelting of Cu. Extensive ab initio calculations of vacancy formation and migration energies in Cu/AlN nanomultilayers were performed to arrive at the fundamental understanding of such anomalously fast interface diffusion phenomena. It was found that both the metallic Al-terminated interface and the mixed-bonded N-terminated interface promote high atomic interface mobilities by lowering the vacancy formation and vacancy migration energies in the interfacial Cu planes. Moreover, the out-of-plane vacancy migration energies highlights a strong tendency of vacancy segregation toward both interfaces.

en: Anomalously low vacancy formation energies and migration barriers at Cu/AlN interfaces from ab initio calculations

ab-initio

display_name: ab-initio

metal-ceramic-interfaces

display_name: metal-ceramic-interfaces

nanomultilayers

display_name: nanomultilayers

vacancy-driven-diffusion

display_name: vacancy-driven-diffusion


en: Using first-principles techniques, we study the structural, magnetic, and electronic properties of (111)-oriented (LaMnO₃)₂ₙ|(SrMnO₃)ₙ superlattices of varying thickness (n=2,4,6). We find that the properties of the thinnest superlattice (n=2) are similar to the celebrated half-metallic ferromagnetic alloy La2/3Sr1/3MnO₃, with quenched Jahn-Teller distortions. At intermediate thickness (n=4), the a⁻a⁻a⁻ tilting pattern transitions to the a⁻a⁻c⁺ tilting pattern, driven by the lattice degrees of freedom in the LaMnO₃ region. The emergence of the Jahn-Teller modes and the spatial extent needed for their development play a key role in this structural transition. For the largest thickness considered (n=6), we unveil an emergent separation of Jahn-Teller and volume-breathing orders in the ground-state structure with the a⁻a⁻c⁺ tilting pattern, whereas it vanishes in the antiferromagnetic configurations. The ground state of all superlattices is half-metallic ferromagnetic, not affected by the underlying series of structural transitions. Overall, these results outline a thickness-induced crossover between the physical properties of bulk La2/3Sr1/3MnO₃ and bulk LaMnO₃. The dataset includes the fully relaxed atomic positions of the most favorable tilting patterns, as well as the main output files of the electronic structure calculations. Selected files for the density of states are also included.

en: Emergent half-metal with mixed structural order in (111)-oriented (LaMnO₃)₂ₙ|(SrMnO₃)ₙ superlattices

dft

display_name: dft

electronic-properties

display_name: electronic-properties

magnetic-properties

display_name: magnetic-properties

oxide-heterostructures

display_name: oxide-heterostructures


en: Polyelectrolyte complexes (PECs), formed via the self-assembly of oppositely charged polysaccharides, are highly valued for their biocompatibility, biodegradability, and hydrophilicity, offering significant potential for biotechnological applications. However, the complex nature and lack of insight at a molecular level into polyelectrolytes conformation and aggregation often hinders the possibility of achieving an optimal control of PEC systems, limiting their practical applications. To address this problem, an in-depth investigation of PECs microscopic structural organization is required. In this work, for the first time, a hybrid approach that combines experimental techniques with atomistic molecular dynamics simulations is used to elucidate, at a molecular level, the mechanisms underlying the aggregation and structural organization of complexes formed by gellan and chitosan, i.e. PECs commonly used in food technology. This combined analysis reveals a two-step complexation process: gellan initially self-assembles into a double-helix structure, subsequently surrounded and stabilized by chitosan via electrostatic interactions. Furthermore, these results show that complexation preserves the individual conformation and intrinsic functionality of both polyelectrolytes, thereby ensuring the efficacy of the PECs in biotechnological applications.

en: Unveiling the self-assembly process of gellan-chitosan complexes through a combination of atomistic simulations and experiments

chitosan

display_name: chitosan

gellan

display_name: gellan

gels

display_name: gels

polyelectrolyte-complexes

display_name: polyelectrolyte-complexes

polysaccharides

display_name: polysaccharides


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: Doping and compositing are two universal design strategies used to engineer the electronic state of a material and mitigate its disadvantages. These two strategies have been extensively applied to the design of efficient electrocatalysts for water splitting. Using cobalt oxide (CoO) as a model catalyst, we prove that the oxygen evolution reaction (OER) performance could be progressively improved, first by Fe-doping to form Fe-CoO solid solution, and further by the addition of CeO2 to produce a Fe-CoO/CeO2 composite. X-ray adsorption spectroscopy (XAS) reveals that distinct electronic interactions are induced by the processes of doping and compositing. Fe-doping of CoO can break down the structural symmetry in the pristine material, changing the electronic structure of both Co and O species at the surface and decreasing the flat-band potential (Vfb). In comparison, subsequent compositing of Fe-CoO with CeO2 induces negligible electronic changes in the as-synthesized Fe-CoO (as seen in ex-situ characterizations), but significantly modifies the oxidative transformations of both Co and Fe under OER conditions. Our spectroscopic investigations reveal that Fe-doping and CeO2 compositing play different roles in modifying the electronic properties of CoO in its pristine state and during OER catalysis, respectively, in return, providing useful guidance for the design of more efficient electrocatalysts using these two strategies.

en: Spectroscopic investigations of complex electronic interactions by elemental doping and material compositing of cobalt oxide for enhanced oxygen evolution reaction activity

compositing

display_name: compositing

doping

display_name: doping

electronic-interaction

display_name: electronic-interaction

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction

x-ray-adsorption-spectroscopy

display_name: x-ray-adsorption-spectroscopy


materialscloud

en: Materials Cloud


en: This database contains phosphorus and sulfur K-edge X-ray absorption near-edge structure (XANES) of delithiated Lithium-Phosphorus-Sulfur compounds. The structures were generated by a computational delithiation procedure of β-Li3PS4 with density functional theory calculations, where we systematically enumerated distinct lithium/vacancy orderings in the super cells with the general composition Li12-xP4S16 (i.e., four Li3PS4 formula units and x from 0 to 12). The database contains a total of 2227 P K-edge and 8885 S K-edge XANES spectra of symmetrically inequivalent absorbing P and S sites. The XANES spectra were calculated using the excited electron and core hole method as implemented in The Vienna Ab initio Simulation Package (VASP) 6.2.1. Details of the structure generation procedure as well as the VASP simulations can be found in the associated manuscript (see reference below).

en: Atomic insights into the oxidative degradation mechanisms of sulfide solid electrolytes

lithium-ion-batteries

display_name: lithium-ion-batteries

lithium-phosphorus-sulfide

display_name: lithium-phosphorus-sulfide

solid-electrolyte

display_name: solid-electrolyte

vasp

display_name: vasp

x-ray-absorption-spectroscopy

display_name: x-ray-absorption-spectroscopy


envi

en: Environment


en: Raman spectra play an important role in characterizing two-dimensional materials, as they provide a direct link between the atomic structure and the spectral features. In this work, we present an  automatic computational workflow for Raman spectra using all-electron density functional perturbation theory. Utilizing this workflow, we have successfully completed the Raman spectra calculation for 3504 different two-dimensional materials, with the resultant data saved in a data repository.

en: High-throughput computation of ab initio Raman spectra for two-dimensional materials

fhi-aims

display_name: fhi-aims

raman-spectra

display_name: raman-spectra

two-dimensional-material

display_name: two-dimensional-material


en:
Two-dimensional (2D) materials have been a central focus of recent research because they host a variety of properties, making them attractive both for fundamental science and for applications. It is thus crucial to be able to identify accurately and efficiently if bulk three-dimensional (3D) materials are formed by layers held together by weak binding energy and, thus, can be potentially exfoliated into 2D materials. In this work, we develop a machine-learning (ML) approach that, combined with a fast preliminary geometrical screening, is able to efficiently identify potentially exfoliable materials. Starting from a combination of descriptors for crystal structures, we work out a subset of them that are crucial for accurate predictions. Our final ML model, based on a random forest classifier, has a very high recall of 98%. Using a SHapely Additive exPlanations (SHAP) analysis, we also provide an intuitive explanation of the five most important variables of the model. Finally, we compare the performance of our best ML model with a deep neural network architecture using the same descriptors.
To make our algorithms and models easily accessible, we publish an online tool on the Materials Cloud portal that only requires a bulk 3D crystal structure as input. Our tool thus provides a practical yet straightforward approach to assess whether any 3D compound can be exfoliated into 2D layers.

en: Machine-learning accelerated identification of exfoliable two-dimensional materials

binding-energy

display_name: binding-energy

crystal-structure

display_name: crystal-structure

exfoliation

display_name: exfoliation

online-tool

display_name: online-tool

two-dimensional

display_name: two-dimensional


en: Ab initio random structure searching (AIRSS) has been performed for carbon at 10GPa, a pressure at which diamond is expected to be the thermodynamic ground state. A small (~10K structures) and larger (~100K structures) set are provided. To demonstrate a search in a ternary composition space ~100K AIRSS structures for the C+N+H+O system are provided. All structures are generated at random using the buildcell tool of the AIRSS suite, and relaxed to a nearby local minimum in the DFT-PBE total energy using the CASTEP code. Computational details are provided in the REM block of the .res structure files.

en: AIRSS data for carbon at 10GPa and the C+N+H+O system at 1GPa

ab-initio-random-structure-searching

display_name: ab-initio-random-structure-searching

carbon

display_name: carbon

dft

display_name: dft


en: Two-dimensional (2D) materials have emerged as promising candidates for next-generation electronic and optoelectronic applications. Yet, only a few dozens of 2D materials have been successfully synthesized or exfoliated. Here, we search for novel 2D materials that can be easily exfoliated from their parent compounds. Starting from 108423 unique, experimentally known three-dimensional compounds we identify a subset of 5619 that appear layered according to robust geometric and bonding criteria. High-throughput calculations using van-der-Waals density-functional theory, validated against experimental structural data and calculated random-phase-approximation binding energies, allow to identify 1825 compounds that are either easily or potentially exfoliable. In particular, the subset of 1036 easily exfoliable cases provides novel structural prototypes and simple ternary compounds as well as a large portfolio of materials to search from for optimal properties. For a subset of 258 compounds we explore vibrational, electronic, magnetic, and topological properties, identifying 56 ferromagnetic and antiferromagnetic systems, including half-metals and half-semiconductors. This archive entry contains the database of 2D materials (structural parameters, band structures, binding energies, etc.) together with the provenance of all data and calculations as stored by AiiDA.

en: Two-dimensional materials from high-throughput computational exfoliation of  experimentally known compounds

database

display_name: database

electronic-bands

display_name: electronic-bands

electronic-properties

display_name: electronic-properties

high-throughput

display_name: high-throughput

magnetic-properties

display_name: magnetic-properties

marvel

display_name: marvel

monolayer

display_name: monolayer

phonons

display_name: phonons

vibrational-properties

display_name: vibrational-properties


en: Two-dimensional materials from high-throughput computational exfoliation of  experimentally known compounds (Data download)


en: As exemplified by the growing interest in the quantum anomalous Hall effect, the research on topology as an organizing principle of quantum matter is greatly enriched from the interplay with magnetism. In this vein, we present a combined electrical and thermoelectrical transport study on the magnetic Weyl semimetal EuCd₂As₂. Unconventional contribution to the anomalous Hall and anomalous Nernst effects were observed both above and below the magnetic transition temperature of EuCd₂As₂, indicating the existence of significant Berry curvature. EuCd₂As₂ represents a rare case in which this unconventional transverse transport emerges both above and below the magnetic transition temperature in the same material. The transport properties evolve with temperature and field in the antiferromagnetic phase in a different manner than in the paramagnetic phase, suggesting different mechanisms to their origin. Our results indicate EuCd₂As₂ is a fertile playground for investigating the interplay between magnetism and topology, and potentially a plethora of topologically nontrivial phases rooted in this interplay. This record contains the raw data for this manuscript [Physical Review Letters 126, 076602 (2021)].

en: Unconventional transverse transport above and below the magnetic transition temperature in Weyl semimetal EuCd₂As₂

anomalous-hall-effect

display_name: anomalous-hall-effect

anomalous-nernst-effect

display_name: anomalous-nernst-effect

experimental

display_name: experimental

magnetic-weyl-semimetal

display_name: magnetic-weyl-semimetal

topological-spin-texture

display_name: topological-spin-texture


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 recent years, significant progress has been made in the development of machine learning potentials (MLPs) for atomistic simulations with applications in many fields from chemistry to materials science. While most current MLPs are based on environment-dependent atomic energies, the limitations of this locality approximation can be overcome, e.g., in fourth-generation MLPs, which incorporate long-range electrostatic interactions based on an equilibrated global charge distribution. Apart from the considered interactions,  the quality of MLPs crucially depends on the information available about the system, i.e., the descriptors.
In this work we show that including — in addition to structural information — the electrostatic potential arising from the charge distribution in the atomic environments significantly improves the quality and transferability of the potentials. Moreover, the extended descriptor allows to overcome current limitations of two- and three-body based feature vectors regarding artificially degenerate atomic environments.
The capabilities of such an electrostatically embedded fourth-generation high-dimensional neural network potential (ee4G-HDNNP), which is further augmented by pairwise interactions, are demonstrated for NaCl as a benchmark system. Employing a data set containing only neutral and negatively charged NaCl clusters, even small energy differences between different cluster geometries can be resolved, and the potential shows an impressive transferability to positively charged clusters as well as the melt.

en: Accurate fourth-generation machine learning potentials by electrostatic embedding

electrostatics

display_name: electrostatics

machine-learning-potentials

display_name: machine-learning-potentials


materialscloud

en: Materials Cloud

dft

display_name: dft


en: We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form A2B2X6, based on the known material Cr2Ge2Te6, using density functional theory (DFT) calculations and determine their magnetic properties, such as magnetic order and magnetic moment. We also examine formation energies and use them as a proxy for chemical stability.

en: Data-driven studies of magnetic two-dimensional materials

machine-learning

display_name: machine-learning

magnetic-materials

display_name: magnetic-materials

two-dimensional-materials

display_name: two-dimensional-materials


envi

en: Environment


en: In this work we provide the results for IP and EA of all the 100 molecules of the set as computed within the Yambo code. In this way, we enlarge the GW100 benchmark considering the largely used Godby-Need Plasmon Pole Approximation (GNPPA), used in Yambo to describe the frequency dependence of the screening potential and not yet included in previous GW100 studies. Fully converged results on HOMO and LUMO are provided here for each molecule of the GW100 set, as extrapolated from Yambo calculations with different value of the involved parameters (plane wave cutoff for the microscopic dielectric matrix and empty state summations).

en: Benchmarking the GW100 dataset with the Yambo code by means of G₀W₀ approximation

aiida

display_name: aiida

aiida-yambo

display_name: aiida-yambo

first-principles

display_name: first-principles

godby-need-plasmon-pole-approximation

display_name: godby-need-plasmon-pole-approximation

gw

display_name: gw

high-throughput

display_name: high-throughput

yambo

display_name: yambo

yambo-code

display_name: yambo-code


en: The development of enhanced sampling methods has greatly extended the scope of atomistic simulations, allowing long-time phenomena to be studied with accessible computational resources. Many such methods rely on the identification of an appropriate set of collective variables. These are meant to describe the system's modes that most slowly approach equilibrium. Once identified, the equilibration of these modes is accelerated by the enhanced sampling method of choice. An attractive way of determining the collective variables is to relate them to the eigenfunctions and eigenvalues of the transfer operator. Unfortunately, this requires knowing the long-term dynamics of the system beforehand, which is generally not available. However, we have recently shown that it is indeed possible to determine efficient collective variables starting from biased simulations. In this paper, we bring the power of machine learning and the efficiency of the recently developed on-the-fly probability enhanced sampling method to bear on this approach. The result is a powerful and robust algorithm that, given an initial enhanced sampling simulation performed with trial collective variables or generalized ensembles, extracts transfer operator eigenfunctions using a neural network ansatz and then accelerates them to promote sampling of rare events. To illustrate the generality of this approach we apply it to several systems, ranging from the conformational transition of a small molecule to the folding of a mini-protein and the study of materials crystallization.

en: Deep learning the slow modes for rare events sampling

collective-variables

display_name: collective-variables

enhanced-sampling

display_name: enhanced-sampling

molecular-dynamics

display_name: molecular-dynamics

protein-folding

display_name: protein-folding

silicon-crystallization

display_name: silicon-crystallization


en: Being Wannierizable is not the end of the story for topological insulators. We introduce a family of topological insulators that would be considered trivial in the paradigm set by the tenfold way, topological quantum chemistry, and the method of symmetry-based indicators. Despite having a symmetric, exponentially localized Wannier representation, each Wannier function cannot be completely localized to a single primitive unit cell in the bulk. Such multicellular topology is shown to be neither stable nor fragile, but delicate; i.e., the topology can be nullified by adding trivial bands to either valence or conduction band.

en: Multicellularity of delicate topological insulators

delicate-topology

display_name: delicate-topology

erc

display_name: erc

hopf-insulator

display_name: hopf-insulator

multicellular-wannier-functions

display_name: multicellular-wannier-functions

returning-thouless-pump

display_name: returning-thouless-pump

snsf

display_name: snsf

topological-insulators

display_name: topological-insulators


en: Two-dimensional (2D) materials have emerged as promising candidates for next-generation electronic and optoelectronic applications. Yet, only a few dozens of 2D materials have been successfully synthesized or exfoliated. Here, we search for novel 2D materials that can be easily exfoliated from their parent compounds. Starting from 108423 unique, experimentally known three-dimensional compounds we identify a subset of 5619 that appear layered according to robust geometric and bonding criteria. High-throughput calculations using van-der-Waals density-functional theory, validated against experimental structural data and calculated random-phase-approximation binding energies, allow to identify 1825 compounds that are either easily or potentially exfoliable. In particular, the subset of 1036 easily exfoliable cases provides novel structural prototypes and simple ternary compounds as well as a large portfolio of materials to search from for optimal properties. For a subset of 258 compounds we explore vibrational, electronic, magnetic, and topological properties, identifying 56 ferromagnetic and antiferromagnetic systems, including half-metals and half-semiconductors. This archive entry contains the database of 2D materials (structural parameters, band structures, binding energies, phonons for the subset of the 258 easily exfoliable materials with less than 6 atoms, structures and binding energies for the remaining 1567 materials) together with the provenance of all data and calculations as stored by AiiDA.

en: Two-dimensional materials from high-throughput computational exfoliation of  experimentally known compounds

database

display_name: database

dft

display_name: dft

electronic-bands

display_name: electronic-bands

electronic-properties

display_name: electronic-properties

magnetic-properties

display_name: magnetic-properties

marvel

display_name: marvel

monolayer

display_name: monolayer

phonons

display_name: phonons

two-dimensional

display_name: two-dimensional

vibrational-properties

display_name: vibrational-properties


en: Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds

high-throughput

display_name: high-throughput


en:
Despite their incredible performance, it is well reported that deep neural networks tend to be overoptimistic about their prediction confidence. Finding effective and efficient calibration methods for neural networks is therefore an important endeavour towards better uncertainty quantification in deep learning. In this manuscript, we introduce a novel calibration technique named expectation consistency (EC), consisting of a post-training rescaling of the last layer weights by enforcing that the average validation confidence coincides with the average proportion of correct labels. First, we show that the EC method achieves similar calibration performance to temperature scaling (TS) across different neural network architectures and data sets, all while requiring similar validation samples and computational resources. However, we argue that EC provides a principled method grounded on a Bayesian optimality principle known as the Nishimori identity. Next, we provide an asymptotic characterization of both TS and EC in a synthetic setting and show that their performance crucially depends on the target function. In particular, we discuss examples where EC significantly outperforms TS.
This record provides the code for the paper "Expectation consistency for calibration of neural networks".

en: Expectation consistency for calibration of neural networks (code)

neural-networks

display_name: neural-networks

temperature-scaling

display_name: temperature-scaling

uncertainty-quantification

display_name: uncertainty-quantification


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 of contaminants in snow and ice can be important sources and sinks for various organic and inorganic compounds. Snow contaminants can be found in the bulk ice matrix, in internal liquid-like regions (LLRs), or in quasi-liquid layers (QLLs) at the air-ice interface, where they can readily exchange with the firn air. Some studies have reported that direct photochemical reactions occur faster in LLRs and QLLs than in aqueous solution, while others have found similar rates. Here, we measure the photodegradation rate constants of the three dimethoxybenzene isomers under varying experimental conditions, including in aqueous solution, in LLRs, and at the air-ice interface of nature-identical snow. Relative to aqueous solution, we find modest photodegradation enhancements (3- and 6-fold) in LLRs for two of the isomers, and larger enhancements (15- to 30-fold) at the air-ice interface for all three isomers. We use computational modeling to assess the impact of light absorbance changes on photodegradation rate enhancements at the interface. We find small (2–5 nm) bathochromic (red) absorbance shifts at the interface relative to in solution, which increases light absorption, but this factor only accounts for less than 50 % of the measured rate constant enhancements. The major factor responsible for photodegradation rate enhancements at the air-ice interface appears to be more efficient photodecay: estimated dimethoxybenzene quantum yields are 6- to 24-fold larger at the interface compared to in aqueous solution and account for the majority (51–96 %) of the observed enhancements. Using a hypothetical model compound with an assumed Gaussian-shaped absorbance peak, we find that a shift in the peak to higher or lower wavelengths can have a minor to substantial impact on photodecay rate constants, depending on the original location of the peak and the magnitude of the shift. Changes in other peak properties at the air-ice interface, such as peak width and height (i.e., molar absorptivity) can also impact rates of light absorption and direct photodecay.

en: Enhanced photodegradation of dimethoxybenzene isomers in/on ice compared to in aqueous solution

absorption-spectra

display_name: absorption-spectra

dimethoxybenzene

display_name: dimethoxybenzene

ice

display_name: ice


materialscloud

en: Materials Cloud


en: A highly appealing strategy to modulate a catalyst's activity and/or selectivity in a dynamic and non-invasive way is to incorporate a photoresponsive unit into a catalytically competent molecule. However, the description of the photoinduced conformational or structural changes that alter the catalyst's intrinsic reactivity is often reduced to a handful of intuitive static representations, which can struggle to capture the complexity of flexible organocatalysts. Here, we show how a comprehensive exploration of the free energy landscape of N-alkylated azobenzene-tethered piperidine catalysts is essential to unravel the conformational characteristics of each configurational state and explain the experimentally observed reactivity trends. Mapping the catalysts' conformational space highlights the existence of false ON or OFF states that lower their switching ability. Our findings expose the challenges associated with the realisation of reversible steric shielding for the photocontrol of Brønsted basicity of piperidine photoswitchable organocatalysts.

en: How robust is the reversible steric shielding strategy for photoswitchable organocatalysts?

epfl

display_name: epfl

erc

display_name: erc

photoswitchable-organocatalysis

display_name: photoswitchable-organocatalysis

reversible-steric-shielding

display_name: reversible-steric-shielding

snsf

display_name: snsf

statistical-sampling

display_name: statistical-sampling


envi

en: Environment


en: We propose Landau levels as a probe for the topological character of electronic bands in two-dimensional moiré superlattices. We consider two configurations of twisted double bilayer graphene (TDBG) that have very similar band structures, but show different valley Chern numbers of the flat bands. These differences between the AB-AB and AB-BA configurations of TDBG clearly manifest as different Landau level sequences in the Hofstadter butterfly spectra calculated using the tight-binding model. The Landau level sequences are explained from the point of view of the distribution of orbital magnetization in momentum space that is governed by the rotational C2 and time-reversal T symmetries. Our results can be readily extended to other twisted graphene multilayers and h-BN/graphene heterostructures thus establishing the Hofstadter butterfly spectra as a powerful tool for detecting the nontrivial valley band topology.

en: Landau levels as a probe for band topology in graphene moiré superlattices

band-topology

display_name: band-topology

landau-level

display_name: landau-level

moire-superlattice

display_name: moire-superlattice

twisted-double-bilayer-graphene

display_name: twisted-double-bilayer-graphene


database

display_name: database

dft

display_name: dft

electronic-bands

display_name: electronic-bands

electronic-properties

display_name: electronic-properties

high-throughput

display_name: high-throughput

magnetic-properties

display_name: magnetic-properties

marvel

display_name: marvel

monolayer

display_name: monolayer

phonons

display_name: phonons

two-dimensional

display_name: two-dimensional

vibrational-properties

display_name: vibrational-properties


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

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

ceria-nanostructures

display_name: ceria-nanostructures

ethanol-decomposition

display_name: ethanol-decomposition

ethylenedioxy-species

display_name: ethylenedioxy-species

facet-dependent-activity

display_name: facet-dependent-activity

green-hydrogen

display_name: green-hydrogen


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

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

anharmonicity

display_name: anharmonicity

thermal-conductivity

display_name: thermal-conductivity

wigner-heat-transport-equation

display_name: wigner-heat-transport-equation


en: Wigner formulation of thermal transport in solids


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

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

density-functional-theory

display_name: density-functional-theory

machine-learning

display_name: machine-learning

molecular-dynamics

display_name: molecular-dynamics