en: Artificial muscle materials promise incredible applications in actuators, robotics and medical apparatus, yet the ability to mimic the full characteristics of skeletal muscles into synthetic materials remains a huge challenge. Herein, inspired by the dynamic sacrificial bonds in biomaterials and the self-strengthening of skeletal muscles by physical exercise, high performance artificial muscle material is prepared by rearrangement of sacrificial coordination bonds in the polyolefin elastomer via a repetitive mechanical training process. Biomass lignin is incorporated as a green reinforcer for the construction of interfacial coordination bonds. The prepared artificial muscle material exhibits high actuation strain (>40%), high actuation stress (1.5 MPa) which can lift more than 10000 times its own weight with 30% strain, characteristics of excellent self-strengthening by mechanical training, strain-adaptive stiffening, and heat/electric programmable actuation performance. In this work, we show a facile strategyfor the fabrication of intelligent materials using easily available raw materials.

en: Biomimetic high performance artificial muscle built on sacrificial coordination network and mechanical training process

materialscloud

en: Materials Cloud

artificial-muscle-materials

display_name: artificial-muscle-materials

lignin

display_name: lignin

mechanical-training

display_name: mechanical-training

programmable-actuation

display_name: programmable-actuation

sacrificial-coordination-bonds

display_name: sacrificial-coordination-bonds

self-strengthening

display_name: self-strengthening

strain-adaptive-stiffening

display_name: strain-adaptive-stiffening


$oid: OBSOLETE

en: The on-top pair density [Π(r)] is a local quantum chemical property, which reflects the probability of two electrons of any spin to occupy the same position in space. Simplest quantity related to the two-particles density matrix, the on-top pair density is a powerful indicator of electron correlation effects and, as such, it has been extensively used to combine density functional theory and multireference wavefunction theory. The widespread application of Π(r) is currently hindered by the need for post-Hartree-Fock or multireference computations for its accurate evaluation. In this work, we propose the construction of a machine learning model capable of predicting the CASSCF-quality on-top pair density of a molecule only from its structure and composition. Our model, trained on the GDB11-AD-3165 database, is able to predict with minimal error the on-top pair density of organic molecules bypassing completely the need for ab-initio computations. The accuracy of the regression is demonstrated using the on-top ratio as a visual metric of electron correlation effects and bond-breaking in real-space. In addition, we report the construction of a specialized basis set, built to fit the on-top pair density in a single, atom-centered expansion. This basis, cornerstone of the regression, could be potentially used also in the same spirit of the resolution-of-the-identity approximation for the electron density.

en: Learning on-top: regressing the on-top pair density for real-space visualization of electron correlation

materialscloud

approval_status: approved

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

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

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

epfl

display_name: epfl

erc

display_name: erc

machine-learning

display_name: machine-learning

on-top-pair-density

display_name: on-top-pair-density

snsf

display_name: snsf

strong-electron-correlation

display_name: strong-electron-correlation


en: A computationally efficient workflow for obtaining low-energy tight-binding Hamiltonians for twisted bilayer graphene, obeying both crystal and time-reversal symmetries is presented in this work. The Hamiltonians at the first magic angle are generated using the Slater-Koster approach with parameters obtained by a fit to ab-initio data at larger angles. Low-energy symmetric four-band and twelve-band Hamiltonians are constructed using the Wannier90 software. The advantage of our scheme is that the low-energy Hamiltonians are purely real and are obtained with the maximum-localization procedure to reduce the spread of the basis functions. Finally, we compute extended Hubbard parameters for both models within the constrained random phase approximation (cRPA) for screening, which again respect the symmetries. The workflow is straightforwardly transferable to other twisted multi-layer materials.

en: Four- and twelve-band low-energy symmetric Hamiltonians and Hubbard parameters for twisted bilayer graphene using ab-initio input

crpa

display_name: crpa

hubbard-parameters

display_name: hubbard-parameters

magic-angle

display_name: magic-angle

slater-koster

display_name: slater-koster

tight-binding

display_name: tight-binding

twisted-bilayer-graphene

display_name: twisted-bilayer-graphene

wannierization

display_name: wannierization


en: We present a scheme to extract the adsorption energy, adsorbate interaction parameter and the saturation coverage from temperature programmed desorption (TPD) experiments. We propose that the coverage dependent adsorption energy can be fit using a functional form including the configurational entropy and linear adsorbate-adsorbate interaction terms. As one example of this scheme, we analyze TPD of CO desorption on Au(211) and Au(310) surfaces. We determine that under atmospheric CO pressure, the steps of both facets adsorb between 0.4-0.9 ML coverage of CO*. We compare this result against energies obtained from five density functionals, RPBE, PBE, PBE-D3, RPBE-D3 and BEEF-vdW. We find that the energies and equilibrium coverages from RPBE-D3 and PBE are closest to the values determined from the TPD. This dataset contains all the DFT calculations run using AiiDA.

en: How to extract adsorption energies, adsorbate-adsorbate interaction parameters, and saturation coverages from temperature programmed desorption experiments

benchmark

display_name: benchmark

catalysis

display_name: catalysis


en: Understanding the physical mechanisms behind thermal effects in phosphors is crucial for white light-emitting device (WLEDs) applications, as thermal quenching of their photoluminescence might render them useless. We analyze from first-principles, before and after absorption/emission of light, two chemically close Eu-doped Ba₃Si₆O₁₂N₂ and Ba₃Si₆O₉N₄ crystals for WLEDs. The first one has an almost constant emission intensity with increasing temperature whereas the other one does not. Our results, in which the Eu-5d levels are obtained inside the band gap thanks to the removal of an electron from the 4f⁷ shell, and the atomic neighborhood properly relaxed in the excited state, attributes the above-mentioned experimental difference to an autoionization model of the thermal quenching, based on the energy difference between Eu 5d and the conduction band minimum. Our depleted-shifted 4f method can identify luminescent centers and therefore allows for effective crystal site engineering of luminescent centers in phosphors from first principles.

en: Understanding thermal quenching of photoluminescence in oxynitride phosphors from first principles

ab-initio

display_name: ab-initio

bson

display_name: bson

ceci

display_name: ceci

first-principles

display_name: first-principles

frs-fnrs

display_name: frs-fnrs

oxynitride-phosphors

display_name: oxynitride-phosphors

photoluminescence

display_name: photoluminescence

rare-earth

display_name: rare-earth

thermal-quenching

display_name: thermal-quenching


en: A new implementation of linear-response time-dependent density functional theory (LR-TDDFT) for core level near-edge absorption spectroscopy is discussed. The method is based on established LR-TDDFT approaches to X-ray absorption spectroscopy (XAS) with additional accurate approximations for increased efficiency. We validate our implementation by reproducing benchmark results at the K-edge and showing that spin–orbit coupling effects at the L2,3-edge are well described. We also demonstrate that the method is suitable for extended systems in periodic boundary conditions and measure a favorable sub-cubic scaling of the calculation cost with system size. We finally show that GPUs can be efficiently exploited and report speedups of up to a factor 2.

en: Efficient and low-scaling linear-response time-dependent density functional theory implementation for core-level spectroscopy of large and periodic systems

low-scaling-algorithm

display_name: low-scaling-algorithm

method-development

display_name: method-development

tddft

display_name: tddft

xas

display_name: xas


en: We performed calculations of the electronic band structure and the Fermi surface as well as measured the longitudinal resistivity ρxx(T,H), Hall resistivity ρxy(T,H), and quantum oscillations of the magnetization as a function of temperature at various magnetic fields for MoO2 with a monoclinic crystal structure. The band structure calculations show that MoO2 is a nodal-line semimetal when the spin-orbit coupling is ignored. It was found that a large magnetoresistance reaching 5.03 × 10^4% at 2 K and 9 T, its nearly quadratic field dependence, and a field-induced up-turn behavior of ρxx(T), the characteristics common for many topologically nontrivial as well as trivial semimetals, emerge also in MoO2. The observed properties are attributed to a perfect charge-carrier compensation, evidenced by both calculations relying on the Fermi surface topology and the Hall resistivity measurements. Both the observation of negative magnetoresistance for the magnetic field along the current direction and the nonzero Berry phase in de Haas–van Alphen measurements indicate that pairs of Weyl points appear in MoO2, which may be due to the crystal symmetry breaking. These results highlight MoO2 as a platform for studying the topological properties of oxides.

en: Large magnetoresistance and nonzero Berry phase in the nodal-line semimetal MoO2

berry-phase

display_name: berry-phase

cscs

display_name: cscs

epfl

display_name: epfl

magnetoresistance

display_name: magnetoresistance

marvel

display_name: marvel


en: The simultaneous condensation of electronic and structural degrees of freedom gives rise to new states of matter, including superconductivity and charge-density-wave formation. When exciting such a condensed system, it is commonly assumed that the ultrafast laser pulse disturbs primarily the electronic order, which in turn destabilizes the atomic structure. Contrary to this conception, we show here that structural destabilization of few atoms causes macroscopic melting of the charge-density wave in 1T-TiSe2. In detail, we use ultrafast pump-probe non-resonant and resonant X-ray diffraction to track the periodic lattice distortion and the electronic charge density wave in 1T-TiSe2 upon optical excitation. We observe a fluence regime in which the periodic lattice deformation is strongly suppressed but the charge density wave related Se 4p orbital order remains mostly intact. Complete melting of both structural and electronic order occurs 4-5 times faster than expected from a purely electronic charge-screening process, strongly suggesting a structurally assisted weakening of excitonic correlations. Our experimental data provides insight on the intricate coupling between structural and electronic order in stabilizing the periodic-lattice-distortion/charge-density-wave state in 1T-TiSe2. The results further show that electron-phonon-coupling can lead to different, energy dependent phase-transition pathways in condensed matter systems, opening new possibilities in the conception of non-equilibrium phenomena at the ultrafast scale.

en: Structural involvement in the melting of the charge density wave in 1T-TiSe2

charge-density-wave

display_name: charge-density-wave

paul-scherrer-institute

display_name: paul-scherrer-institute

snsf

display_name: snsf

transient-x-ray-diffraction

display_name: transient-x-ray-diffraction

xfel

display_name: xfel


en: An advanced modeling approach is presented to shed light on the thermal transport properties of van der Waals materials (vdWMs) composed of single-layer transition metal dichalcogenides (TMDs) stacked on top of each other with a total or partial overlap only in the middle region. It relies on the calculation of dynamical matrices from first principles and on their usage in a phonon quantum transport simulator. We observe that vibrations are transferred microscopically from one layer to the other along the overlap region which acts as a filter selecting out the states that can pass through it. Our work emphasizes the possibility of engineering heat flows at the nanoscale by carefully selecting the TMD monolayers that compose vdWMs.

en: Ab initio modeling of thermal transport through van der Waals materials

materialscloud

en: Materials Cloud

thermal-properties

display_name: thermal-properties

transition-metal-dichalcogenides

display_name: transition-metal-dichalcogenides

van-der-waals-materials

display_name: van-der-waals-materials


en: While alloy design has practically shown an efficient strategy to mediate two seemingly conflicted performances of writing speed and data retention in phase-change memory, the detailed kinetic pathway of alloy-tuned crystallization is still unclear. Here, we propose hierarchical melt and coordinate bond strategies to solve them, where the former stabilizes a medium-range crystal-like region and the latter provides a rule to stabilize amorphous. The Er0.52Sb2Te3 compound we designed achieves writing speed of 3.2 ns and ten-year data retention of 161 °C. We provide a direct atomic-level evidence that two neighbor Er atoms stabilize a medium-range crystal-like region, acting as a precursor to accelerate crystallization; meanwhile, the essential reason of stabilization originates from the formation of coordinate bonds by sharing lone-pair electrons of chalcogenide atoms with the empty 5d orbitals of Er atoms. The two rules pave the way for the development of storage-class memory with excellent comprehensive performance to achieve next revolutionary technology node.

en: Designing crystallization to tune the performance of phase-change memory: rules of hierarchical melt and coordinate bond

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).

coordinate-bond

display_name: coordinate-bond

er-sb-te

display_name: er-sb-te

experimental

display_name: experimental

hierarchical-melt

display_name: hierarchical-melt

phase-change-memory

display_name: phase-change-memory


en: Materials for high-temperature environments are actively being investigated for deployment in aerospace and nuclear applications. This study uses computational approaches to unravel the crystallography, and thermodynamics of a promising class of refractory alloys containing aluminum. Accurate first-principles calculations, cluster expansion models, and statistical mechanics techniques are employed to rigorously analyze precipitation in a prototypical senary Al-Nb-Ta-Ti-V-Zr alloy. Finite-temperature calculations reveal a strong tendency for aluminum to segregate to a single sublattice at elevated temperatures. Precipitate and matrix compositions computed with our ab-initio model are in excellent agreement with previous experimental measurements (Soni et al., 2020). Surprisingly, conventional B2-like orderings are found to be both thermodynamically and mechanically unstable in this alloy system. Complex anti-site defects are essential to forming a stable ordered precipitate. Our calculations reveal that the instability of B2 compounds can be related to a simple electron counting rule across all binary alloys formed by elements in groups 4,5, and 6. The results of this study provide viable routes toward designing high-temperature materials for deployment in extreme environments.

en: First-principles thermodynamics of precipitation in aluminum-containing refractory alloys

envi

en: Environment

ab-initio

display_name: ab-initio

alloy-theory

display_name: alloy-theory

cluster-expansion

display_name: cluster-expansion

phase-stability

display_name: phase-stability

precipitation

display_name: precipitation


en: Machine learning (ML) algorithms have undergone an explosive development impacting every aspect of computational chemistry. To obtain reliable predictions, one needs to maintain the proper balance between the black-box nature of ML frameworks and the physics of the target properties. One of the most appealing quantum-chemical properties for regression models is the electron density, and some of us recently proposed a transferable and scalable model based on the decomposition of the density onto an atom-centered basis set. The decomposition, as well as the training of the model, is at its core a minimization of some loss function, which can be arbitrarily chosen and may lead to results of different quality. Well-studied in the context of density fitting (DF), the impact of the metric on the performance of ML models has not been analyzed yet. In this work, we compare predictions obtained using the overlap and the Coulomb repulsion metrics for both the decomposition and training. As expected, the Coulomb metric used as both the DF and ML loss functions leads to the best results for the electrostatic potential and dipole moments. The origin of this difference lies in the fact that the model is not constrained to predict densities that integrate to the exact number of electrons N. Since an a posteriori correction for the number of electrons decreases the errors, we proposed a modification of the model where N is included directly into the kernel function, which allowed to lower the errors on the test and out-of-sample sets.

en: Impact of quantum-chemical metrics on the machine learning prediction of electron density

correction-number-of-electrons

display_name: correction-number-of-electrons

epfl

display_name: epfl

erc

display_name: erc

machine-learning

display_name: machine-learning

quantum-chemical-metrics

display_name: quantum-chemical-metrics


en: Magneto-electric multipoles, which are odd under both space-inversion 𝓘 and time-reversal 𝓣 symmetries, are fundamental in understanding and characterizing magneto-electric materials. However, the detection of these magneto-electric multipoles is often not straightforward as they remain "hidden" in conventional experiments in part since many magneto-electrics exhibit combined 𝓘𝓣 symmetry. In the present work, we show that the anti-symmetric Compton profile is a unique signature for all the magneto-electric multipoles, since the asymmetric magnetization density of the magneto-electric multipoles couples to space via spin-orbit coupling, resulting in an anti-symmetric Compton profile. We develop the key physics of the anti-symmetric Compton scattering using symmetry analysis and demonstrate it using explicit first-principles calculations for two well-known representative materials with magneto-electric multipoles, insulating LiNiPO₄ and metallic Mn₂Au. Our work emphasizes the crucial roles of the orientation of the spin moments, the spin-orbit coupling, and the band structure in generating the anti-symmetric Compton profile in magneto-electric materials.

en: Revealing hidden magneto-electric multipoles using Compton scattering

compton-scattering

display_name: compton-scattering

density-functional-theory

display_name: density-functional-theory

horizon-europe

display_name: horizon-europe

magneto-electric-multipoles

display_name: magneto-electric-multipoles


en: Neural network (NN) force fields can predict potential energy surfaces with high accuracy and speed compared to electronic structure methods typically used to generate their training data. However, NN predictions are well-defined only for points close to the training domains, and may exhibit poor results during extrapolation. Uncertainty quantification methods can detect geometries for which predicted errors are high, but sampling regions of high uncertainty requires a thorough exploration of the phase space, often using expensive simulations. Our work uses automatic differentiation to sample atomistic configurations by balancing thermodynamic accessibility and uncertainty quantification without using molecular dynamics simulations. This dataset provides the atomistic data used to train the NN potentials for the ammonia, alanine dipeptide, and zeolite-molecule systems. For all materials, geometries, energies, and forces are provided. The ammonia and zeolite systems were computed using density functional theory calculations, while the alanine dipeptide dataset was generated using molecular dynamics simulations with the OPLS force field.

en: Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks

molecular-dynamics

display_name: molecular-dynamics

zeolites

display_name: zeolites


en:
This repository has the input files and a guide to recreate the data from "The importance of reference frame for pressure at the liquid-vapour interface" (https://arxiv.org/abs/2107.00499). It requires the Flowmol code to be downloaded and built from https://github.com/edwardsmith999/flowmol (it should work with the latest version but the paper was generated from commit c4a52d434053d676c0281449b0fce7112116fd54 or the persistent version linked to DOI https://doi.org/10.5281/zenodo.4639546). The included README.txt file outlines how to do this. The input files are also included on the Github repository. The summarised data is also included as a Python pickle (summary.p) with scripts to produce all plots from the paper. This data, which shows the profile going through a liquid vapour interface, can be analysed in Python.

The abstract for the article, which explains the importance of this data, is as follows: The local pressure tensor is non-unique, a fact which has generated confusion and debate in the seventy years since the seminal work by Irving Kirkwood. This non-uniqueness is normally attributed to the interaction path between molecules, especially in the interfacial-science community. In this work we reframe this discussion of non-uniqueness in terms of the location, or reference frame, used to measure the pressure. By using a general mathematical description of the liquid-vapour interface, we obtain a reference frame that moves with the interface through time, providing a new insight into the pressure. We compare this instantaneous moving reference frame with the fixed Eulerian one. Through this process, we show the requirement that normal pressure balance at the moving surface is satisfied by surface fluxes, however an additional corrective term based on surface curvature is required for the average pressure in a volume. We make the case that a focus on the path of integration is the cause of much of the confusion in the literature. Using an explicit reference frame with a more general derivation of pressure clarifies some of the issues of uniqueness in the pressure tensor and provides a pressure tensor which is defined at any instant in time and valid away from thermodynamic equilibrium.

flowmol

display_name: flowmol

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

python-pickle

display_name: python-pickle

software-input-files

display_name: software-input-files


en:
The potential profile and the energy level offset of core/shell heterostructured nanocrystals (h-NCs) determine the photophysical properties and the charge transport characteristics of h-NC solids. However, limited material choices for heavy metal-free III-V/II-VI h-NCs pose challenges in comprehensive control of the potential profile. Herein, we present an approach to such control by steering dipole densities at the interface of III-V/II-VI h-NCs. The controllable heterovalency at the interface is responsible for interfacial dipole densities that result in the vacuum-level shift, providing an additional knob for the control of optical and electrical characteristics of h-NCs. The synthesis of h-NCs with atomic precision allows us to correlate interfacial dipole moments with the nanocrystals' photochemical stability and optoelectronic performance.

The description was referred to the abstract of the original paper. All numerical data of the main figures in the articles are included in this data repository.
In particular, each tab of the data file indicates each panel in the main figures.
Please refer to the main research article.

en: Interface polarization in heterovalent core/shell nanocrystals

experimental

display_name: experimental

heterostructured-nanocrystal

display_name: heterostructured-nanocrystal

interface-polarization

display_name: interface-polarization


en: Charge-transfer-to-solvent states in aqueous halides are ideal systems for studying the electron-transfer dynamics to the solvent involving a complex interplay between electronic excitation and solvent polarization. Despite extensive experimental investigations, a full picture of the charge-transfer-to-solvent dynamics has remained elusive. Here, we visualise the intricate interplay between the dynamics of the electron and the solvent polarization occurring in this process. Through the combined use of ab initio molecular dynamics and machine learning methods, we investigate the structure, dynamics and free energy as the excited electron evolves through the charge-transfer-to-solvent process, which we characterize as a sequence of states denoted charge-transfer-to-solvent, contact-pair, solvent-separated, and hydrated electron states, depending on the distance between the iodine and the excited electron. Our assignment of the charge-transfer-to-solvent states is supported by the good agreement between calculated and measured vertical binding energies. Our results reveal the charge transfer process in terms of the underlying atomic processes and mechanisms.

en: Dynamics of the charge transfer to solvent process in aqueous iodide

ab-initio

display_name: ab-initio


en: We present a numerical tool for solving the non-relativistic Kohn-Sham problem for spherically-symmetric atoms. It treats the Schrödinger equation as an integral equation relying heavily on convolutions. The solver supports different types of exchange-correlation functionals including screened and long-range corrected hybrids. We implement a new method for treating range separation based on the complementary error function kernel. The present tool is applied in spin-restricted non-relativistic total energy calculations of atoms. A comparison with ultra-precise reference data[Cinal, JOMC 58, 1571 (2020)] shows a 14-digit agreement for Hartree-Fock results. We provide further benchmark data obtained with 5 different exchange-correlation functionals: VWN5 (the local-density approximation), PBE (the generalized gradient approximation), PBE0 and B3LYP (hybrids with a Fock exchange) and LC-BLYP (hybrid with a long-range corrected exchange).

en: Total energies of atoms from integral-equation radial solver

materialscloud

en: Materials Cloud

atoms

display_name: atoms

hartree-fock

display_name: hartree-fock

high-precision

display_name: high-precision

hybrids

display_name: hybrids


en: Here, we develop a self-assembly technique to synthesize 1-nm-thick rutile-structured high-entropy oxides (RuIrFeCoCrO₂) from naked metal ions assembly and oxidation at air-molten salt interface. The RuIrFeCoCrO₂ only requires an overpotential of 185 mV at 10 m A cm⁻² and maintains the high activity for over 1000 hours in an acidic electrolyte via the adsorption evolution mechanism. In this dataset, we calculated the desolvation coefficient (D) of various cations in aqueous solution and molten salt. In molten salt, the corresponding D for metal ions is higher by several orders of magnitude compared to that in water, which allows metal ions to become freely moving ions. We also conducted DFT calculations to gain insight to the structural stability and reaction mechanism of this HEO. The results show that the reaction mechanism changes from LOM (RuO₂) to AEM (RuIrFeCoCrO₂).

en: Atomically thin high-entropy oxides via naked metal ion self-assembly for proton exchange membrane electrolysis

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).

dft

display_name: dft

electrocatalysis

display_name: electrocatalysis

high-entropy-oxides

display_name: high-entropy-oxides


en: Alloys based on lanthanum phosphate (LaPO₄) are often employed as thermal barrier coatings, due to their low thermal conductivity and structural stability over a wide temperature range. To enhance the thermal-insulation performance of these alloys, it is essential to comprehensively understand the fundamental physics governing their heat conduction. Here, we employ the Wigner formulation of thermal transport in conjunction with first-principles calculations to elucidate how the interplay between anharmonicity and compositional disorder determines the thermal properties of La1-xGdxPO₄ alloys, and discuss the fundamental physics underlying the emergence and coexistence of particle-like and wave-like heat-transport mechanisms. Our predictions for microscopic vibrational properties (temperature-dependent Raman spectrum) and for macroscopic thermal conductivity are validated against experiments. Finally, we leverage these findings to devise strategies to optimize the performance of thermal barrier coatings.

en: First-principles characterization of thermal conductivity in LaPO₄-based alloys

envi

en: Environment

alloys

display_name: alloys

anharmonicity-and-disorder

display_name: anharmonicity-and-disorder

first-principles

display_name: first-principles

thermal-barrier-coating

display_name: thermal-barrier-coating

thermal-transport

display_name: thermal-transport


en: In this paper, we present a workflow that is designed to work without manual intervention to efficiently predict, by using molecular simulations, the thermodynamic data that is needed to design a carbon capture process. We developed a procedure that does not rely on fitting of the adsorption isotherms. From molecular simulations, we can obtain accurate data for both, the pure component isotherms as well as the mixture isotherms. This allowed us to make a detailed comparison of the different methods to predict the mixture isotherms. All approaches rely on an accurate description of the pure component isotherms and a model to predict the mixture isotherms. As we are interested in low CO₂ concentrations, it is essential that these models correctly predict the low pressure limit, i.e., give a correct description of the Henry regime. Among the equations that describe this limit correctly, the dual-site Langmuir (DSL) model is often used for the pure components and the extended DSL (EDSL) for the mixtures. An alternative approach, which avoids describing the pure component isotherms with a model, is to numerically integrate the pure component isotherms in the context of IAST.  In this work we compare these two methods. In addition, we show that the way these data are fitted for DSL can significantly impact the ranking of materials, in particular for capture processes with low concentration of CO₂ in the feed stream.

en: A robust framework for generating adsorption isotherms to screen materials for carbon capture

dual-site-langmuir-dsl

display_name: dual-site-langmuir-dsl

ideal-adsorbed-solution-theory-iast

display_name: ideal-adsorbed-solution-theory-iast

metal-organic-frameworks-mofs

display_name: metal-organic-frameworks-mofs

temperature-swing-adsorption-tsa

display_name: temperature-swing-adsorption-tsa


en: 'Magic'-angle twisted bilayer graphene has received a lot of interest due to its flat bands with potentially non-trivial topology that lead to intricate correlated phases. A spectrum with flat bands, however, does not require a twist between multiple sheets of van der Waals materials, but rather can be realized with the application of an appropriate periodic potential. Here, we propose the imposition of a tailored periodic potential onto a single graphene layer through local perturbations that could be created via lithography or adatom manipulation, which also results in an energy spectrum featuring flat bands. Our first-principle calculations for an appropriate decoration of graphene with adatoms indeed show the presence of flat bands in the spectrum. Furthermore, we reveal the topological nature of the flat bands through a symmetry-indicator analysis. This non-trivial topology manifests itself in corner-localized states with a filling anomaly as we show using a tight-binding model. Our proposal of a single decorated graphene sheet provides a new versatile route to study correlated phases in topologically non-trivial, flat band structures.

en: Flat bands with fragile topology through superlattice engineering on single-layer graphene

graphene

display_name: graphene

marvel

display_name: marvel

topological-materials

display_name: topological-materials

vasp

display_name: vasp


en: The vast amount of computational studies on electrical conduction in solid-state electrolytes is not mirrored by comparable efforts addressing thermal conduction, which has been scarcely investigated despite its relevance to thermal management and (over)heating of batteries. The reason for this lies in the complexity of the calculations: on one hand, the diffusion of ionic charge carriers makes lattice methods formally unsuitable due to the lack of equilibrium atomic positions needed for normal-mode expansion. On the other hand, the prohibitive cost of large-scale molecular dynamics (MD) simulations of heat transport in large systems at ab initio levels has hindered the use of MD-based methods. In this work, we leverage recently developed machine-learning potentials targeting different ab initio functionals (PBEsol, r2SCAN, PBE0) and a state-of-the-art formulation of the Green-Kubo theory of heat transport in multicomponent systems to compute the thermal conductivity of a promising solid-state-electrolyte, Li3PS4, in all its polymorphs (α, β and γ). By comparing MD estimates with lattice methods on the low-temperature, non-diffusive γ-Li3PS4, we highlight strong anharmonicities and negligible nuclear quantum effects, hence further justifying MD-based methods even for non-diffusive phases. Finally, for the ion-conducting α and β phases, where the multicomponent Green-Kubo MD approach is mandatory, our simulations indicate a weak temperature dependence of the thermal conductivity, a glass-like behavior due to the effective local disorder characterizing these Li-diffusing phases.

en: Thermal transport of Li₃PS₄ solid electrolytes with ab initio accuracy

epfl

display_name: epfl

lab-cosmo

display_name: lab-cosmo

machine-learning

display_name: machine-learning

max

display_name: max

snsf

display_name: snsf

solid-state-electrolytes

display_name: solid-state-electrolytes

swissuniversities

display_name: swissuniversities


en:
We propose a variational algorithm to study the real time dynamics of quantum systems as a ground-state problem on quantum devices. The method is based on the original proposal of Feynman and Kitaev to encode time into a register of auxiliary qubits.
We prepare the Feynman-Kitaev Hamiltonian acting on the composed system as a qubit operator and find an approximate ground state using the Variational Quantum Eigensolver. We apply the algorithm to the study of the dynamics of a transverse field Ising chain with an increasing number of spins and time steps, proving a favorable scaling in terms of the number of two qubit gates.
Through numerical experiments, we investigate its robustness against hardware noise, showing that the method can be use to evaluate dynamical properties of quantum systems and detect the presence of dynamical quantum phase transitions by measuring Loschmidt echoes.
The scripts provided implement the algorithm both in Python, using the Qiskit library, and in Julia, using the Yao.jl package.
Moreover, we included the data to reproduce the figures of the manuscript without running the simulations.

en: Variational dynamics as a ground-state problem on a quantum computer

quantum-computing

display_name: quantum-computing

quantum-simulation

display_name: quantum-simulation

real-time-evolution

display_name: real-time-evolution


en: The piecewise linearity condition on the total energy with respect to the total magnetization of finite quantum systems is derived using the infinite-separation-limit technique. This generalizes the well-known constancy condition, related to static correlation error, in approximate density functional theory. The magnetic analog of Koopmans' theorem in density functional theory is also derived. Moving to fractional electron count, the tilted-plane condition is derived, lifting certain assumptions in previous works. This generalization of the flat-plane condition characterizes the total energy surface of a finite system for all values of electron count N and magnetization M. This result is used in combination with tabulated spectroscopic data to show the flat-plane structure of the oxygen atom, among others. We find that derivative discontinuities with respect to electron count sometimes occur at noninteger values. A diverse set of tilted-plane structures is shown to occur in d-orbital subspaces, depending on chemical coordination. General occupancy-based total-energy expressions are demonstrated thereby to be necessarily dependent on the symmetry-imposed degeneracies.

en: Tilted-plane structure of the energy of finite quantum systems

density-functional-theory

display_name: density-functional-theory

static-correlation-error

display_name: static-correlation-error


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

materialscloud

en: Materials Cloud

atomistic-models

display_name: atomistic-models

high-entropy-alloys

display_name: high-entropy-alloys

solute-strengthening

display_name: solute-strengthening


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

materialscloud

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

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

density-of-states

display_name: density-of-states

electronic-structure

display_name: electronic-structure

erc

display_name: erc

marvel

display_name: marvel


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

envi

en: Environment

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


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

snsf

display_name: snsf

three-dimensional-structures

display_name: three-dimensional-structures


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


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


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


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


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

materialscloud

en: Materials Cloud

collective-variables

display_name: collective-variables

enhanced-sampling

display_name: enhanced-sampling

machine-learning

display_name: machine-learning

molecular-dynamics

display_name: molecular-dynamics

protein-folding

display_name: protein-folding

silicon-crystallization

display_name: silicon-crystallization


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)

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).

neural-networks

display_name: neural-networks

temperature-scaling

display_name: temperature-scaling

uncertainty-quantification

display_name: uncertainty-quantification


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

envi

en: Environment

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


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

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

co-oxidation

display_name: co-oxidation

co-redox

display_name: co-redox

operando-xas

display_name: operando-xas

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction


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

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

ab-initio

display_name: ab-initio

quantum-transport

display_name: quantum-transport

thermal-properties

display_name: thermal-properties


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

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

catalysis

display_name: catalysis

enantioselectivity

display_name: enantioselectivity

generality

display_name: generality

genetic-optimization

display_name: genetic-optimization

machine-learning

display_name: machine-learning

nccr-catalysis

display_name: nccr-catalysis

organic-molecules

display_name: organic-molecules

organocatalysis

display_name: organocatalysis


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

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

density-functional-theory

display_name: density-functional-theory

high-throughput

display_name: high-throughput

raman-tensors

display_name: raman-tensors


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

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

dft

display_name: dft

doping

display_name: doping


many-body-self-interaction-and-polarons

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

en: Many-body self-interaction and polarons

materialscloud

en: Materials Cloud

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

electron-screening

display_name: electron-screening

many-body-self-interaction

display_name: many-body-self-interaction

mgo

display_name: mgo

one-body-self-interaction

display_name: one-body-self-interaction

polaron-localization

display_name: polaron-localization

polarons

display_name: polarons

self-interaction

display_name: self-interaction


en: Hydroxyapatite (HA, Ca10PO4(OH)2) is a widely explored material in the experimental domain of biomaterials science, because of its resemblance with natural bone minerals. Specifically, in the bioceramic community, HA doped with multivalent cations (e.g., Mg+2, Fe+2, Sr+2, etc.) has been extensively investigated in the last few decades. Experimental research largely established the critical role of dopant content on the changes in mechanical and biocompatibility properties. The plethora of experimental measurements of mechanical response on doped HA is based on compression or indentation testing of polycrystalline materials. Such measurements, as well as computational predictions of me,  on single crystalline (doped) HA are scarce. On that premise, the present study aims to build atomistic models of Fe2+-doped HA, a model system, with varying Fe content (10, 20, 30, and 40 mol%) and to explore their uniaxial tensile response by means of molecular dynamics (MD) simulation, together with the calculation of IR spectrum. In the equilibrated unit cell structures, Ca(1) sites were found to be energetically favourable for Fe2+ substitution. The local distribution of Fe2+ ions significantly affect the atomic partial charge distribution and chemical symmetry surrounding the functional groups. These signatures are reflected in the significant decrease in the intensity of IR peaks and are found in the Fe-doped HA, together with peak splitting because of the symmetry change in the crystal structure. Another important objective of this work is to computationally predict the mechanical response of doped HA in their single crystal format. An interesting observation is that the elastic anisotropy of undoped HA was not compromised with Fe-doping. Tensile strength (TS) is systematically reduced in doped HA with Fe+2 dopant content and a decrease in TS with temperature can be attributed to the increased thermal agitation of atoms at elevated temperatures. The physics of the tensile response was rationalized in terms of the strain dependent changes in covalent/ionic framework (Ca-P distance, P-O bond strain, O-P-O angular strain, O-H bond distance). Further, the dynamic changes in covalent bond network were energetically analyzed by calculating the changes in O-H and P-O bond vibrational energy. Summarizing, the current work develops our foundational understanding of the atomistic phenomena involved in the phase stability and tensile response of Fe-doped HA single crystals.

en: In silico study on probing atomistic insight into structural stability and tensile properties of Fe-doped hydroxyapatite single crystals

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).

hydroxyapatite

display_name: hydroxyapatite

iron-doping

display_name: iron-doping

md-simulation

display_name: md-simulation

mechanical-response

display_name: mechanical-response


en: We present a first-principles method to calculate electron-phonon coupling elements in atomic systems, and showcase its application to the evaluation of the phonon-limited mobility of n-type single-layer MoS₂. The method combines a density functional theory (DFT) plane-wave supercell approach with a real-space maximally localized Wannier basis. It enables the calculation of electronic structure, phonon displacements with their corresponding frequencies, and real-space electron-phonon coupling elements on the same footing, without the need for density functional perturbation theory (DFPT) or Wannier interpolation. We report a low-field, intrinsic mobility of 274 cm²/Vs at room temperature for MoS₂, and highlight its dependence on carrier density and temperature. In addition, we compare our findings to the latest available modeling data and put them in perspective with the experimentally measured values. Based on these observations, the mobilities presented in this work appear to be compatible with experimental results, when taking into account other scattering sources. Hence, the proposed approach provides a reliable framework for mobility calculations that can be extended towards large-scale device simulations.

en: Electron-phonon calculations using a wannier-based supercell approach: applications to the monolayer MoS₂ mobility

envi

en: Environment

ab-initio

display_name: ab-initio

carrier-transport

display_name: carrier-transport

electron-mobility

display_name: electron-mobility

electron-phonon-coupling

display_name: electron-phonon-coupling

snsf

display_name: snsf


en:
In a recent preprint, entitled: "Development of a machine learning interatomic potential for exploring pressure-dependent kinetics of phase transitions in Germanium", we presented a novel Neural-Network (NN)  interatomic potential for Ge. We recall that Ge phases different from the cubic-diamond one are of particular interest for applications. Hexagonal Ge, for instance, displays superior optical properties. It is therefore important to investigate how, exploiting pressure, Ge can be transformed into different allotropes. In order to build a potential tackling kinetics of pressure-induced phase transformations, several kinetic paths (mainly sampled using the solid-state Nudged Elastic Band method) were added to the database, following a suitable active-learning procedure. Energies, forces, and stressed relative to the various configurations were computed ab initio using VASP with the PBE functional. The NN potential was trained using the Deep Potential Molecular Dynamic package (DeePMDkit). The potential greatly reproduces the relative stability of several Ge phases and yields at least a semi-quantitative description of the energetics along complex phase-transformation paths.
In the present archive, we provide the full potential for use in LAMMPS and ASE, together with the full database produced using VASP.

en: A NN-Potential for phase transformations in Ge

deepmd

display_name: deepmd

germanium

display_name: germanium

neural-network

display_name: neural-network

phase-transitions

display_name: phase-transitions


en: The functionality of atomic quantum emitters is intrinsically linked to their host lattice coordination. Structural distortions that spontaneously break the lattice symmetry strongly impact their optical emission properties and spin-photon interface. In a recent manuscript, we report on the direct imaging of charge state-dependent symmetry breaking of two prototypical atomic quantum emitters in mono- and bilayer MoS₂ by scanning tunneling microscopy (STM) and non-contact atomic force microscopy (nc-AFM). By changing the built-in substrate chemical potential, different charge states of sulfur vacancies (VacS) and substitutional rhenium dopants (ReMo) can be stabilized. VacS⁻¹ as well as ReMo⁰ and ReMo⁻¹ exhibit local lattice distortions and symmetry-broken defect orbitals attributed to a Jahn-Teller effect (JTE) and pseudo-JTE, respectively. By mapping the electronic and geometric structure of single point defects, we disentangle the effects of spatial averaging, charge multistability, configurational dynamics, and external perturbations that often mask the presence of local symmetry breaking. This record contains data to support the results discussed in our manuscript.

en: Charge state-dependent symmetry breaking of atomic defects in transition metal dichalcogenides

dfg

display_name: dfg

erc

display_name: erc

jahn-teller-effect

display_name: jahn-teller-effect

stm

display_name: stm


en: Field-effect transistors (FETs) based on two-dimensional materials (2DMs) with atomically thin channels have emerged as a promising platform for beyond-silicon electronics. However, low carrier mobility in 2DM transistors driven by phonon scattering remains a critical challenge. To address this issue, we propose the controlled introduction of localized tensile strain as an effective mean to inhibit electron-phonon scattering in 2DM. Strain is achieved by conformally adhering the 2DM via van-der-Waals forces to a dielectric layer previously nanoengineered with a gray-tone topography. Our results show that monolayer MoS₂ FETs under tensile strain achieve an 8-fold increase in on-state current, reaching mobilities of 185 cm²/Vs at room temperature, in good agreement with theoretical calculations. The present work on nanotopographic grayscale surface engineering and the use of high-quality dielectric materials has the potential to find application in the nanofabrication of photonic and nanoelectronic devices. Here we report the main data and codes needed to reproduce the theoretical findings of this work.

en: Deterministic grayscale nanotopography to engineer mobilities in strained MoS₂ FETs

density-functional-theory

display_name: density-functional-theory

first-principles

display_name: first-principles

transport

display_name: transport


en: Mn₅Si₃ displays an unusual and complex magnetic ground state, which is considered to be the origin of the anomalous transport and thermodynamic properties that it exhibits. We report the magnetic exchange couplings of the noncollinear antiferromagnetic phase of Mn₅Si₃ using inelastic neutron scattering measurements and density functional theory calculations. We determine the ground-state spin configuration and compute its magnon dispersion relations which are in good agreement with the ones obtained experimentally. Furthermore, we investigate the evolution of the spin texture under the application of an external magnetic field to demonstrate theoretically the multiple field-induced phase transitions that Mn₅Si₃ undergoes. Finally, we model the stability of some of the material's magnetic moments under a magnetic field and we find that very susceptible magnetic moments in a frustrated arrangement can be tuned by the field. This data set contains the data relevant to perform the DFT calculations with the JuKKR code, the spin dynamics simulations with the Spirit code, the spin-wave calculations with the SWIS code, and the neutron scattering experimental data.

en: Complex magnetic structure and spin waves of the noncollinear antiferromagnet Mn₅Si₃

antiferromagnetism

display_name: antiferromagnetism

frustration

display_name: frustration

inelastic-neutron-scattering

display_name: inelastic-neutron-scattering

intersect

display_name: intersect

marvel

display_name: marvel

noncollinear-magnetism

display_name: noncollinear-magnetism

spin-waves

display_name: spin-waves


fatigue-database-of-high-entropy-alloys

en: Fatigue failure of metallic structures is of great concern to industrial applications. A material will not be able to practically useful if it is prone to fatigue failure. To take the advantage of lately emerged high entropy alloys (HEAs) for designing novel fatigue-resistant alloys, we compiled a fatigue database of HEAs from the literature reported till the yearend of 2021. The database is subdivided into three categories, i.e., low-cycle fatigue (LCF), high-cycle fatigue (HCF), and fatigue crack growth rate (FCGR), which contains 15, 23, and 28 distinct data records, respectively. Each data record in any of three categories is characteristic of a summary, which is comprised of alloy composition, key fatigue properties, and additional information influential to or interrelated with fatigue (e.g., material processing history, phase constitution, grain size, uniaxial tensile properties, and fatigue testing conditions), and an individual dataset, which makes up the original fatigue testing curve.

en: Fatigue database of high entropy alloys

title_for_slug: fatigue-database-of-high-entropy-alloys

fatigue-crack-growth-rate

display_name: fatigue-crack-growth-rate

high-cycle-fatigue

display_name: high-cycle-fatigue

high-entropy-alloy

display_name: high-entropy-alloy

low-cycle-fatigue

display_name: low-cycle-fatigue


en:
We use a combination of symmetry analysis, phenomenological modeling, and first-principles density functional theory to explore the interplay between the magnetic ground state and the detailed atomic structure in the hexagonal rare-earth manganites. We find that the magnetic ordering is sensitive to a breathing mode distortion of the Mn and O ions in the ab plane, which is described by the K1 mode of the high-symmetry structure. Our density functional calculations of the magnetic interactions indicate that this mode particularly affects the single-ion anisotropy and the interplanar symmetric exchanges. By extracting the parameters of a magnetic model Hamiltonian from our first-principles results, we develop a phase diagram to describe the magnetic structure as a function of the anisotropy and exchange interactions. This in turn allows us to explain the dependence of the magnetic ground state on the identity of the rare-earth ion and on the K1 mode.
The attached files contain VASP input files and bash scripts for running all the DFT calculations in the manuscript.

en: Influence of the triangular Mn-O breathing mode on magnetic ordering in multiferroic hexagonal manganites

materialscloud

en: Materials Cloud

ab-initio

display_name: ab-initio

dftu

display_name: dftu

erc

display_name: erc


en: Dimensionality provides a clear fingerprint on the dispersion of infrared-active, polar-optical phonons. For these phonons, the local dipoles parametrized by the Born effective charges drive the LO-TO splitting of bulk materials; this splitting actually breaks down in two-dimensional materials. Here, we develop the theory for one-dimensional (1D) systems -nanowires, nanotubes, and atomic and polymeric chains. Combining an analytical model with the implementation of density-functional perturbation theory in 1D boundary conditions, we show that the dielectric splitting in the dispersion relations collapses as x²log(x) at the zone center. The dielectric properties and the radius of the 1D materials are linked by the present work to these red shifts, opening infrared and Raman characterization avenues.

en: Infrared-active phonons in one-dimensional materials and their spectroscopic signatures

materialscloud

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

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

density-functional-theory

display_name: density-functional-theory

one-dimensional-materials

display_name: one-dimensional-materials

phonons

display_name: phonons

raman

display_name: raman


en:
Dynamical potentials appear in many advanced electronic-structure methods, including self-energies from many-body perturbation theory, dynamical mean-field theory, electronic-transport formulations, and many embedding approaches. Here, we propose a novel treatment for the frequency dependence, introducing an algorithmic inversion method that can be applied to dynamical potentials expanded as sum-over-poles. This approach allows for an exact solution of Dyson-like equations at all frequencies via a mapping to a matrix diagonalization, and provides simultaneously frequency-dependent (spectral) and frequency-integrated (thermodynamic) properties of the Dyson-inverted propagators.
The transformation to a sum-over-poles is performed introducing n-th order generalized Lorentzians as an improved basis set to represent the spectral function of a propagator.
Numerical results for the homogeneous electron gas at the G0W0 level are provided to argue for the accuracy and efficiency of such unified approach.
In this record, we provide the input and outputs of the AGWX suite (see article) used in the work. Also, we add the plots of the obtained Green's function for all densities, k-points, and frequencies.

en: A unified Green's function approach for spectral and thermodynamic properties from algorithmic inversion of dynamical potentials

envi

en: Environment

computational-materials-science

display_name: computational-materials-science

homogeneous-electron-gas

display_name: homogeneous-electron-gas

many-body-perturbation-theory-gw

display_name: many-body-perturbation-theory-gw

max

display_name: max

snsf

display_name: snsf


en: The microscopic mechanism of heavy band formation, relevant for unconventional superconductivity in CeCoIn₅ and other Ce-based heavy fermion materials, depends strongly on the efficiency with which f electrons are delocalized from the rare earth sites and participate in a Kondo lattice. Replacing Ce³⁺ (4f 1, J = 5/2) with Sm³⁺ (4f 5, J = 5/2), we show that a combination of crystal field and on-site Coulomb repulsion causes SmCoIn₅ to exhibit a Γ7 ground state similar to CeCoIn5 with multiple f electrons. Remarkably, we also find that with this ground state, SmCoIn₅ exhibits a temperature-induced valence crossover consistent with a Kondo scenario, leading to increased delocalization of f holes below a temperature scale set by the crystal field, Tv ≈ 60 K. Our result provides evidence that in the case of many f electrons, the crystal field remains the most important tuning knob in controlling the efficiency of delocalization near a heavy fermion quantum critical point, and additionally clarifies that charge fluctuations are present and may play a generally important role in the ground state of "115" materials.

en: Charge fluctuations in the intermediate-valence ground state of SmCoIn₅

heavy-fermions

display_name: heavy-fermions

marvel

display_name: marvel

mixed-valence

display_name: mixed-valence


en: Linear scaling relations have led to an understanding of trends in catalytic activity and selectivity of many reactions in heterogeneous and electro-catalysis. Yet, linear scaling between the chemisorption energies of any two small molecule adsorbates is not guaranteed. A prominent example is the lack of scaling between the chemisorption energies of carbon and oxygen on transition metal surfaces. In this work, we show that this lack of scaling originates from different re-normalised adsorbate valence energies of lower-lying oxygen versus higher-lying carbon. We develop a model for chemisorption of small molecule adsorbates within the d-band model by combining a modified form of the Newns-Anderson hybridisation energy with an effective orthogonalization term. We develop a general descriptor to a priori determine if two adsorbates are likely to scale with each other. This record contains the AiiDA archive required to reproduce all calculations in the manuscript.

en: Limits to scaling relations between adsorption energies?

catalysis

display_name: catalysis

d-band-model

display_name: d-band-model

scaling-relations

display_name: scaling-relations


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

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

bcc-iron

display_name: bcc-iron

fracture

display_name: fracture

gaussian-approximation-potential

display_name: gaussian-approximation-potential


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

en: Temperature dependent properties of the aqueous electron

ab-initio-molecular-dynamics

display_name: ab-initio-molecular-dynamics

aqueous-electron

display_name: aqueous-electron

hybrid-functional

display_name: hybrid-functional

machine-learning

display_name: machine-learning


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

en: Developments and further applications of ephemeral data derived potentials

hydrides

display_name: hydrides

machine-learned-interatomic-potentials

display_name: machine-learned-interatomic-potentials

metal-organic-frameworks

display_name: metal-organic-frameworks

molecular-dynamics

display_name: molecular-dynamics

phase-diagram

display_name: phase-diagram

phonons

display_name: phonons

superionicity

display_name: superionicity


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

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

materialscloud

en: Materials Cloud

ab-initio

display_name: ab-initio

electron-phonon-coupling

display_name: electron-phonon-coupling


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

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

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).

oxygen-exchange

display_name: oxygen-exchange

surface-chemistry

display_name: surface-chemistry

work-function

display_name: work-function


en: We show that hematite, 𝛼-Fe₂O₃, below its Morin transition, has a ferroic ordering of rank-5 magnetic triakontadipoles on the Fe ions. In the absence of spin-orbit coupling, these are the lowest-order ferroically aligned magnetic multipoles, and they give rise to the g-wave non-relativistic spin splitting in hematite. We find that the ferroically ordered magnetic triakontadipoles result from the simultaneous antiferroic ordering of the charge hexadecapoles and the magnetic dipoles, providing a route to manipulating the magnitude and the sign of the magnetic triakontadipoles as well as the spin splitting. Furthermore, we find that both the ferroic ordering of the magnetic triakontadipoles and many of the spin-split features persist in the weak ferromagnetic phase above the Morin transition temperature.

en: Non-relativistic ferromagnetotriakontadipolar order and spin splitting in hematite

envi

en: Environment

erc

display_name: erc

multipoles

display_name: multipoles


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

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

density-functional-theory-calculations

display_name: density-functional-theory-calculations

electric-dipole-strength

display_name: electric-dipole-strength

oxygen-reduction-reaction

display_name: oxygen-reduction-reaction

support-polarization

display_name: support-polarization