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

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

auni

display_name: auni

misfit-volume

display_name: misfit-volume

solute-strengthening-theory

display_name: solute-strengthening-theory

stacking-fault-energy

display_name: stacking-fault-energy

yield-strength

display_name: yield-strength


$oid: OBSOLETE

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

en: Temperature dependent properties of the aqueous electron

materialscloud

approval_status: approved

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

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

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

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

marvel

display_name: marvel


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

en: Accelerating GW calculations through machine learned dielectric matrices

ab-initio

display_name: ab-initio

dft

display_name: dft


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

en: Topological data analysis of superionic conductor silver iodide

ion-migration

display_name: ion-migration

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

persistence-diagram

display_name: persistence-diagram


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

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

dfpt

display_name: dfpt

magnon-phonon-coupling

display_name: magnon-phonon-coupling

magnons

display_name: magnons

phonons

display_name: phonons

quantum-espresso

display_name: quantum-espresso

tddft

display_name: tddft


en:
Super-resolution microscopy has become a powerful tool to investigate the internal structure of complex colloidal and polymeric systems, such as microgels, at the nanometer scale. An interesting feature of this method is the possibility of monitoring microgel response to temperature changes in situ. However, when performing advanced microscopy experiments, interactions between the particle and the environment can be important. Often microgels are deposited on a substrate, since they have to remain still for several minutes during the experiment. In the publication associated with this data, we use direct stochastic optical reconstruction microscopy (dSTORM) and advanced coarse-grained molecular dynamics simulations to investigate how individual microgels anchored on hydrophilic and hydrophobic surfaces undergo their volume phase transition with temperature. We find that, in the presence of a hydrophilic substrate, the structure of the microgel is unperturbed and the resulting density profiles quantitatively agree with simulations performed under bulk conditions. Instead, when a hydrophobic surface is used, the microgel spreads at the interface and an interesting competition between the two hydrophobic strengths, monomer–monomer vs monomer–surface, comes into play at high temperatures.
The data deposited on the Materials Cloud Archive contains the initial microgel configuration in LAMMPS read format, the calculated radii and the density profiles under the different temperatures and surfaces.

en: Probing temperature responsivity of microgels and its interplay with a solid surface by super-resolution microscopy and numerical simulations

cineca-iscra

display_name: cineca-iscra

experimental

display_name: experimental

microgels

display_name: microgels

molecular-dynamics

display_name: molecular-dynamics

volume-phase-transition

display_name: volume-phase-transition


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

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

ferroelectricity

display_name: ferroelectricity

quantum-paraelectricity

display_name: quantum-paraelectricity


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

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

density-functional-theory

display_name: density-functional-theory

machine-learning

display_name: machine-learning


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

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

fracture-toughness

display_name: fracture-toughness

high-entropy-alloys

display_name: high-entropy-alloys

impact-energy

display_name: impact-energy

impact-toughness

display_name: impact-toughness


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

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

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).

nodal-point-superconductivity

display_name: nodal-point-superconductivity

scaning-tunneling-microscopy

display_name: scaning-tunneling-microscopy

topological-quantum-materials

display_name: topological-quantum-materials


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

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

materialscloud

en: Materials Cloud

envi

en: Environment

epfl

display_name: epfl

marvel

display_name: marvel

materials-selection

display_name: materials-selection


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

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

band-gap

display_name: band-gap

dftuv

display_name: dftuv

halide-perovskite

display_name: halide-perovskite


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

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

amorphous

display_name: amorphous

chalcogenides

display_name: chalcogenides

disorder

display_name: disorder

intersect

display_name: intersect

molecular-dynamics

display_name: molecular-dynamics

openmodel

display_name: openmodel


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

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

dftu

display_name: dftu

first-principles

display_name: first-principles

quantum-espresso

display_name: quantum-espresso


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

en: Developments and further applications of ephemeral data derived potentials

hydrides

display_name: hydrides

machine-learned-interatomic-potentials

display_name: machine-learned-interatomic-potentials

metal-organic-frameworks

display_name: metal-organic-frameworks

phase-diagram

display_name: phase-diagram

phonons

display_name: phonons

superionicity

display_name: superionicity


en: 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

ab-initio

display_name: ab-initio

electron-phonon-coupling

display_name: electron-phonon-coupling


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

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

ambipolar-charge-transfer

display_name: ambipolar-charge-transfer

cscs

display_name: cscs

fullerenes

display_name: fullerenes

intermolecular-interactions

display_name: intermolecular-interactions

non-contact-atomic-force-microscopy

display_name: non-contact-atomic-force-microscopy

organic-hybrid-electronics

display_name: organic-hybrid-electronics

scanning-tunneling-microscopy

display_name: scanning-tunneling-microscopy

snsf

display_name: snsf


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

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

materialscloud

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

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

density-functional-theory

display_name: density-functional-theory

hybrid-functional

display_name: hybrid-functional

quantum-espresso

display_name: quantum-espresso


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

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

materialscloud

en: Materials Cloud

oxygen-exchange

display_name: oxygen-exchange

surface-chemistry

display_name: surface-chemistry

work-function

display_name: work-function


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

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

envi

en: Environment

dftu

display_name: dftu

dftuv

display_name: dftuv

extended-hubbard-functionals

display_name: extended-hubbard-functionals

li-ion-batteries

display_name: li-ion-batteries

lithium-intercalation-voltage

display_name: lithium-intercalation-voltage

marvelosp

display_name: marvelosp

spinel-cathode-materials

display_name: spinel-cathode-materials


en: Perovskite oxynitrides are, due to their reduced band gap compared to oxides,  promising materials for photocatalytic applications. They are most commonly synthesized from {110} layered Carpy-Galy (A₂B₂O₇) perovskites via thermal ammonolysis, i.e. the exposure to a flow of ammonia at elevated temperature. The conversion of the layered oxide to the non-layered oxynitride must involve a complex combination of nitrogen incorporation, oxygen removal and ultimately structural transition by elimination of the interlayer shear plane. Despite the process being commonly used, little is known about the microscopic mechanisms and hence factors that could ease the conversion. Here we aim to derive such insights via density functional theory calculations of the defect chemistry of the oxide and the oxynitride as well as the oxide's surface chemistry. Our results point to the crucial role of surface oxygen vacancies in forming clusters of NH₃ decomposition products and in incorporating N, most favorably substitutionally at the anion site. N then spontaneously diffuses away from the surface, more easily parallel to the surface and in interlayer regions, while diffusion perpendicular to the interlayer plane is somewhat slower. Once incorporation and diffusion lead to a local N concentration of about 70 % of the stoichiometric oxynitride composition, the nitridated oxide spontaneously transforms to a nitrogen-deficient oxynitride.

en: Conversion of La₂Ti₂O₇ to LaTiO₂N via ammonolysis: An ab-initio investigation

ammonolysis

display_name: ammonolysis

defects

display_name: defects

marvel

display_name: marvel

oxynitride

display_name: oxynitride


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

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

fracture-anisotropy

display_name: fracture-anisotropy

fracture-mechanics

display_name: fracture-mechanics

graphene

display_name: graphene

machine-learning

display_name: machine-learning

neural-newrok-force-field

display_name: neural-newrok-force-field


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

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

nmr-crystallography

display_name: nmr-crystallography

solid-state-nmr

display_name: solid-state-nmr

verinurad

display_name: verinurad


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

erc

display_name: erc

multipoles

display_name: multipoles


en: Electrosynthesis of urea from CO2 and NOX provides an exceptional opportunity for human society, given the increasingly available renewable energy. To raise the overall electrosynthesis efficiency, the most critical reaction step for such electrosynthesis, C-N coupling, needs to be significantly improved. The C-N coupling can only happen at a narrow potential window, generally in the low overpotential region, and a fundamental understanding of the C-N coupling is needed for further development of this strategy. In this regard, we performed ab initio Molecular Dynamics (AIMD) simulations to reveal the origin of C-N coupling under a small electrode potential window with both the dynamic nature of water as a solvent, and the electrode potentials considered. We explored the key reaction networks for urea formation on Cu(100) surface in neutral electrolytes. Our work shows excellent agreement with experimentally observed selectivity under different potentials on the Cu electrode. We discovered that the *NH and *CO are the key precursors for C-N bonds formation at low overpotential, while at high overpotential the C-N coupling occurs between adsorbed *NH and solvated CO. These insights provide vital information for future spectroscopic measurements and enable us to design new electrochemical systems for more value-added chemicals.

en: Mechanism of C-N bonds formation in electrocatalytic urea production revealed by ab initio molecular dynamics simulation

aimd

display_name: aimd

electrocatalysis

display_name: electrocatalysis

nox-reduction

display_name: nox-reduction


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

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

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).

free-energy

display_name: free-energy

photoswitchable-organocatalyst

display_name: photoswitchable-organocatalyst


en:
In the past few years, the theory of thermal transport in amorphous solids has been substantially extended beyond the Allen-Feldman model. The resulting formulation, based on the Green-Kubo linear response or the Wigner-transport equation, bridges this model for glasses with the traditional Boltzmann kinetic approach for crystals. The computational effort required by these methods usually scales as the cube of the number of atoms, thus severely limiting the size range of computationally affordable glass models. Leveraging hydrodynamic arguments, we show how this issue can be overcome through a simple formula to extrapolate a reliable estimate of the bulk thermal conductivity of glasses from finite models of moderate size. We showcase our findings for realistic models of paradigmatic glassy materials.
This repository contains example inputs to compute the hydrodynamic extrapolation of the thermal conductivity of glasses.
Explicit examples are for amorphous silicon. Optimized atomic configurations are provided also for amorphous silica and silicon carbide.

en: Hydrodynamic finite-size scaling of the thermal conductivity in glasses

materialscloud

en: Materials Cloud

amorphous-solids

display_name: amorphous-solids

glasses

display_name: glasses

max

display_name: max

size-effects

display_name: size-effects

thermal-transport

display_name: thermal-transport


en: Graph neural networks are a popular deep-learning architecture in applications to materials and molecules, and the most widespread implementations rely on interatomic distances as geometric descriptors. Unfortunately, GNNs based on distances are not complete, i.e. there are geometries, corresponding to molecules and/or periodic structures, that are indistinguishable by the GNN. For these, the corresponding machine-learning models will be unable to learn differences in the properties of the "degenerate" structures. This dataset contains a collection of molecular and solid structures that cannot be discriminated by distance-based graph neural networks, together with example code showing how to parse them and use to demonstrate the shortcomings of this class of machine-learning algorithms.

en: Incompleteness of graph neural networks for points clouds in three dimensions

envi

en: Environment

graph-neural-networks

display_name: graph-neural-networks

machine-learning

display_name: machine-learning

marvel

display_name: marvel

point-clouds

display_name: point-clouds


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

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

antiferromagnets

display_name: antiferromagnets

magnetostatic-spin-waves

display_name: magnetostatic-spin-waves

spin-transport

display_name: spin-transport

time-resolved-kerr-microscopy

display_name: time-resolved-kerr-microscopy


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

en: Conical spin order with chiral quadrupole helix in CsCuCl₃

experimental

display_name: experimental


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

en: Synchronized crystallization in tin-lead perovskite solar cells

halide-perovskite

display_name: halide-perovskite

lead-iodide

display_name: lead-iodide

tin-iodide

display_name: tin-iodide


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

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

snsf

display_name: snsf

trinity-college

display_name: trinity-college


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

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

ab-initio

display_name: ab-initio

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

verification-and-validation

display_name: verification-and-validation

zero-point-renormalization

display_name: zero-point-renormalization


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

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

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

materialscloud

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

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

bogoliubov-de-gennes

display_name: bogoliubov-de-gennes

jukkr

display_name: jukkr

spin-orbit-coupling

display_name: spin-orbit-coupling

superconductivity

display_name: superconductivity


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

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

materialscloud

en: Materials Cloud

iv

display_name: iv

multiple-junction

display_name: multiple-junction

parameter-extraction-methods

display_name: parameter-extraction-methods

solar-energy

display_name: solar-energy


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

en: Machine learning on multiple topological materials datasets

envi

en: Environment

database

display_name: database

machine-learning

display_name: machine-learning

topological-materials

display_name: topological-materials


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

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

density-functional-theory-calculations

display_name: density-functional-theory-calculations

electric-dipole-strength

display_name: electric-dipole-strength

oxygen-reduction-reaction

display_name: oxygen-reduction-reaction

support-polarization

display_name: support-polarization


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

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

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

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

battery-electrolyte

display_name: battery-electrolyte

big-map

display_name: big-map

conductivity

display_name: conductivity

density

display_name: density

materials-acceleration-platform-map

display_name: materials-acceleration-platform-map

polis

display_name: polis

radial-distribution-function-rdf

display_name: radial-distribution-function-rdf

viscosity

display_name: viscosity


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

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

dft

display_name: dft

epitaxial-interfaces

display_name: epitaxial-interfaces

fleur

display_name: fleur

hybrid-devices

display_name: hybrid-devices

interface-engineering

display_name: interface-engineering

josephson-junctions

display_name: josephson-junctions

jukkr

display_name: jukkr

majorana-platform

display_name: majorana-platform

superconductors

display_name: superconductors

tmdc

display_name: tmdc

topological-insulator

display_name: topological-insulator

topological-superconductivity

display_name: topological-superconductivity


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

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

dirac-cone

display_name: dirac-cone

magnons

display_name: magnons

marvel

display_name: marvel

quantum-espresso

display_name: quantum-espresso

tddft

display_name: tddft


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

en: Electronic excited states from physically-constrained machine learning

epfl

display_name: epfl

erc

display_name: erc

excited-states

display_name: excited-states

fiamma

display_name: fiamma

hamiltonian

display_name: hamiltonian

lifetimes

display_name: lifetimes

machine-learning

display_name: machine-learning


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

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

materialscloud

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

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

aqueous-hydroxyl-radical

display_name: aqueous-hydroxyl-radical

aqueous-sulfanyl-radical

display_name: aqueous-sulfanyl-radical

density-corrected-dft

display_name: density-corrected-dft

density-functional-theory

display_name: density-functional-theory

hydrogen-sulfide

display_name: hydrogen-sulfide

methanethiol

display_name: methanethiol


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

en: Solute strengthening of prism edge dislocations in Mg alloys

materialscloud

en: Materials Cloud

dft

display_name: dft

edge-solute-strengthening

display_name: edge-solute-strengthening

mg-zn

display_name: mg-zn

prismatic-slip

display_name: prismatic-slip

solutedislocation-interaction-energy

display_name: solutedislocation-interaction-energy


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

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

envi

en: Environment

active-learning

display_name: active-learning

chemical-reactions

display_name: chemical-reactions

free-energy

display_name: free-energy

neural-network-potential

display_name: neural-network-potential

silicates

display_name: silicates


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

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

carbon-monoxide

display_name: carbon-monoxide

ceria

display_name: ceria

infrarred

display_name: infrarred

palladium

display_name: palladium

spectroscopy

display_name: spectroscopy


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

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

cobalt-oxides

display_name: cobalt-oxides

electrocatalytic-interface

display_name: electrocatalytic-interface

operando-x-ray-adsorption-spectroscopy

display_name: operando-x-ray-adsorption-spectroscopy

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction


en: Materials demonstrating positive thermal expansion (PTE) or negative thermal expansion (NTE) are quite common, whereas those exhibiting zero thermal expansion (ZTE) are notably scarce. In this work, we identify the mechanical descriptors, namely in-plane tensile stiffness and out-of-plane bending stiffness, that can effectively classify PTE and NTE 2D crystals. By utilizing high throughput calculations and the state-of-the-art symbolic regression method, these descriptors aid in the discovery of ZTE or 2D Invar monolayers with the linear thermal expansion coefficient (LTEC) within ±2×10⁻⁶ K⁻¹ in the middle range of temperatures. Additionally, the descriptors assist the discovery of large PTE and NTE 2D monolayers with the LTEC larger than ±15×10⁻⁶ K⁻¹, which are so-called 2D anti-Invar monolayers. Advancing our understanding of materials with exceptionally low or high thermal expansion is of substantial scientific and technological interest, particularly in developing next-generation electronics at the nanometer even Ångstrom scale.

en: Machine learning enables the discovery of 2D Invar and anti-Invar monolayers

bending-stiffness

display_name: bending-stiffness

linear-coefficient-of-thermal-expansion

display_name: linear-coefficient-of-thermal-expansion

machine-learning

display_name: machine-learning

symbolic-regression

display_name: symbolic-regression

thermal-expansion

display_name: thermal-expansion

zero-thermal-expansion

display_name: zero-thermal-expansion


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

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

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

copper

display_name: copper

density-functional-theory

display_name: density-functional-theory

graphene

display_name: graphene

graphene-growth

display_name: graphene-growth

metal-substrate

display_name: metal-substrate


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

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

dft

display_name: dft

pbe

display_name: pbe


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

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

materialscloud

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

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

metal-halide-perovskite

display_name: metal-halide-perovskite

non-collinear-magnetism

display_name: non-collinear-magnetism

snsf

display_name: snsf

spectral-functionals

display_name: spectral-functionals

spin-dependent-interactions

display_name: spin-dependent-interactions

spin-orbit-coupling

display_name: spin-orbit-coupling

spin-torque

display_name: spin-torque

transition-metal-dichalcogenide

display_name: transition-metal-dichalcogenide


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

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

materialscloud

en: Materials Cloud

eley-rideal-mechanism

display_name: eley-rideal-mechanism


en: The design of stealthy hyperuniform (SHU) materials has been a critical topic in realizing bandgap materials without crystalline order. Most previous approaches to constructing SHU materials, such as the collective coordinate method, have assumed the closed system, maintaining the number of particles inside a system during the design process. Here, I develop the concept of evolving wave networks, allowing for the open-system design of disordered materials based on the evolution process. The programs are applied to introduce the concept of evolving wave networks (Code_Set_Fig_2), classify material states according to network parameters (Code_Set_Fig_3), generate the stealthy hyperuniformity (SHU) shielding of existing materials (Code_Set_Fig_4), realize preferential attachment in evolving wave networks (Code_Set_Fig_5), and obtain the following phase diagram of disordered materials (Code_Set_Fig_6).

en: Evolving scattering networks for material classification, stealthy hyperuniform shielding, preferential attachment, and material phase diagram

envi

en: Environment

disordered-material

display_name: disordered-material

evolution

display_name: evolution

network

display_name: network

scattering

display_name: scattering


en: We present a combined density-functional theory and single-site dynamical mean-field theory (DMFT) study of vanadium dioxide (VO₂) using an unconventional set of bond-centered orbitals as the basis of the correlated subspace. VO₂ is a prototypical material undergoing a metal-insulator transition (MIT), hosting both intriguing physical phenomena and the potential for industrial applications. With our choice of correlated subspace basis, we investigate the interplay of structural dimerization and electronic correlations in VO₂ in a computationally cheaper way compared to other state-of-the-art methods, such as cluster DMFT. Our approach allows us to treat the rutile and M1 monoclinic VO₂ phases on an equal footing and to vary the dimerizing distortion continuously, exploring the energetics of the transition between the two phases. The choice of basis presented in this work hence offers a complementary view on the long-standing discussion of the MIT in VO₂ and suggests possible future extensions to other similar materials hosting molecular-orbital-like states.

en: Single-site DFT+DMFT for vanadium dioxide using bond-centered orbitals

dftdmft

display_name: dftdmft


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

benzene

display_name: benzene

liquid-crystals

display_name: liquid-crystals

machine-learning

display_name: machine-learning

methods

display_name: methods

quantum-espresso

display_name: quantum-espresso


en:
In this work we study isomers of several representative small clusters to find principles for their stability. Our conclusions about the principles underlying the structure of clusters are based on a huge database of 44'000 isomers generated for 59 different clusters on the density functional theory level by Minima Hopping. We explore the potential energy surface of small neutral, anionic and cationic isomers, moving left to right across the third period of the periodic table and varying the number of atoms n and the cluster charge state q (X^q_n, with X={Na, Mg, Al, Si, Ge}, q=-1,0,1,2). We use structural descriptors such as bond lengths and atomic coordination numbers, the surface to volume ratios and the shape factor as well as electronic descriptors such as shell filling and hardness to detect correlations with the stability of clusters. The isomers of metallic clusters are found to be structure seekers with a strong tendency to adopt compact  shapes. However certain numbers of atoms can suppress the formation of a nearly spherical metallic clusters. Small non-metallic clusters typically also do not adopt compact spherical shapes for their lowest energy structures. In both cases spherical jellium models are not any more applicable. Nevertheless for many  structures, that have frequently a high degree of symmetry, the Kohn-Sham eigenvalues are bunched into shells and if the available electrons can completely fill such shells, a particularly stable structure can result. We call such a cluster whose shape gives rise to shells that can be completely filled by the number of available electrons an optimally matched cluster, since both the structure and the number of electrons must be special and match. In this way we can also explain the stability trends for covalent silicon and germanium cluster isomers, whose stability was previously explained by the presence of certain structural motifs. Thus we propose a unified framework to explain trends in the stability of isomers and to predict their structure for a wide range of small clusters.

This data record contains structure coordinates (compressed file all_minima_xyz_structures.tar.gz) of all minima found by the minima hopping method for all neutral and charged clusters, that are 43606 different structures. The python dictionary all_minima_names.npy stores all minima names. The python dictionary all_minima_data.npy stores all structural and electronic properties calculated for all minima. The jupyter notebook all_minima_data_analysis.ipynb helps the reading of the all_minima_names.npy and all_minima_data.npy python dictionaries.

en: Principles of isomer stability in small clusters

atomic-clusters

display_name: atomic-clusters

cscs

display_name: cscs

dft

display_name: dft

snsf

display_name: snsf


en:
We present SIESTA-BdG, an implementation of the simultaneous solution of the Bogoliubov-de Gennes (BdG) and Density Functional Theory (DFT) problem in SIESTA, a first-principles method and code for material simulations which uses pseudopotentials and a localized basis set. This unified approach describes both conventional and unconventional superconducting states, and enables a description of inhomogeneous superconductors and heterostructures. We demonstrate the validity, accuracy, and efficiency of SIESTA-BdG by computing physically relevant quantities (superconducting charge density, band structure, superconducting gap features, density of states) for conventional singlet (Nb, Pb) and unconventional (FeSe) superconductors. We find excellent agreement with experiments and results obtained within the KKR-BdG computational framework. SIESTA-BdG forms the basis for modelling quantum transport in superconducting devices and including - in an approximate fashion - the superconducting DFT potential of Oliveira, Gross, and Kohn.

This dataset contains the calculations performed on bulk Nb and the associated notebooks used to generate figures from this data.

en: Density functional Bogoliubov-de Gennes theory for superconductors implemented in the SIESTA code

aiida

display_name: aiida

aiida-siesta

display_name: aiida-siesta

bogoliubov-de-gennes

display_name: bogoliubov-de-gennes

density-functional-theory

display_name: density-functional-theory

horizon-europe

display_name: horizon-europe

marie-curie-fellowship

display_name: marie-curie-fellowship

max

display_name: max

nb

display_name: nb

prace

display_name: prace

siesta

display_name: siesta

superconductivity

display_name: superconductivity


en: The phase of the quantum-mechanical wave function can encode a topological structure with wide-ranging physical consequences, such as anomalous transport effects and the existence of edge states robust against perturbations. While this has been exhaustively demonstrated for electrons, properties associated with the elementary quasiparticles in magnetic materials are still underexplored. Here, we show theoretically and via inelastic neutron scattering experiments that the bulk ferromagnet Mn₅Ge₃ hosts gapped topological Dirac magnons. Although inversion symmetry prohibits a net Dzyaloshinskii-Moriya interaction in the unit cell, it is locally allowed and is responsible for the gap opening in the magnon spectrum. This gap is predicted and experimentally verified to close by rotating the magnetization away from the c-axis. Hence, Mn₅Ge₃ is the first realization of a gapped Dirac magnon material in three dimensions. Its tunability by chemical doping or by thin film nanostructuring defines an exciting new platform to explore and design topological magnons.

en: Topological magnons driven by the Dzyaloshinskii-Moriya interaction in the centrosymmetric ferromagnet Mn₅Ge₃

dzyaloshinskii-moriya-interaction

display_name: dzyaloshinskii-moriya-interaction

inelastic-neutron-scattering

display_name: inelastic-neutron-scattering

marvel

display_name: marvel

spin-waves

display_name: spin-waves

topological-magnon

display_name: topological-magnon


en:
Interface stress is a fundamental descriptor for interphase boundaries and is defined in strict relation to the interface energy. In nanomultilayered coatings with their intrinsically high interface density, the functional properties are generally dictated by the interface structure, which in turn is governed by the delicate interaction of residual interface and volume stresses in the coating system. In the present work, experimental estimations of the interface stress in Cu/W NMLs (with a variable residual stress state from tensile to compressive) were compared with corresponding theoretical values as calculated using DFT (adopting an incoherent bcc W{110}/fcc Cu{111} interface with variable in-plane strain). The Cu/W interface stress was experimentally tuned monotonically from positive to negative values by changing the residual stress in the W nanolayers by increasing the Ar pressure during the W deposition steps. Qualitative agreement between experiment and simulation was achieved, both confirming a decrease of the interface stress from the compressive to the tensile regime. The DFT simulations showed that Cu atoms in the vicinity of the strained Cu/W interfaces are displaced along the in-plane and out-of-plane directions in response to the acting
interface stress. Using a preliminary developed neural network potential for the immiscible Cu-W system, specific in-plane crystallographic orientation relationships for the Cu/W interfaces were also tested, which improved quantitative agreement between experiment and theory. Assumptions and limitations in experiments and theory for deriving the interface stress are critically discussed.

en: Experimental and ab initio derivation of interface stress in nanomultilayered coatings: application to immiscible Cu/W system with variable in-plane stress

ab-initio

display_name: ab-initio

copper-tungsten

display_name: copper-tungsten

interface-stress

display_name: interface-stress

neural-network-potential

display_name: neural-network-potential

xrd

display_name: xrd


en:
Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven simulations with a linear-scaling cost, and can also be used to deduce useful structure--property relations as they associate simple atomic motifs with complicated macroscopic properties. However, even though there exist practical justifications for these decompositions, only the global quantity is rigorously defined, and thus it is unclear to what extent the atomistic terms predicted by the model can be trusted.  Here, we introduce a quantitative metric, which we call the local prediction rigidity (LPR), to assess how robust the locally decomposed predictions of ML models are. We investigate the dependence of LPR on the details of model training, e.g. composition of the dataset, for several different problems ranging from simple toy models to real chemical systems. We present strategies to systematically enhance the LPR, which can be used to improve the robustness, interpretability, and transferability of the resulting atomistic ML models.
This repository contains datasets and Jupyter notebooks employed to corroborate the results of the above study.

en: Robustness of local predictions in atomistic machine learning models

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).

epsrc

display_name: epsrc

erc

display_name: erc


en: This data presents the characteristics obtained using extracted parameters of multi- junction solar cells using a single diode triple-junction model. The proposed method combines analytical and numerical techniques to solve a nonlinear equation and determine the parameters, including the ideality factor, series resistance, shunt resistance, photocurrent, and saturation current, using manufacturer data and mathematical equations. The results show that the suggested approach achieves satisfactory performance and provide an accurate representation of the multi-junction circuit's parameters. Overall, this study provides a valuable contribution to the field of solar cell parameter extraction and offers insights for further research in this area.

en: Characteristics data for a new parameters extraction method of InGaP/GaAs/Ge multi-junction solar cell

materialscloud

en: Materials Cloud

i-v-characteristics

display_name: i-v-characteristics

multi-junction

display_name: multi-junction

pv-parameter-extraction

display_name: pv-parameter-extraction

solar-energy

display_name: solar-energy


en: We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the probability distribution of the off-diagonal elements of a Wishart matrix, we develop a consistent and unbiased estimator for the Seebeck coefficient, whose statistical uncertainty can be arbitrarily reduced in the long-time limit. We assess the efficacy of our method by benchmarking it against extensive equilibrium molecular dynamics simulations conducted on molten CsF using empirical force fields. We then employ this procedure to calculate the Seebeck coefficient of molten NaCl, KCl and LiCl using neural network force fields trained on ab initio data over a range of pressure-temperature conditions.

en: Seebeck coefficient of ionic conductors from Bayesian regression analysis

envi

en: Environment

bayesian

display_name: bayesian

ionic-conductors

display_name: ionic-conductors

seebeck

display_name: seebeck


en: The effect of hydrogen doping on the crystal structure and the electronic state in SmNiO₃ is investigated by means of density-functional theory with a combinatorial structure-generation approach. While 100% of hydrogen doping per Ni atom has been supposed to be responsible for the experimentally observed insulating phase, we found that 50% of hydrogen doping results in an outstandingly stable atomic structure showing the insulating property. The stable crystal structure shows the peculiar layered pattern of charge disproportionation of Ni²⁺ and Ni³⁺ valences together with the strong Jahn-Teller distortion that causes the eg orbital state splitting and opens the band gap.

en: Hydrogen-induced insulating state accompanied by inter-layer charge ordering in SmNiO₃

dft

display_name: dft

hydrogen-doping

display_name: hydrogen-doping