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

en: A NN-Potential for phase transformations in Ge

materialscloud

en: Materials Cloud

deepmd

display_name: deepmd

germanium

display_name: germanium

neural-network

display_name: neural-network

phase-transitions

display_name: phase-transitions


$oid: OBSOLETE

en: We calculate the screened electron-electron interaction for the charge-disproportionated insulator CaFeO₃ using the constrained random-phase approximation (cRPA). While in many correlated materials, the formation of a Mott-insulating state is driven by a large local Coulomb repulsion, represented by the Hubbard U, several cases have been identified more recently where U is strongly screened and instead the Hund's interaction J dominates the physics. Our results confirm a strong screening of the local Coulomb repulsion U in CaFeO₃ whereas J is much less screened and can thus stabilize a charge-disproportionated insulating state. This is consistent with the case of the rare-earth nickelates where similar behavior has been demonstrated. In addition, we validate some common assumptions used for parametrizing the local electron-electron interaction in first-principles calculations based on density-functional theory (DFT), assess the dependence of the interaction on the choice of correlated orbitals, and discuss the use of the calculated interaction parameters in DFT+U calculations of CaFeO₃. Our work also highlights certain limitations for the direct use of cRPA results in DFT-based first-principles calculations, in particular for systems with strong entanglement between the correlated and uncorrelated bands.

en: Calculation of screened Coulomb interaction parameters for the charge-disproportionated insulator CaFeO₃

materialscloud

approval_status: approved

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

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

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

charge-disproportionation

display_name: charge-disproportionation

constrained-random-phase-approximation

display_name: constrained-random-phase-approximation

metal-insulator-transition

display_name: metal-insulator-transition

perovskite

display_name: perovskite


en: This database contains the neural network potential (NNP) model and training data for aqueous ZnCl₂ solutions from 1 m to 30 m. The NNP model can be used to compute total energies and atomic forces, with one of its major applications being large-scale molecular dynamics (MD) simulations. The model was trained using DeePMD-kit v2.2.1, with training data generated through an active learning approach implemented in DP-GEN. The energies and forces in the training set were obtained from density functional theory (DFT) calculations using the SCAN exchange-correlation functional performed using Quantum ESPRESSO. Further details on the ab initio calculation procedures and model training methodology are available in the associated manuscript (see reference below).

en: Resolving the solvation structure and transport properties of aqueous zinc electrolytes from salt-in-water to water-in-salt using neural network potential

neural-network-potential

display_name: neural-network-potential

water

display_name: water

water-in-salt

display_name: water-in-salt


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

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

ab-initio

display_name: ab-initio

dftu

display_name: dftu

erc

display_name: erc

marvel

display_name: marvel


en: Understanding the phononic origin of the infrared dielectric properties of yttria (Y₂O₃) and other rare-earth sesquioxides (RE₂O₃) is a fundamental task in the search of appropriate RE₂O₃ materials that serve particular infrared optical applications. We herein investigate the infrared dielectric properties of RE₂O₃ (RE = Y, Gd, Ho, Lu) using DFT-based phonon calculations and Lorentz oscillator model. The abundant IR-active optical phonon modes that are available for effective absorption of photons result in high reflectance of RE₂O₃, among which four IR-active modes originated from large distortions of REO₆ octahedra are found to contribute dominantly to the phonon dielectric constants. Particularly, the present calculation method by considering one-phonon absorption process is demonstrated with good reliability in predicting the infrared dielectric parameters of RE₂O₃ at the far-infrared as well as the vicinity of mid-infrared region, and the potential cutoff frequency/wavelength of its applicability is disclosed as characterized by the maximum frequency of IR-active longitudinal phonon modes. The results deepen the understanding on infrared dielectric properties of RE₂O₃, and aid the computational design of materials with appropriate infrared properties.

en: Phononic origin of the infrared dielectric properties of RE₂O₃ (RE = Y, Gd, Ho, Lu) compounds

first-principles

display_name: first-principles

infrared

display_name: infrared

phonon

display_name: phonon

sesquioxides

display_name: sesquioxides


en: Singlet fission (SF) is a two-step process in which a singlet splits into two triplets throughout the so-called correlated triplet-pair (1TT) state. Intramolecular SF (iSF) materials in particular, have attracted growing interest as they can be easily implemented in single junction solar cells and boost their power conversion efficiency. Still, the potential of iSF materials such as polymers and oligomers for photovoltaic applications has been partially hindered by their ability to go beyond the 1TT intermediate and generate free triplets, which mechanism remains poorly understood. In this work, the main aspects governing the 1TT dissociation in donor-acceptor copolymers and the key features that optimize this process are exposed. First, we show that both thermodynamics and kinetics play a crucial role in the intramolecular triplet-pair separation and second, we uncover the inherent flexibility of the donor unit as the fundamental ingredient to optimize them simultaneously. Overall, these results provide a better understanding of the intramolecular 1TT dissociation process and establish a new paradigm for the development of novel iSF active materials.

en: Optimizing the thermodynamics and kinetics of the triplet-pair dissociation in donor-acceptor copolymers for intramolecular singlet fission

donor-acceptor-copolymers

display_name: donor-acceptor-copolymers

electronic-structure

display_name: electronic-structure

epfl

display_name: epfl

marie-curie-fellowship

display_name: marie-curie-fellowship

singlet-fission

display_name: singlet-fission


en:
The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency whilst guaranteeing interpretability and transferability. In this work, we demonstrate the wide utility of prediction rigidities, a family of metrics derived from the loss function, in understanding the robustness of ML model predictions. We show that the prediction rigidities allow the assessment of the model not only at the global level, but also on the local or the component-wise level at which the intermediate (e.g. atomic, body-ordered, or range-separated) predictions are made. We leverage these metrics to understand the learning behavior of different ML models, and to guide efficient dataset construction for model training. We finally implement the formalism for a ML model targeting a coarse-grained system to demonstrate the applicability of the prediction rigidities to an even broader class of atomistic modeling problems.
This record contains all the data used for the analyses conducted in the associated work published in Faraday Discussions.

en: Prediction rigidities for data-driven chemistry

machine-learning

display_name: machine-learning

ml-model-robustness

display_name: ml-model-robustness

prediction-rigidity

display_name: prediction-rigidity

uncertainty-quantification

display_name: uncertainty-quantification


en: In the upcoming process to overcome the limitations of the standard von Neumann architecture, synaptic electronics is gaining a primary role for the development of in-memory computing. In this field, Ge-based compounds have been proposed as switching materials for nonvolatile memory devices and for selectors. By employing the classical molecular dynamics, we study the structural features of both the liquid states at 1500 K and the amorphous phase at 300 K of Ge-rich and Se-rich chalcogenides binary Ge_x Se_1–x systems in the range 0.4 ≤ x ≤ 0.6. The simulations rely on a model of interatomic potentials where ions interact through steric repulsion, as well as Coulomb and charge–dipole interactions given by the large electronic polarizability of Se ions. Our results indicate the formation of temperature-dependent hierarchical structures with short-range local orders and medium-range structures, which vary with the Ge content. Our work demonstrates that nanosecond-long simulations, not accessible via ab initio techniques, are required to obtain a realistic amorphous phase from the melt. Our classical molecular dynamics simulations are able to describe the profound structural differences between the melt and the glassy structures of GeSe chalcogenides. These results open to the understanding of the interplay between chemical composition, atomic structure, and electrical properties in switching materials.

en: Hierarchical short- and medium-range order structures in amorphous Ge_x Se_1–x for selectors applications

amorphous

display_name: amorphous

chalcogenides

display_name: chalcogenides

intersect

display_name: intersect

molecular-dynamics

display_name: molecular-dynamics


en: The α/β interface is central to the microstructure and mechanical properties of titanium alloys. We investigate the structure, thermodynamics and migration of the coherent and semicoherent Ti α/β interfaces as a function of temperature and misfit strain via molecular dynamics (MD) simulations, thermodynamic integration and an accurate, DFT-trained Deep Potential. The structure of an equilibrium semicoherent interface consists of an array of steps, an array of misfit dislocations, and coherent terraces. Analysis determines the dislocation and step (disconnection) array structure and habit plane. The MD simulations show the detailed interface morphology dictated by intersecting disconnection arrays. The steps are shown to facilitate α/β interface migration, while the misfit dislocations lead to interface drag; the drag mechanism is different depending on the direction of interface migration. These results are used to predict the nature of α phase nucleation on cooling through the α-β phase transition.

en: Coherent and semicoherent interfaces in titanium: structure, thermodynamics, migration

materialscloud

en: Materials Cloud

free-energy

display_name: free-energy

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

titanium-interfaces

display_name: titanium-interfaces


en: Maximally localized Wannier functions (MLWFs) are widely used to construct first-principles tight-binding models that accurately reproduce the electronic structure of materials. Recently, robust and automated approaches to generate these MLWFs have emerged, leading to natural sets of atomic-like orbitals that describe both the occupied states and the lowest lying unoccupied ones (when the latter can be meaningfully described by bonding/anti-bonding combinations of localized orbitals). For many applications, it is important to instead have MLWFs that describe only certain target manifolds separated in energy between them — the occupied states, the empty states, or certain groups of bands. Here, we start from the full set of MLWFs describing simultaneously all the target manifolds, and then mix them using a combination of parallel transport and maximal localization to construct orthogonal sets of MLWFs that fully and only span the desired target submanifolds. The algorithm is simple and robust, and it is applied to some paradigmatic but non-trivial cases (the valence and conduction bands of silicon, the top valence band of MoS₂, the 3d and t2g/eg bands of SrVO₃ and to a mid-throughput study of 77 insulators.

en: Automated mixing of maximally localized Wannier functions into target manifolds

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).

manifold-optimization

display_name: manifold-optimization

marvel

display_name: marvel

snsf

display_name: snsf

wannier-functions

display_name: wannier-functions

wannier-interpolation

display_name: wannier-interpolation

wannierization

display_name: wannierization


en: This dataset includes all the atomic coordinates of the optimized computational models from electronic structure calculations reported in the manuscript. In this work, we highlight that single-site noble metal dopants on the Cu surface can influence C–O bond dissociation in a key selectivity-determining intermediate (e.g., oxygen-bound *CH₂CHO), which in turn direct the post-C–C coupling pathways to ethylene versus ethanol. Combining theoretical and experimental analyses, we demonstrate that the favorability of C–O bond scission is controlled by the oxygen affinity of the metal dopant on the Cu catalyst. We find that the selectivity ratio of ethylene-to-ethanol displays a volcano relationship with respect to the oxygen binding strength on the doped surfaces.

en: Directing CO₂ electroreduction pathways for selective C₂ product formation using single-site doped Cu catalysts

envi

en: Environment

selectivity

display_name: selectivity

single-site-doped-catalysts

display_name: single-site-doped-catalysts


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

oxynitride

display_name: oxynitride


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:
The development of novel double-hybrid density functionals offers new levels of accuracy, and is leading to fresh insights into the fundamental properties of matter. Hartree–Fock exact exchange and correlated wave function methods such as MP2 and direct RPA are usually required to build such functionals. Their high computational cost is a concern, and their application to large and periodic systems is therefore limited. In this work, low-scaling methods for HFX, SOS-MP2 and direct RPA energy gradients are developed and implemented in the CP2K software package. The use of the resolution-of-the-identity approximation with a short range metric and atom-centered basis functions lead to sparsity, allowing for sparse tensor contractions to take place. These operations are efficiently performed with the newly developed DBT and DBM libraries, which scale to hundreds of GPU nodes. The resulting methods, RI-HFX, SOS-MP2 and dRPA, were benchmarked on large supercomputers. They exhibit favorable sub-cubic scaling with system size, good strong scaling performance, and GPU acceleration up to a factor of 3. These developments will allow for double-hybrid level calculations of large and periodic condensed phase systems to take place on a more regular basis.

This record contains all CP2K input and output files used for the paper.

en: Sparse tensor based nuclear gradients for periodic Hartree–Fock and low-scaling correlated wave function methods in the CP2K software package: A massively parallel and GPU accelerated implementation.

correlated-wave-function

display_name: correlated-wave-function

gpu

display_name: gpu

hartree-fock

display_name: hartree-fock

hpc

display_name: hpc

low-scaling

display_name: low-scaling

nuclear-gradients

display_name: nuclear-gradients

rpa

display_name: rpa

sparse-tensors

display_name: sparse-tensors


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:
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

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

materialscloud

en: Materials Cloud

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: 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

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).

disordered-material

display_name: disordered-material

evolution

display_name: evolution

network

display_name: network

scattering

display_name: scattering


en:
Seven imidazolium-based ionic liquids (ILs) with different anions and cations were investigated as catholytes for the CO₂ electrocatalytic reduction to CO over silver. A significant activity and stability, but different selectivities for CO₂ reduction or the side H₂ evolution were observed. Density functional theory results show that the role of the IL anions is to tune the ratio between the CO₂ captured and electrochemically converted. Acetate anions (being strong Lewis bases) are more prone to CO₂ capture, enhancing H₂ evolution, while fluorinated anions (being weaker Lewis bases) favour the CO₂ electroreduction. Differently from the hydrolytically unstable 1-butyl-3-methylimidazolium tetrafluoroborate, 1-Butyl-3-Methylimidazolium Triflate was the most promising IL showing the highest Faradaic efficiency to CO (>95%), up to 8h of stable operation at high current rates (-20mA & -60 mA).
These results open the way for a strategic selection of the most suitable IL for the CO₂ electroreduction and its future process scale-up.

en: Understanding imidazolium-based ionic liquids role in the CO₂ electroreduction reaction: experimental and theoretical study

envi

en: Environment

dft

display_name: dft

experimental

display_name: experimental

free-energy

display_name: free-energy

quantum-espresso

display_name: quantum-espresso


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

spin-waves

display_name: spin-waves

topological-magnon

display_name: topological-magnon


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

bayesian

display_name: bayesian

ionic-conductors

display_name: ionic-conductors

seebeck

display_name: seebeck


en: Photocatalytic conversion of CO₂ into fuel feed stocks is a promising method for sustainable fuel production. A highly attractive class of materials, inorganic-core@metal–organic-framework heterogeneous catalysts, boasts a significant increase in catalytic performance when compared to the individual materials. However, due to the ever-expanding chemical space of inorganic-core catalysts and metal–organic frameworks (MOFs), identification of these optimal heterojunctions is difficult without appropriate computational screening. In this work, a novel high-throughput screening method of nano-hybrid photocatalysts is presented by screening 65'784 inorganic-core materials and 20'375 MOF-shells for their ability to reduce CO₂ based on their synthesizability, aqueous stability, visible light absorption, and electronic structure; the passing materials were then paired based on their electronic structure to create novel heterojunctions. The results showed 58 suitable inorganic-core materials and 204 suitable MOFs ranging from never-beforesynthesized catalysts to catalysts that have been overlooked for their photocatalytic ability. These materials lay a new foundation of photocatalysts that have passed theoretical requirements and can significantly increase the rate of catalyst discovery.

en: High-throughput screening of nano-hybrid metal–organic-frameworks for photocatalytic CO₂ reduction

coreshell

display_name: coreshell

metal-organic-frameworks

display_name: metal-organic-frameworks

photocatalyst

display_name: photocatalyst


neural-network-potential-for-zr-h

en: The introduction of Hydrogen (H) into Zirconium (Zr) influences many mechanical properties, especially due to low H solubility and easy formation of Zirconium hydride phases. Understanding the various effects of H requires studies with atomistic resolution but at scales that incorporate defects such as cracks, interfaces, and dislocations. Such studies thus demand accurate interatomic potentials. Here, a neural network potential (NNP) for the Zr-H system is developed within the  Behler-Parrinello framework. The Zr-H NNP retains the accuracy of a recent NNP for hcp Zr and exhibits excellent agreement with first-principles density functional theory (DFT) for (i) H interstitials and their diffusion in hcp Zr, (ii) formation energies, elastic constants, and surface energies of relevant Zr hydrides, and (iii) energetics of a common Zr/Zr-H interface. The Zr-H NNP shows physical behavior for many different crack orientations in the most-stable ε-hydride and structures and reasonable relative energetics for the ⟨a⟩ screw dislocation in pure Zr. This Zr-H NNP should thus be very powerful for future study of many phenomena driving H degradation in Zr that require atomistic detail at scales far above those accessible by first-principles

en: Neural network potential for Zr-H

title_for_slug: neural-network-potential-for-zr-h

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

neural-network-potentials

display_name: neural-network-potentials

zirconium-hydrides

display_name: zirconium-hydrides


en: Thermal annealing experiments evidence opposite effect on the degradation kinetics of Cu/W nano-multilayers from compressive to tensile in-plane strain.  Besides higher activation energy, nano-multilayers with tensile strains degrade to nanocomposites faster than those with compressive strains. By assuming a vacancy-driven diffusion mechanism of degradation, we applied ab initio calculations to quantify different contributions to the corresponding diffusion coefficients in relation to in-plane strain. The average vacancy formation energy increases as the strain changes from compressive to tensile, which explains the higher experimental activation energy. The bulk in-plane and out-of-plane vacancy migration energies and corresponding diffusion prefactors highlight that enhanced transformation rate under tension can be explained by the segregation of non-equilibrium W vacancies to Cu/W interfaces. Our thermodynamic evaluation of grain boundary wetting and grooving by hybrid molecular dynamics/Monte Carlo method further supports this point, as both stress states enhance W grain separation to the same level.

en: Explaining the effect of in-plane strain on thermal degradation kinetics of Cu/W nano-multilayers

ab-initio-calculations

display_name: ab-initio-calculations

cuw

display_name: cuw

degradation-kinetics

display_name: degradation-kinetics

in-plane-strain

display_name: in-plane-strain

nano-multilayers

display_name: nano-multilayers

vacancy-driven-diffusion

display_name: vacancy-driven-diffusion


en: Topological materials have been a main focus of studies in the past decade due to their protected properties that can be exploited for the fabrication of new devices. Among them, Weyl semimetals are a class of topological semimetals with non-trivial linear band crossing close to the Fermi level. The existence of such crossings requires the breaking of either time-reversal or inversion symmetry and is responsible for the exotic physical properties. In this work we identify the full-Heusler compound InMnTi₂, as a promising, easy to synthesize, T- and I-breaking Weyl semimetal. This material exhibits several features that are comparatively more intriguing with respect to other known Weyl semimetals: the distance between two neighboring nodes is large enough to observe a wide range of linear dispersions in the bands, and only one kind of such node's pairs is present in the Brillouin zone. We also show the presence of Fermi arcs stable across a wide range of chemical potentials. Finally, the lack of contributions from trivial points to the low-energy properties makes the materials a promising candidate for practical devices.

en: Prediction of a novel type-I antiferromagnetic Weyl semimetal

materialscloud

en: Materials Cloud

surface-properties

display_name: surface-properties

topological-materials

display_name: topological-materials

weyl-semimetal

display_name: weyl-semimetal


en: The accurate description of redox reactions remains a challenge for first-principles calculations, but it has been shown that extended Hubbard functionals (DFT+U+V) can provide a reliable approach, mitigating self-interaction errors, in materials with strongly localized d or f electrons. Here, we first show that DFT+U+V molecular dynamics is capable to follow the adiabatic evolution of oxidation states over time, using representative Li-ion cathode materials.  In turn, this allows to develop redox-aware machine-learned potentials. We show that considering atoms with different oxidation states (as accurately predicted by DFT+U+V) as distinct species in the training leads to potentials that are able to identify the correct ground state and pattern of oxidation states for redox elements present. This is achieved, e.g., trough a combinatorial search for the lowest energy configuration. This brings the advantages of machine-learned potential to key technological applications (e.g., rechargeable batteries), which require an accurate description of the evolution of redox states.

en: Teaching oxidation states to neural networks

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).

dftuv

display_name: dftuv

machine-learning

display_name: machine-learning

max

display_name: max

oxidation-states

display_name: oxidation-states


en: Homogeneous nucleation processes are important for understanding solidification and the resulting microstructure of materials. Simulating this process requires accurately describing the interactions between atoms,  hich is further complicated by chemical order through cross-species interactions. The large scales needed to observe rare nucleation events are far beyond the capabilities of ab initio simulations. Machine-learning is used for overcoming these limitations in terms of both accuracy and speed, by building a high-dimensional neural network potential for binary Al-Ni alloys, which serve as a model system relevant to many industrial applications. The potential is validated against experimental diffusion, viscosity, and scattering data, and is applied to large-scale molecular dynamics simulations of homogeneous nucleation at equiatomic composition, as well as for pure Ni. Pure Ni nucleates in a single-step into an fcc crystal phase, in contrast to previous results obtained with a classical empirical potential. This highlights the sensitivity of nucleation pathways to the underlying atomic interactions. Our findings suggest that the nucleation pathway for AlNi proceeds in a single step toward a B2 structure, which is discussed in relation to the pure elements counterparts.

en: Homogeneous nucleation of undercooled Al-Ni melts via a machine-learned interaction potential

envi

en: Environment

aluminium

display_name: aluminium

interaction-potential

display_name: interaction-potential

nickel

display_name: nickel


en: The dataset provides records of tensile properties of nuclear structural materials. The focus is on studying the influence of specimen dimensions and geometry on mechanical properties such as yield strength, ultimate tensile strength, uniform elongation, and total elongation. The dataset was created through an extensive literature review of scientific articles and databases. The search inclusion criteria targeted peer-reviewed studies on tensile testing of sub-sized specimens, providing quantitative data on tensile properties relative to specimen size. The extracted data points from the literature review were organized into a tabular format database containing 1,070 tensile testing records with 54 parameters, including material type and composition, manufacturing information, irradiation conditions, specimen size and dimensions, and tensile properties. Materials science experts conducted systematic checks to validate the collected data, ensuring accuracy in the material type, manufacturing processes and treatment methods, and testing conditions, as well as verifying the chemical composition and other pertinent information. Our team performed statistical analyses to identify and address data outliers, ensuring the reliability of the dataset.

en: Dataset of tensile properties for sub-sized specimens of nuclear structural materials

nuclear-structural-materials

display_name: nuclear-structural-materials

specimen-size-effect

display_name: specimen-size-effect

sub-sized-specimens

display_name: sub-sized-specimens

tensile-properties

display_name: tensile-properties


en:
The intermetallic quasi-one-dimensional binary superconductor V2Ga5 was recently found to exhibit a topologically nontrivial normal state, making it a natural candidate for a topological superconductor.
By combining dc-magnetization, nuclear magnetic resonance, and muon-spin rotation (µSR) measurements on high-quality V2Ga5 single crystals, we investigate the electronic properties of its normal- and superconducting ground states. NMR measurements in the normal state indicate a strong anisotropy in both the line shifts and the relaxation rates. Such anisotropy persists also in the superconducting state, as shown by the magnetization and µSR-spectroscopy results. In the latter case, data collected at different temperatures, pressures, and directions of the magnetic field (with respect to the crystalline axes) evidence a fully-gapped, strongly anisotropic superconductivity. At the same time, hydrostatic pressure is shown to only lower the Tc value, but not to change the superfluid density nor its temperature dependence. Lastly, we discuss the search for topological signatures in the normal state of V2Ga5, as well as a peak splitting in the FFT of the µSR spectrum, possibly related to an unconventional vortex lattice. Our results suggest that V2Ga5 is a novel system, whose anisotropy plays a key role in determining its unusual electronic properties.

en: Anisotropic superconductivity in the quasi-one-dimensional superconductor

nuclear-magnetic-resonance

display_name: nuclear-magnetic-resonance

superconductivity

display_name: superconductivity


en: Solidification control is crucial in manufacturing technologies, as it determines the microstructureand,  consequently,  the performance of the final product.  Investigating the mechanisms occurring during the early stages of nucleation remains experimentally challenging as it initiates on nanometer length and sub-picoseconds time scales.  Large scale molecular dynamics simulations using machinelearning interatomic potential with quantum accuracy appears the dedicated approach to complex,atomic level, multidimensional mechanisms with local symmetry breaking.  A potential trained ona high-dimensional neural network on density functional theory-based ab initio molecular dynamics(AIMD) trajectories for liquid and undercooled states for Al-Si binary alloys enables us to study the  nucleation  mechanisms  occurring  at  the  early  stages  from  the  liquid  phase  near  the  eutectic composition.  Our results indicate that nucleation starts with Al at hypoeutectic compositions and with Si at hypereutectic compositions.  Whereas Al nuclei grow in a globular shape, Si ones grow with polygonal faceting, whose underlying mechanisms are further discussed.

en: Crystal nucleation in eutectic Al-Si alloys by machine-learned molecular dynamics

aluminum-silicon

display_name: aluminum-silicon

hdnnp

display_name: hdnnp

machine-learning-interatomic-potential

display_name: machine-learning-interatomic-potential


en: We report a first-principles study of the electronic and optical properties of recently identified one-dimensional semiconducting materials exfoliable from van der Waals-bonded bulk crystals. Specifically, we investigate four chalcogenide-based atomic chains, the covalently bonded S3 and Te3 chains, and polar-bonded As2S3 and Bi2Te3 chains, using a fully first-principles approach that combines density functional theory (DFT), density functional perturbation theory (DFPT), and many-body perturbation theory within the GW approximation and Bethe−Salpeter equation (BSE). Our vibrational analysis shows that the isolated, freestanding wires remain dynamically stable, with the zone-center optical phonon modes leading to infrared activity. The main finding of this study is the presence of very strong exciton binding energies (1−3 eV), which make these exfoliable 1D materials suitable platforms for room-temperature excitonic applications. Interestingly, the exciton character remains Wannier−Mott-like, as indicated by average electron−hole separations greater than the lattice constant. Notably, the optical gaps of these materials span a wide range - from infrared (0.8 eV, Bi2Te3), through the visible spectrum (yellow: 2.17 eV, Te3; blue: 2.71 eV, As2S3), up to ultraviolet (4.07 eV, S3) - highlighting their versatility for broadband optoelectronic applications. Our results offer a detailed, many-body perspective on the optoelectronic behavior of these low-dimensional materials and underscore their potential for applications in nanoscale optoelectronic devices.

en: Quasiparticle effects and strong excitonic features in exfoliable 1D semiconducting materials

dft

display_name: dft

exciton

display_name: exciton

gw

display_name: gw

one-dimensional-materials

display_name: one-dimensional-materials

phonons

display_name: phonons

van-der-waals-wires

display_name: van-der-waals-wires


en: This dataset contains experimental and theoretical results from the study of heat transport in suspended single-crystal diamond cantilevers with sub-micrometer widths. "Imaging heat transport in suspended diamond nanostructures with integrated spindefect thermometers", Physical Review Letters, 2026Temperature profiles are measured using embedded nitrogen-vacancy (NV) spin defects acting as in-situ thermometers, enabling spatially resolved thermal mapping under laser heating. The data include measured temperature gradients, extracted effective thermal conductivities as a function of cantilever width, and first-principles simulations based on the linearized phonon Boltzmann transport equation and viscous heat equations. The combined experimental and computational results demonstrate non-diffusive, sub-ballistic heat transport arising from the interplay of intrinsic phonon scattering and boundary effects.

en: Imaging heat transport in suspended diamond nanostructures with integrated spin defect thermometers

diamond-nanostructures

display_name: diamond-nanostructures

heat-transport

display_name: heat-transport

nanoscale-thermometry

display_name: nanoscale-thermometry

nitrogen-vacancy-centers

display_name: nitrogen-vacancy-centers

non-diffusive-thermal-conduction

display_name: non-diffusive-thermal-conduction

phonon-boltzmann-transport-equation

display_name: phonon-boltzmann-transport-equation

viscous-heat-equations

display_name: viscous-heat-equations


en: Defect engineering in two-dimensional semiconductors has been exploited to tune the optoelectronic properties and introduce new quantum states in the band gap. Chalcogen vacancies in transition metal dichalcogenides in particular have been found to strongly impact charge carrier concentration and mobility in 2D transistors as well as feature subgap emission and single-photon response. In this Letter, we investigate the layer-dependent charge-state lifetime of Se vacancies in WSe2. In one monolayer WSe2,we observe ultrafast charge transfer from the lowest unoccupied orbital of the top Se vacancy to the graphene substrate within 1 ± 0.2 ps measured via the current saturation in scanning tunneling approach curves. For Se vacancies decoupled by transition metal dichalcogenide (TMD) multilayers, we find a subexponential increase of the charge lifetime from 62 ±14 ps in bilayer to a few nanoseconds in four-layer WSe2, alongside a reduction of the defect state binding energy. Additionally, we attribute the continuous suppression and energy shift of the dI=dV in-gap defect state resonances at very close tip-sample distances to a current saturation effect. Our results provide a key measure of the layer-dependent charge transfer rate of chalcogen vacancies in TMDs.

en: Layer-dependent charge-state lifetime of single Se vacancies in WSe2

materialscloud

en: Materials Cloud

defect-engineering

display_name: defect-engineering

scanning-tunneling-microscopy-stm

display_name: scanning-tunneling-microscopy-stm

transition-metal-dichalcogenide-tmd

display_name: transition-metal-dichalcogenide-tmd


en: Two-dimensional topological insulators are characterized by an insulating bulk and conductive edge states protected by the nontrivial topology of the bulk electronic structure. They remain robust against moderate disorder until Anderson localization occurs and destroys the topological phase. Interestingly, disorder can also induce a topological phase—known as a topological Anderson insulator—starting from an otherwise pristine trivial phase. While topological invariants are generally regarded as global quantities, we argue that space-resolved topological markers can act as local order parameters, revealing the role of fluctuations and correlations in the local topology under Anderson disorder and vacancies. With this perspective, we perform numerical simulations of disorder-driven topological phase transitions in the Haldane and Kane-Mele models, using supercells with both open and periodic boundary conditions. We find that short-scale fluctuations of topological markers vanish upon coarse-graining, except at the topological phase transition, where their correlation length peaks and large-scale fluctuations remain. Notably, such a topological correlation function is characterized by critical exponents that appear universal across disorder types, yet they can resolve different topological phase transitions.

en: Fluctuations and correlations of local topological order parameters in disordered two-dimensional topological insulators

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).

anderson-disorder

display_name: anderson-disorder

critical-exponents

display_name: critical-exponents

fluctuations-and-correlations

display_name: fluctuations-and-correlations

haldane-model

display_name: haldane-model

kane-mele-model

display_name: kane-mele-model

local-topology

display_name: local-topology

topological-order-parameters

display_name: topological-order-parameters

vacancies

display_name: vacancies


en: Lithium thiophosphate (LPS) has demonstrated promising properties for use as a solid electrolyte for the next-generation of lithium ion batteries. However, the high reactivity of LPS with common contaminants such as atmospheric water hinders commercialization of the cells. We employ a machine learning interatomic potential to gain fundamental, atomistic understanding on the mechanical, chemical and electronic properties of an LPS surface. Our focus lies first on the identification of relevant surface complexions formed by surface reconstructions, which differ greatly in properties from the bulk and define the form of the reactive sites. Then, we reveal the complex reactivity of the LPS surface with water and identify the surface moieties that play a relevant role in the material’s degradation. Thus, we uncover the impact of dynamical changes of the surfaces and their electronic structure on the material’s reactivity.This dataset includes all files necessary to reproduce the data reported in Reconstructions and dynamics of 𝛽 -lithium thiophosphate surfaces.This includes the MLIP model and dataset, generated surface structures with certain Miller indices, Wulff constructions, as well the input and result files of LAMMPS and DFT calculations.

envi

en: Environment

deutsche-forschungsgemeinschaft

display_name: deutsche-forschungsgemeinschaft

lps

display_name: lps

machine-learning

display_name: machine-learning

machine-learning-interatomic-potentials

display_name: machine-learning-interatomic-potentials

marvel

display_name: marvel

molecular-dynamics

display_name: molecular-dynamics

smoothsoap

display_name: smoothsoap

solid-electrolyte

display_name: solid-electrolyte

surface-complexion

display_name: surface-complexion


en: Experiments reveal negative (non-Laplacian) surface stresses in metal oxide nanoparticles, partly associated with humidity during fabrication and annealing. Using a neural network interatomic potential for MgO, we prove that water adsorption induces surface hydroxylation, shifting facets from {100} to {110} to {111} and switching the average surface stress from positive to negative. Predicted lattice strains versus nanoparticle size agree well with experiments, clarifying experimental correlations. The new framework informs broad applications in catalysis, sensors, batteries, and biomedicine.

en: Hydroxylation-driven surface reconstruction at the origin of compressive-to-tensile stress transition in metal oxide nanoparticles

nanoparticles

display_name: nanoparticles

surface-reconstruction

display_name: surface-reconstruction

surface-stress

display_name: surface-stress

xrd

display_name: xrd


en: The electron-phonon Wannier interpolation (EPWI) method is an efficient way to compute the properties of electron-phonon interactions (EPIs) accurately. This study presents a GPU-accelerated implementation of the EPWI method for computing transport properties, followed by a performance analysis. The implementation is tested on common systems such as aluminum andsilicon. The results show complete consistency with those obtained through CPU computations. The proposed algorithm has the capability of computing the conductivity of aluminum in 20 minutes on a single NVIDIA Tesla V100 GPU, adopting a 2003 electron and phonon sampling grid. This speed is 54 times higher than the CPU-based algorithm, running on two nodes of the Intel Xeon Platinum 8260 CPU. Such impressive acceleration is achieved by carefully designing the algorithm to exploit the GPU’s specific features. The idea of this approach can also be applied to the GPU code for other EPI-related properties. This record includes the input/output files of this work. The code snippets of the core GPU implementation can be found in https://github.com/zheliu137/EPWI_BTE_GPU

en: A high-performance GPU implementation of the electron-phonon Wannier interpolation and the related transport properties

boltzmann-transport-equation

display_name: boltzmann-transport-equation

electron-phonon-coupling

display_name: electron-phonon-coupling

gpu-acceleration

display_name: gpu-acceleration


en: Density functional theory calculations have been a pillar of computational materials science in the past two decades, and the recent development of AI and data-driven methods has reiterated their importance. ABACUS (Atomic-orbital Based Ab-initio Computation at USTC) is an open-source density functional theory code that supports both plane-wave and numerical atomic orbital basis sets. It is designed as a versatile platform integrating various electronic structure methods and is particularly well-suited for deep-learning-assisted materials simulations. The accuracy of a density functional theory code is crucial for its wide deployment. In this work, we use the verification protocol established by E. Bosoni et al. [Nat. Rev. Phys. 6, 45-58 (2024)] based on the AiiDA common workflows framework to validate the precision of ABACUS by comparing its results with previously generated all-electron benchmarks. Our results show that, using consistent pseudopotentials and comparable calculation settings, ABACUS yields highly consistent results compared to other established codes such as QE, Abinit, and CASTEP. This entry contains all the data, calculations, and scripts required to reproduce the results.

en: Precision verification results data for ABACUS@PW|PseudoDojo-v0.4

automation

display_name: automation

dft

display_name: dft

equation-of-state

display_name: equation-of-state

pseudopotentials

display_name: pseudopotentials

verification

display_name: verification


en: The direct conversion of methane to methanol (DCMM) under continuous flow and atmospheric pressure offers notable environmental benefits and industrial promise, but remains a long-standing challenge due to the difficulty of activating CH4 while avoiding over-oxidation of methanol. Here, we demonstrate that pure ceria (CeO2), without any metal promoters, enables gas-phase DCMM with up to 80 % selectivity at 300–350 °C, upon optimization of the H2O/O2 ratio. At 550 °C, methanol and formaldehyde are formed at rates of 24 and 36 μmol g-1 h-1, respectively-both dropping below 1 μmol g-1 h-1 in the absence of O2. Ex situ transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and Raman spectroscopy confirm that CeO2 maintains structural integrity and resists carbon deposition during reaction. Combining kinetic studies, steady-state in-situ diffuse reflectance infrared Fourier transform spectroscopy (in-situ DRIFTS), and density functional theory (DFT) reveals that hydroxyl groups (OH), generated from water dissociation, play a multifaceted role: they facilitate C–H bond activation, promote methoxy formation, and enhance methanol desorption. In-situ ambient-pressure X-ray photoelectron spectroscopy (AP-XPS) directly reveals the evolution of surface intermediates and shows that co-feeding O2 and H2O suppresses CH3O and CHx accumulation while boosting methanol yield—indicating a rapid intermediate turnover as key to sustained activity. AP-XPS O 1s spectra further highlight that O2 promotes H2O dissociation, regenerating reactive OH groups and maintaining performance at elevated temperature. These findings offer molecular-level insights into how water and oxygen cooperatively tune reactivity, enabling efficient methane-to-methanol conversion on a metal-free oxide catalyst.

en: Synergy of oxygen and water in ceria-catalyzed direct conversion of methane to methanol under continuous flow

ap-xps

display_name: ap-xps

in-situ

display_name: in-situ

in-situ-drifts

display_name: in-situ-drifts

methane-to-methanol

display_name: methane-to-methanol

reaction-mechanism

display_name: reaction-mechanism


en: We present a comprehensive first-principles study of the magnetoresistance in ZrSiX (X = S, Se, Te) topological nodal-line semimetals. Our study demonstrates that all primary features of the experimentally measured magnetoresistance in these materials are captured by our calculations, including the unusual butterfly-shaped anisotropic magnetoresistance. This anisotropic magnetoresistance can be accurately reproduced using the semiclassical Boltzmann transport theory without introducing any information on the topological nature of bands or the concepts of topological phase transition. Considering the complex structure of the Fermi surface in these topological materials, we develop a theoretical description explaining the features observed in magnetoresistance measurements. Additionally, the atypical Hall resistance can be interpreted by the same semiclassical approach. Our findings establish magnetotransport as a powerful tool for analyzing the geometry of the Fermi surface, complementing angle-resolved photoemission spectroscopy and quantum oscillation measurements. This approach is demonstrated to be particularly useful for determining the role of non-trivial topology in transport properties.

en: Magnetoresistance in ZrSiX (X = S, Se, Te) nodal-line semimetals

boltzmann-theory

display_name: boltzmann-theory

density-functional-calculations

display_name: density-functional-calculations

fermi-surface

display_name: fermi-surface

first-principles-calculations

display_name: first-principles-calculations

hall-effect

display_name: hall-effect

magnetoresistance

display_name: magnetoresistance

node-line-semimetals

display_name: node-line-semimetals

topological-materials

display_name: topological-materials


en: Cobalt-based oxides have been investigated as potential alternatives to Ir/Ru-based oxides for catalyzing the oxygen evolution reaction (OER) in acidic media. Past research, however, is mainly focused on the spinel oxide structure so far. Exploring alternative crystal structures is essential for expanding the material library and developing highly efficient OER catalysts for acidic environments. As a proof of concept, we demonstrate that Co-based perovskite oxides can drive acidic OER effectively. Appling hard/soft X-ray absorption spectroscopy (hXAS/sXAS) characterizations, we show that the La and Ce doped SrCoO3 (denoted as LSC and CSC, respectively) have a bulk-average Co oxidation state close to 3+ and surface-dominant low-spin CoIII species. Electrochemical analysis reveals that they only show one Co redox pair, similar to CoOOH in acidic environments. The recorded Tafel slopes are around ~65 mV dec-1, comparable to the benchmarking Ir/Ru-based catalysts. The combination of the spectroscopic and electrochemical findings presented here highlights the important role of low-spin CoIII species in catalyzing OER in acidic environments and contributes to the rational design of non-noble metal OER catalysts.

en: Cobalt-based perovskite oxides as catalysts for acidic oxygen evolution reaction

acidic-oxygen-evolution-reaction

display_name: acidic-oxygen-evolution-reaction

cobalt-based-catalysts

display_name: cobalt-based-catalysts

cobalt-oxidation-state

display_name: cobalt-oxidation-state

experimental

display_name: experimental

perovskite-oxides

display_name: perovskite-oxides

x-ray-adsorption-spectroscopy

display_name: x-ray-adsorption-spectroscopy


en: The total energy is a fundamental characteristic of solids, molecules, and nanostructures. In most first-principles calculations of the total energy, the nuclear kinetic operator is decoupled from the many-body electronic Hamiltonian and the dynamics of the nuclei is reintroduced afterwards. This two-step procedure introduced by Born and Oppenheimer (BO) is approximate. Energies beyond the electronic and vibrational (or phononic) main contributions might be relevant when small energy differences are important, such as when predicting stable polymorphs or describing magnetic energy landscape. We clarify the different flavors of BO decoupling and give an exact formulation for the total energy in the basis of BO electronic wavefunctions. Then, we list contributions, beyond the main ones, that appear in a perturbative expansion in powers of M0-1/4, where M0 is a typical nuclear mass, up to sixth order. Some of these might be grouped and denoted the electron-phonon contribution to total energy,  Eelph, that first appears at fourth order. The electronic inertial mass contributes at sixth order. We clarify that the sum of the Allen-Heine-Cardona zero-point renormalization of eigenvalues over occupied states is not the electron-phonon contribution to the total energy but a part of the phononic contribution. The computation of the lowest-order Eelph is implemented and shown to be small but non-negligible (3.8 meV per atom) in the case of diamond and its hexagonal polymorph. We also estimate the electronic inertial mass contribution and confirm the size-consistency of all computed terms.

en: In search of the electron-phonon contribution to total energy

materialscloud

en: Materials Cloud

ab-initio

display_name: ab-initio

diamond

display_name: diamond

electron-phonon-coupling

display_name: electron-phonon-coupling

total-energy

display_name: total-energy


en: Photothermal (photonic) sintering crystallizes as-deposited amorphous LiCoO2 (LCO) cathodes for solid-state thin-film batteries using millisecond, surface-localized heating. However, process design often relies on 1D models with phase-averaged, temperature-independent properties, which can mispredict peak temperatures and thermal damage margins. Here we develop a multiscale, data-driven framework that provides phase- and grain-size–resolved thermophysical inputs for stoichiometric LCO. We train an Allegro neural network potential with near-ab initio accuracy, enabling Green–Kubo calculations of thermal conductivity for crystalline and amorphous phases. The low, weakly density-dependent conductivity of amorphous LCO motivates its use as an effective intergranular phase in a thin-interface model that reproduces observed grain-size–dependent thermal transport. Combined with measured wavelength-resolved optical properties in 1D multiphysics simulations, we show amorphous LCO absorbs more strongly and reaches higher peak temperatures than crystalline LCO; thus crystalline, constant-property models systematically overestimate safe operating windows.

en: Critical role of phase-dependent properties in modeling photothermal sintering of LiCoO2 cathodes

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).

heat-transfer

display_name: heat-transfer

molecular-dynamics

display_name: molecular-dynamics

neural-network-potential

display_name: neural-network-potential

photonic-sintering

display_name: photonic-sintering

thermal-conductivity

display_name: thermal-conductivity


en: Mechanical properties of ultrathin coatings can deviate from bulk values due to growth-stage and interface effects. Elastic moduli of bulk amorphous alumina were calculated using a near-ab initio-accurate neural-network interatomic potential to provide a modeling benchmark for hydroxylated alumina coatings deposited by atomic layer deposition at 50 °C. Experimentally, elastic moduli were evaluated by both spherical nanoindentation and laser-induced surface acoustic wave spectroscopy (LiSAWS). For coatings thicker than 7 nm, both techniques indicated a weakly decreasing modulus from ~137 GPa to ~126 GPa (nanoindentation) and ~116 GPa to ~108 GPa (LiSAWS). These values are consistent with the simulated Young’s modulus.

en: Modulus and yield strength determination at ultra-thin atomic layer deposited hydroxylated alumina coatings using spherical nanoindentation and surface acoustic waves

envi

en: Environment

atomic-layer-deposition-ald

display_name: atomic-layer-deposition-ald

foundation-model

display_name: foundation-model

hydroxylated-alumina

display_name: hydroxylated-alumina

spherical-nanoindentation

display_name: spherical-nanoindentation

ultrathin-coatings

display_name: ultrathin-coatings

yield-strength

display_name: yield-strength

youngs-modulus

display_name: youngs-modulus


en: We study the electronic structure and dynamical correlations in antiferromagnetic BiFeO3, a prototypical room-temperature multiferroic, using a variety of static and dynamical first-principles methods. Conventional static Hubbard corrections (DFT+U, DFT+U+V) incorrectly predict a deep-valence Fe 3d peak (around -7 eV) in antiferromagnetic BiFeO3, in contradiction with hard-X-ray photoemission. We resolve this failure by using a recent generalization of DFT+U to include a frequency-dependent screening – DFT+U(ω) – or using a dynamical Hubbard functional (dynH). The screened Coulomb interaction U(ω), computed with spin-polarized RPA and projected onto maximally localized Fe 3d Wannier orbitals, is expressed as a sum-over-poles, yielding a self-energy that augments the Kohn–Sham Hamiltonian. This DFT+U(ω) approach predicts a fundamental band gap of 1.53 eV, consistent with experiments, and completely eliminates the unphysical deep-valence peak. The resulting simulated HAXPES spectrum reproduces the experimental lineshape with an accuracy comparable to state-of-the-art approaches. Our work highlights the critical nature of dynamical screening in complex oxides and of DFT+U(ω) as a predictive and computationally efficient approach to address the electronic structure of correlated materials.

en: Electronic structure and dynamical correlations in antiferromagnetic BiFeO3

density-functional-theory

display_name: density-functional-theory

dynamical-correlations

display_name: dynamical-correlations

electronic-structure

display_name: electronic-structure


en:
This archive provides the AiiDA workflows, metadata, and computational datasets associated with the study of vibrational frequencies and Stark tuning rates at electrochemical interfaces within the framework of grand-canonical density functional theory (GC-DFT) combined with continuum electrochemical models.

In this work, the electrochemical potential is treated as a degree of freedom by adjusting the number of electrons in the Kohn-Sham equations, enabling simulations at fixed potential. Based on the relationship between free energy and grand potential, physical properties such as atomic forces, vibrational frequencies, and Stark tuning rates are consistently evaluated. While atomic forces are found to be equivalent to Hellmann-Feynman forces in the canonical ensemble, vibrational frequencies and Stark tuning rates exhibit distinct behavior under grand-canonical conditions.

The archive contains input parameters, workflows, and computed results obtained using finite-displacement methods for the evaluation of vibrational properties and Stark tuning rates. These data enable the reproduction of the results reported in the associated publication and provide a reference for future studies of electrochemical interfaces under constant potential conditions.

en: Vibrational frequencies and stark tuning rate with continuum electro-chemical models and grand canonical density functional theory

grand-canonical-dft

display_name: grand-canonical-dft

stark-tuning-rate

display_name: stark-tuning-rate

vibrational-frequencies

display_name: vibrational-frequencies


en: This dataset presents the results of a comprehensive crystal structure search for ternary compounds in the Lu–N–H system. Thousands of candidate structures were evaluated, and their thermodynamic stability was assessed through the construction of the convex hull of formation enthalpies. To ensure reliable energetic ordering, all calculations were performed using the highly accurate r2SCAN density functional in an all-electron framework, avoiding possible pseudopotential-related inaccuracies.  A previously unreported lutetium hydride phase (HLu₂) is identified on the convex hull, indicating thermodynamic stability. However, subsequent electron–phonon coupling analysis shows that this structure is not a promising candidate for near-ambient superconductivity. Apart from this additional phase, the results are in close agreement with earlier structure prediction studies, demonstrating strong reproducibility across independent computational approaches. This consistency highlights the maturity and reliability of modern crystal structure prediction methods. Overall, the dataset provides further evidence that no thermodynamically stable structure within the Lu–N–H system is likely to exhibit conventional high-temperature superconductivity under near-ambient conditions, as would be required to explain previously reported experimental claims.

en: Ternary phase diagram of nitrogen doped lutetium hydrides

dft

display_name: dft

minimahopping

display_name: minimahopping

superconductor

display_name: superconductor


en: Strain soliton networks strongly influence the structural and electronic properties of heterodeformed bilayer systems, yet their design remains challenging due to the high dimensionality of heterodeformation space and the absence of a direct map between deformation and network geometry. In this work, we introduce a geometric framework that establishes a one-to-one mapping between heterodeformations and the geometry of the strain soliton network expressed as line vector-Burgers vector pairs. The admissible networks are constrained by topology dictated by the generalized stacking fault energy landscape. We show that the moiré Bravais lattice, corresponding to a uniform heterodeformation, alone is insufficient to characterize the interface: distinct heterodeformations can share identical moiré Bravais lattices while producing different soliton networks, reflecting an inherent many-to-one mapping when only translational symmetry is considered. In contrast, the soliton network encodes the full multilattice geometry of the interface, including topology and connectivity, which are not captured by the moiré Bravais lattice alone. The proposed framework enables the direct construction of heterodeformations from target networks, providing a systematic route for inverse design of moiré interfaces beyond conventional twist-based approaches.

en: Inverse design of heterodeformations for strain soliton networks in bilayer 2D materials

heterodeformations

display_name: heterodeformations

inverse-design

display_name: inverse-design

strain-soliton-networks

display_name: strain-soliton-networks


en: We present a combined density-functional theory and dynamical mean-field theory (DFT+DMFT) study of the full structural phase space of rutile-based vanadium dioxide (VO2), including also the less studied M2 and T phases, using an unconventional bond-centered orbital basis. The use of bond-centered orbitals allows us to treat all main phases of VO2, and the structural transitions between them, using one consistent approach with moderate computational cost and without pre-pattering of the structure into dimerized and undimerized V--V pairs. We obtain two distinct insulating states on the two different types of vanadium chains in the M2 phase, a singlet-insulator on the dimerized chains and a Mott-insulator on the zigzag-distorted chains, which, however, are strongly coupled in the M2 phase and thus the metal-insulator transition always occurs concomitantly for both types of sites. We also demonstrate that the M2 phase corresponds to a local energy minimum in the structural phase space of VO2, the stability of which, apart from the internal structural distortion, depends crucially on the unit cell strain relative to the undistorted rutile phase. Our calculations further indicate that the symmetry-distinct triclinic T phase corresponds electronically to either an M1 or an M2-type insulator with an abrupt transition as a function of distortion. Finally, we disentangle the effect of the dimerization and zigzag distortions by constructing hypothetical structures that contain only one site type, finding that the zigzag distortion strongly favors emergence of the Mott-insulating state, both as function of distortion and on-site interaction.

en: Towards a unified first-principles-based description of VO2 using DFT+DMFT with bond-centered orbitals

dftdmft

display_name: dftdmft

mit

display_name: mit


en:
We establish a set of exact sum rules that relate the interatomic force constants to the frequency-dependent electromagnetic susceptibility of a solid or molecule, thereby generalizing the long-established principles of rototranslational symmetry to the nonadiabatic regime. Crucially, we show that in practical numerical implementations these sum rules are violated, unless special precautions are taken in the treatment of the atomic pseudopotentials. We solve these issues once and for all by correctly adapting the pseudopotential to the motion of the corresponding nucleus, with a velocity dependence of the nonlocal operator. This prescription restores the correct Galilean covariance of the Schr"odinger equation, and the expected identity between mechanical rototranslations and electromagnetic perturbations. These results conclusively fix a number of worrisome inconsistencies that were pointed out over the years in the context of linear-response theory restoring, e.g., the validity of the Larmor theorem, and the equivalence between the inertial and electrical definitions of the Drude weight in metals.

This dataset contains the input and output files used to produce the results presented in the manuscript.

en: Rototranslational sum rules for nuclear dynamics via traveling pseudopotentials

materialscloud

en: Materials Cloud

nonadiabatic-regime

display_name: nonadiabatic-regime

pseudopotentials

display_name: pseudopotentials


en: Cs2KInI6 is a promising lead-free halide double perovskite with a calculated direct band gap of 1.94 eV, ideal for solar cell applications. Our first-principles calculations reveal that its cubic phase (Fm3̅m) is dynamically unstable. Using an accelerated machine learning approach, we identify 42 dynamically stable structures and further validate these findings using first-principles calculations on 11 of these. The most stable phase has Cmc21 symmetry with 20 atoms/unit cell. It lies 13 meV/atom above the convex hull but lacks octahedral cation coordination. The most stable perovskite-like structure has P3̅ symmetry with 10 atoms/unit cell and low octahedral connectivity. Structure-property trade-offs are highlighted, with calculated distortions generally widening the band gap, shifting it from direct to indirect, and flattening the band edges. This work showcases the synergy of genetic algorithms, machine-learned potentials, and first principles validation for discovering stable, complex materials. This archive contains all our input, output files, the data and scripts.

en: From symmetry to stability: structural and electronic transformation in Cs₂KInI₆

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).

first-principles

display_name: first-principles

halide-double-perovskite

display_name: halide-double-perovskite

machine-learned-interatomic-potentials

display_name: machine-learned-interatomic-potentials

phonons

display_name: phonons

polymorphs

display_name: polymorphs


en: The FAIR principles have transformed how computational materials data and workflows are shared, yet existing repositories can only serve pre-computed entries — their coverage is perpetually incomplete and cannot adapt to new questions on demand. We built OptiMat Alloys, a large-language-model-powered conversational agent for multi-principal element alloy exploration, on three pillars: a living database that stores every calculation with provenance, low-barrier accessibility through a web interface that requires zero programming expertise, and built-in uncertainty quantification via cross-potential and cross-configuration validation. By coupling foundational machine-learning interatomic potentials that span nearly the entire periodic table with natural-language interaction, the agent enables targeted, on-demand computation guided by the user's own domain knowledge — extending FAIR from pre-computed repositories to on-demand knowledge generation, and making computational alloy screening accessible to any materials scientist.

en: OptiMat Alloys: a FAIR, living database of multi-principal element alloys enabled by a conversational agent

envi

en: Environment

fair-principle

display_name: fair-principle

llm-agent

display_name: llm-agent

materials-informatics

display_name: materials-informatics

multi-principal-element-alloys

display_name: multi-principal-element-alloys

universal-machine-learning-potentials

display_name: universal-machine-learning-potentials


en:
This dataset contains the data (as well as the code required to generate the data) for the numerical experiments of our manuscript "Quantum Finite Temperature Lanczos Method".

Abstract:

The computation of thermal properties of quantum many-body systems is a central challenge in our understanding of quantum mechanics. We introduce the Quantum Finite Temperature Lanczos Method (QFTLM), which extends the finite-temperature Lanczos method to quantum computers by combining real-time quantum Krylov methods with efficient preparation of typical states for trace estimation. This approach enables the computation of thermal expectation values while avoiding the exponential scaling inherent to classical exact simulation techniques. Numerical experiments on the transverse-field Ising model show that QFTLM can reproduce thermal observables over a wide temperature range. We further analyze the influence of Krylov dimension, number of trace-estimator states, and Trotter error, and show that suitable regularization is essential for robustness in noisy settings. These results establish QFTLM as a promising framework for finite-temperature quantum simulation.

en: Data for manuscript on the quantum finite temperature Lanczos method

finite-temperature

display_name: finite-temperature

krylov

display_name: krylov

lanczos

display_name: lanczos

quantum

display_name: quantum

simulation

display_name: simulation


en: Maximally-localized Wannier functions are quantum wavefunctions resembling atomic orbitals that are used to describe electrons in condensed matter. Since their introduction in 1997, these functions have become ubiquitous in ab initio materials simulations, including applications in linear-scaling methods, strongly correlated electron systems, quantum transport, electron-phonon interactions, and topological materials. Despite their widespread adoption in a vast software ecosystem, Wannier functions have not yet attained their fullest potential in the presence of entangled bands, as their optimization remains challenging and labor-intensive. Here, we introduce a universal meta-optimization method that leverages workflow abstraction and machine-learning techniques like differential evolution and Bayesian optimization to generate globally optimized Wannier functions without human intervention. We demonstrate this approach through three applications: (i) autonomous interpolation of entangled band structures with millielectronvolt accuracy starting from coarse Brillouin zone grids, (ii) thousand-fold acceleration of fully ab initio Boltzmann transport calculations via the use of minimal coarse Brillouin zone grids, and (iii) ultrafast high-throughput calculations of high-precision Wannier functions for large materials libraries. This work brings calculations that previously required supercomputers within the reach of personal computers.The data generated in this work and reported in the manuscript is attached in the Data.tar.gz with full description in the Readme.md.

en: Meta-optimization of maximally-localized Wannier functions

machine-learning

display_name: machine-learning

meta-optimization

display_name: meta-optimization

wannier-functions

display_name: wannier-functions


en: Functionalizing Silicon Nanowires (SiNWs) through covalent attachment of organic molecules offers diverse advantages, including surface passivation, introduction of new functionalities, and enhanced material performance in applications like electronic devices and biosensors. Given the wide range of available functional molecules, systematic large-scale screening is crucial. Therefore, we developed an automated computational workflow using Python scripts in conjunction with the AiiDa framework to explore structural configurations of functional molecules adsorbed onto silicon surfaces. This workflow generates multiple adhesion configurations corresponding to different binding orientations using surface and functional molecule structures as inputs. This dataset contains data related to the structural optimization of molecules with single, double, and triple carbon-carbon bonds attached to the nanowire surface in various adhesion configurations. We describe the chemisorption on SiNWs using the slab models for the Si facets since our reference are samples with diameters of SiNWs around 50 nm, while the quantum confinement effects are important for diameters below 10 nm. For each configuration, structural characterization was conducted by calculating quantities including the bond distance between the two carbons closest to the surface and their respective bond angle relative to the z-axis, the carbon-silicon bond distance and its respective bond angle relative to the z-axis, along with the molecule's rotation angle in the xy plane. The values obtained are summarized in the main folder. The version v1 of dataset contains data related to the Si(111) surface and alkanes, alkenes, and alkynes with lengths from C2 to C10. The version v2 and v3 include also the moieties from C12 to C18. The version v4 extends the dataset with the data related to the Si(110) surface and alkanes, alkenes, and alkynes with lengths from C2 to C18. The version v5 also includes the optimized structures of SiNWs oriented along ⟨112⟩, exposing Si(110) and Si(111) facets, and featuring three different sizes: small (d = 0.7 nm), medium (d = 1.7 nm), and large (d = 2.6 nm). In this version (v6) the relaxation calculations for all the considered moieties are provided. For each system the most stable configuration is identified, and the analysis of the electronic properties is conducted in the listed references.

en: Density functional theory study of silicon nanowire surfaces functionalized by grafting organic molecules

aliphatic-molecules

display_name: aliphatic-molecules

dft

display_name: dft

nanowires

display_name: nanowires

silicon

display_name: silicon

surface-adsorption

display_name: surface-adsorption


en: Hydrogen carriers that enable efficient transport and on-demand release of molecular hydrogen (H2) are crucial for practical hydrogen-based energy systems. Hydrogen boride (HB) nanosheets, composed of boron and hydrogen in a 1:1 stoichiometric ratio, have promising potential as safe and lightweight hydrogen carriers owing to their high gravimetric hydrogen density (8.5 wt %). In particular, heating of HB nanosheets results in H2 release over a broad temperature range from 423 to 1473 K. However, the mechanism of the multimodal H₂ desorption remains unclear. In this work, we elucidate that the interlayer H···H distances (dH···H) determine the multimodal desorption of H2 especially in the lower-temperature range (< 623 K). HB nanosheets subjected to various temperatures under different H2 pressures were prepared to investigate the thermal stability of their bonding configurations. Infrared spectroscopy and temperature-programmed desorption measurements revealed the occurrence of hydrogen depletion while the bonding configuration remains unchanged. The first to third nearest dH···H, along with the numbers of the corresponding H···H pairs, were systematically calculated for four possible interlayer stacking types. The calculated H···H pairs distribution closely matched the experimental profile for the thermally induced H2 desorption, suggesting that the multimodal desorption of H2 in the lower-temperature range is governed by the distribution of hydrogen distances. This study elucidates the unique mechanisms of H2 formation in HB nanosheets and provides valuable insights into the design of two-dimensional hydrogen containing materials.

en: Interlayer hydrogen-hydrogen spacing regulates the formation of molecular hydrogen in hydrogen boride nanosheets

hydrogen-boride-nanosheet

display_name: hydrogen-boride-nanosheet

hydrogen-release

display_name: hydrogen-release

interlayer-space

display_name: interlayer-space

two-dimensional-materials

display_name: two-dimensional-materials


en: The Shastry-Sutherland compound SrCu2(BO3)2 (SCBO) is a paradigmatic low-dimensional quantum spin system, featuring geometrically frustrated interacting spin dimers and a rich landscape of emergent quantum phenomena. While its magnetic properties have been extensively explored, the nature of its electronic excitations - particularly the Cu2+ d–d transitions - remains poorly understood. A detailed characterization of these excitations is essential to determine the crystal field splitting and the degree of Cu 3d–O 2p hybridization, both of which underpin superexchange interactions and magnetic behavior in SCBO. Here, the electronic properties of SCBO are investigated by combining spectroscopic techniques with theoretical approaches for first-principles band structure calculations.Experimentally, we employ L3-edge resonant inelastic x‑ray scattering (RIXS) to directly probe intra-d excitations, supplemented by broadband infrared reflectivity and ellipsometry measurements to resolve charge-transfer (CT) features. Theoretically, multireference quantum chemistry calculations accurately reproduce the energies (1.8–2.4 eV) and symmetries of the observed d–d excitations, while band-structure calculations based on density functional theory elucidate the CT excitation spectrum. These combined approaches define the energy scales for both d–d and CT transitions, validate the computational framework, and provide quantitative microscopic parameters essential for refining superexchange-based magnetic models in this prototypical frustrated quantum antiferromagnet.

en: Probing high-energy electronic excitations in the Shastry-Sutherland compound SrCu2(BO3)2 with resonant inelastic x-ray scattering and optical spectroscopy

d-d-excitations

display_name: d-d-excitations

dftu

display_name: dftu

resonant-inelastic-x-ray-scattering

display_name: resonant-inelastic-x-ray-scattering

shastry-sutherland-model

display_name: shastry-sutherland-model


en: This dataset supports a spin-polarized density functional theory study examining whether O₂-activation-favorable active-site environments at Fe- and Co-substituted sulfur vacancies in monolayer MoS₂ can produce a comparable response for H-free N₂ adsorption. Side-on and end-on N₂ adsorption branches were evaluated together with projected density of states and crystal orbital Hamilton population analyses to distinguish coordination geometry, thermodynamic preference, and intramolecular bond weakening. A perpendicular electric field and K/Na co-adsorption were further considered as physical and chemical perturbations. The calculations show that shared side-on coordination does not produce O₂-like N₂ activation, while the thermodynamically preferred end-on branch retains a shorter and stronger N–N bond. Alkali co-adsorption substantially enhances N–N bond weakening but does not eliminate the O₂–N₂ activation gap or reverse the end-on thermodynamic preference. The archive provides the optimized structures, representative computational input and output data, and numerical source data supporting the reported analyses.

en: Alkali promotion partially bridges a geometry-activation mismatch for N2 at O2-active MoS2 single-atom sites

materialscloud

en: Materials Cloud

alkali-promotion

display_name: alkali-promotion

cohp

display_name: cohp

density-functional-theory

display_name: density-functional-theory

nitrogen-activation

display_name: nitrogen-activation

single-atom-catalysis

display_name: single-atom-catalysis


en: Summing all Feynman diagrams with quantitative accuracy is a holy grail in theoretical physics. In condensed matter, the lattice vibration (phonon) field couples with the electrons, leading to the formation of entangled electron-phonon (e-ph) states called polarons.  In the intermediate- and strong-coupling regimes common to many conventional and quantum materials, a many-body treatment of polarons requires adding up a large number of e-ph diagrams. Diagrammatic Monte Carlo (DMC) is an efficient method for diagram summation and has been employed to study polarons within simplified e-ph models (Holstein, Frohlich, etc.).  Here we show DMC calculations based on accurate first-principles e-ph interactions, enabling numerically exact results for ground-state and dynamical properties of polarons in real materials, including the polaron formation energy, effective mass, spectral weight, phonon cloud distribution, optical conductivity, and mobility. We demonstrate such DMC calculations in systems with polarons ranging from small (localized) to large (delocalized), including LiF, SrTiO3, and TiO2 rutile and anatase. This advance is enabled by our recently developed technique for compressing first-principles e-ph interaction matrices, together with a matrix-product formalism that mitigates the DMC sign problem from multiple electronic bands. Our work enables precise modeling of e-ph interactions and polarons in coupling regimes ranging from weak to strong, opening doors to studies of transport, linear response, and superconductivity in the strong e-ph coupling regime.

en: Dataset for first-principles diagrammatic Monte Carlo for electron-phonon interactions and polaron

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).

diagrammatic-monte-carlo

display_name: diagrammatic-monte-carlo

first-principles

display_name: first-principles

lif

display_name: lif

many-body-physics

display_name: many-body-physics

polaron

display_name: polaron

strong-e-ph-interactions

display_name: strong-e-ph-interactions