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


$oid: OBSOLETE

en: Skutterudites are promising materials for thermoelectric and spintronics applications. Here we explore spin fluctuations in the FeSb3 skutterudite and their effect on its electronic structure using Hubbard-corrected density-functional theory calculations. We identify multiple magnetic and charge-disproportionated configurations, with an antiferromagnetic metallic ground state. Paramagnetic fluctuations modeled through a special quasirandom spin structure open a 61 meV gap, consistent with experiments. This state features non-degenerate spin channels and band-avoided crossings, hinting at a potential altermagnetic transition with topological features. Mapping the electronic structure to a Heisenberg Hamiltonian fails to explain the low Néel temperature (10 K), highlighting the role of magnetic exchange frustration and the need for more in-depth experimental investigations.This dataset contains first-principles calculations supporting the study “Spin fluctuations steer the electronic behavior in the FeSb₃ skutterudite”. It includes linear-response Hubbard parameters, phonon dispersions, electronic band structures, special quasirandom structures modeling of paramagnetism, and magnetic exchange interactions for FeSb₃. The data document how spin fluctuations stabilize the structure, open a small electronic gap, and strongly affect magnetic energetics.

materialscloud

approval_status: approved

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

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

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

en: Spin fluctuations steer the electronic behavior in the FeSb3 skutterudite

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

condensed-matter-physics

display_name: condensed-matter-physics

first-principles-simulations

display_name: first-principles-simulations

heisenberg-model

display_name: heisenberg-model

magnetism

display_name: magnetism

paramagnetism

display_name: paramagnetism

special-quasi-random-structures

display_name: special-quasi-random-structures

spin-fluctuations

display_name: spin-fluctuations


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

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: Dynamical control of the optical nonlinear properties in solids – with light itself – will be essential for the development of ultrafast photonic technologies, including high-speed terahertz-rate data communication and advanced signal processing. Previously, methods for optically modulating nonlinear optical processes, such as second harmonic generation (SHG), have relied predominantly on non-resonant light-matter interaction or photo-generation of hot electrons in nanoscale materials. However, these approaches are typically constrained by limited interaction lengths and the initial frequency conversion is relatively weak under equilibrium conditions, making them impractical for devices that require high-contrast switching of strong signals. Here, modulation of a strong SHG signal in a bulk β-BaB2O4 (BBO) nonlinear optical crystal is achieved through resonant transient lattice deformation induced by intense terahertz (THz) pulses tuned to a specific infrared-active phonon mode. Selective lattice excitation results in an unprecedented ~30% modulation of the SHG in BBO. In contrast to past efforts employing non-resonant THz interaction, this effect cannot be explained by modulation of the nonlinear susceptibility – a 𝜒(3)-effect. But rather – through polarization-resolved measurements, ab initio calculations, and analytical modelling –the SHG modulation is found to be driven by THz-induced phonon dynamics that affect the nonlinear optical phase-matching conditions through a 𝜒(2) process. This resonant all-optical control mechanism points toward new optical control schemes, based on selected lattice degrees of freedom, to dynamically tune the overall nonlinear optical response in bulk materials. Such schemes may find practical applications in ultrafast optoelectronics and integrated photonics.

en: All-optical control of second-harmonic generation in β-BaB2O4 via coherent, terahertz-driven acentric lattice displacement

all-optical-control-in-bulk-crystals

display_name: all-optical-control-in-bulk-crystals

nonlinear-optical-phase-matching

display_name: nonlinear-optical-phase-matching

nonlinear-susceptibility

display_name: nonlinear-susceptibility

resonant-lattice-dynamics

display_name: resonant-lattice-dynamics

second-harmonic-generation

display_name: second-harmonic-generation

terahertz-phonon-excitation

display_name: terahertz-phonon-excitation


en: The origin of the metal-to-insulator transition (MIT) in RNiO3 perovskites with R = trivalent 4 f ion has challenged the condensed matter research community for almost three decades. A drawback for progress in this direction has been the lack of studies combining physical properties and accurate structural data covering the full nickelate phase diagram. Here we focus on a small region close to the itinerant limit (R = Pr, 1.5 K < T < 300 K), where we investigate the gap opening and the simultaneous emergence of charge order in PrNiO3. We combine electrical resistivity, magnetization, and heat capacity measurements with high-resolution neutron and synchrotron x-ray powder diffraction data that, in contrast to previous studies, we analyze in terms of symmetry-adapted distortion modes. Such analysis allow us to identify the contribution of the different modes to the global distortion in a broad temperature range. Moreover, it shows that the structural changes at the MIT, traditionally described in terms of the evolution of the interatomic distances and angles, appear as abrupt increases of all nonzero mode amplitudes at TMIT = TN ∼ 130 K accompanied by the appearance of new modes below this temperature. A further interesting observation is the existence of a nearly perfect linear correlation between the amplitude of the breathing mode associated to the charge order and the staggered magnetization below the MIT. Our data also uncover a previously unnoticed anomaly at T ∗ ∼ 60 K (∼0.4 × TMIT), clearly visible in the electrical resistivity, lattice parameters and some mode amplitudes. Since phase coexistence is only observed in a small temperature region around TMIT (∼±10 K), these observations suggest the existence of a hidden symmetry in the insulating phase. We discuss some possible origins, among them the theoretically predicted existence of polar distortions induced by the noncentrosymmetric magnetic order [Perez-Mato et al., J. Phys.: Condens. Matter 28, 286001 (2016); Giovanetti et al., Phys. Rev. Lett. 103, 156401 (2009)].

en: Distortion mode anomalies in bulk PrNiO3: Illustrating the potential of symmetry-adapted distortion mode analysis for the study of phase transitions

distortion-mode-amplitudes

display_name: distortion-mode-amplitudes

metal-insulator-transition

display_name: metal-insulator-transition

neutron-diffraction

display_name: neutron-diffraction

physical-properties

display_name: physical-properties

synchrotron-x-ray-diffraction

display_name: synchrotron-x-ray-diffraction


en:
If one carries out a molecular simulation of N particles using periodic boundary conditions, linear momentum is conserved and hence the number of degrees of freedom is set to 3N-3. In most programs, this number of degrees of freedom is the default setting. However, if one carries out a molecular simulation in an external field, one needs to ensure that degrees of freedom are changed from this default setting to 3N, as in an external field the velocity of the center of mass can change. Using the correct degrees of freedom is important in calculating the temperature and in some algorithms to simulate at constant temperature.
For sufficiently large systems, the difference between 3N and 3N-3 is negligible in the way. However, there are systems in which the comparison with experimental data requires molecular dynamics simulations of a small number of particles. In this work, we illustrate the effect of an incorrect setting of degrees of freedom in molecular dynamic simulations studying the diffusion properties of guest molecules in nanoporous materials. We show that previously published results have reported a surprising diffusion dependence on the loading, which could be traced back to an incorrect setting of the degrees of freedom. As the correct settings are convoluted and counter-intuitive in some of the most commonly used molecular dynamics programs, we carried out a systematic study on the consequences of the various commonly used (incorrect) settings. Our conclusion is that for systems smaller than 50 particles the results are most likely unreliable as these are either performed at an incorrect temperature or the temperature is incorrectly used in some of the results.
Furthermore, a novel and efficient method to calculate diffusion coefficients of guest molecules into nanoporous materials at zero loading conditions is introduced.

en: On the effects of the degrees of freedom on calculating diffusion properties in nanoporous materials

diffusion

display_name: diffusion

marvel

display_name: marvel

metal-organic-frameworks

display_name: metal-organic-frameworks

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

nanoporous-materials

display_name: nanoporous-materials

prace

display_name: prace

snsf

display_name: snsf

zeolites

display_name: zeolites


en: Cu(I)-containing oxide semiconductors containing early transition-metal cations have been of growing interest as small bandgap semiconductors. Though, their lack of thermodynamic stability has slowed the progress of their synthetic discovery. Two new Cu(I)-molybdovanadates and one new Cu(I)-tungstovanadate have been discovered in high temperature reactions and their structures characterized using single crystal X-ray diffraction. Their chemical compositions are given by CuVMoO6, Cu1.51(1)V3.57(1)Mo0.43(1)O11 and Cu1.59(1)V3.52(1)W0.48(1)O11. The latter two structures possess a Cu site with ~25% occupancy, whereas all structures show a statistical disorder of V/Mo or V/W cations over equivalent crystallographic sites. Using a supercell approach, density functional theory calculations have been performed to understand their thermodynamic stabilities and their relationships to their band structures and densities-of-states (DOS). Results of the calculations showed lower total energies are attained when the partially-filled Cu sites are oxygen-bridged to the V atoms rather than the Mo/W atoms. Results of their DOS and band structures were also found to provide deeper insights into their physical properties as ternary, Cu(I)-containing oxides.

en: Electronic structure calculations of Cu(I) molybdovanadate and tungstovanadate compounds

bulk-structures

display_name: bulk-structures

copper-oxides

display_name: copper-oxides

electronic-structure

display_name: electronic-structure


en: Electron-phonon coupling is a key interaction that governs diverse physical processes such as carrier transport, superconductivity, and optical absorption. Calculating such interactions from first-principles with methods beyond density-functional theory  remains a challenge. We introduce here a finite-difference framework for computing electron-phonon couplings for any electronic structure method that provides eigenvalues and eigenvectors, and showcase applications for hybrid and Koopmans functionals, and $GW$ many-body perturbation theory. Our approach introduces a novel projectability scheme based on eigenvalue differences and bypasses many of the limitations of the direct finite difference methods. It also leverages symmetries to reduce the number of independent atomic displacements, thereby keeping computational costs manageable. This approach enables seamless integration with established first-principles codes for generating displaced supercells, performing Wannier interpolations, and evaluating transport properties. Applications to silicon and gallium arsenide show that advanced electronic-structure functionals predict different electron-phonon couplings and modify band curvatures, resulting in much more accurate estimates of intrinsic carrier drift mobilities and effective masses. In general, our method provides a robust and accessible framework for exploring electron-phonon interactions in complex materials with state-of-the-art electronic structure methods.

en: Carrier mobilities and electron-phonon interactions beyond DFT

beyond-dft-methods

display_name: beyond-dft-methods

boltzmann-transport-equation

display_name: boltzmann-transport-equation

electron-phonon-coupling

display_name: electron-phonon-coupling

koopmans-functional

display_name: koopmans-functional


en: We investigate the absorption spectra of the F centers in MgO and CaO using material-specific hybrid functionals combined with time-dependent hybrid-functional theory calculations. Our study accounts for electron-hole interaction, zero-phonon renormalization, and finite-size effects, which are all found to contribute to the achieved accuracy. The spectra of the neutral states solely present absorption lines stemming from internal defect-to-defect transitions without involving conduction band states of the host. At variance, the spectra of the singly positively charged states show two lines, one analogous to those seen in the neutral states and an extra line corresponding to electron excitations from the valence band to the unoccupied defect level in the band gap. In MgO, the three transition energies pertaining to these two charged states are found to coincide, but are spread out in the case of CaO. The agreement with the energies of experimental lines is excellent, indicating that our scheme reaches an accuracy of 0.2 eV or better.

en: Absorption lines of F centers in MgO and CaO through time-dependent hybrid-functional theory calculations

defect

display_name: defect

dft

display_name: dft

oxide

display_name: oxide

time-dependent-dft

display_name: time-dependent-dft


materialscloud

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

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

en: Materials Cloud


en:
Phonon interactions from lattice anharmonicity govern thermal properties and heat transport in materials.These interactions are described by n-th order interatomic force constants (n-IFCs), which can be viewed as high-dimensional tensors correlating the motion of n atoms, or equivalently encoding n-phonon scattering processes in momentum space. Here, we introduce a tensor decomposition to efficiently compress n-IFCs for arbitrary order n. Using tensor learning, we find optimal low-rank approximations of n-IFCs by solving the resulting optimization problem. Our approach reveals the inherent low dimensionality of phonon-phonon interactions and allows compression of the 3 and 4-IFC tensors by factors of up to 10^3-10^4 while retaining high accuracy in calculations of phonon scattering rates and thermal conductivity. Calculations of thermal conductivity using the compressed n-IFCs achieve a speed-up by nearly three orders of magnitude with >98% accuracy relative to the reference uncompressed solution. These calculations include both 3- and 4-phonon scattering and are shown for a diverse range of materials (Si, HgTe, MgO, TiNiSn and monoclinic ZrO2).  In addition to accelerating state-of-the-art thermal transport calculations, the method shown here paves the way for modeling strongly anharmonic materials and higher-order phonon interactions.

This data contains the phonon scattering rates data with full and compressed phonon-phonon interactions. The data corresponds to Figure 3 in the paper and includes information on the phonon scattering rates for two materials: silicon and HgTe.

en: Phonon scattering rates data for silicon and HgTe

envi

en: Environment

phonon-phonon-interactions

display_name: phonon-phonon-interactions

phonon-scattering-rate

display_name: phonon-scattering-rate

thermal-transport

display_name: thermal-transport


en: Charge carrier mobilities are critical parameters in halide perovskite solar cells, governing their average carrier velocity under an applied electric field and overall efficiency. Recent advances in first-principles calculations of electron-phonon interactions and carrier mobilities have enabled predictive computations for perovskite solar cells. However, the flexible octahedral frameworks and cationic displacements in these materials challenge the harmonic approximation, leading to significant difficulties in accurately calculating transport properties. To address these issues, we combine temperature-dependent effective potentials with the ab initio Boltzmann transport equations to compute carrier mobilities in a representative lead-free perovskite, CsSnBr₃. At room temperature, the electron/hole Hall mobilities in CsSnBr₃ are 106/256 cm²/Vs when neglecting anharmonic effects and 59/145 cm²/Vs when included. This overestimation of the harmonic approximation arises from the neglect of scattering coming from soft modes. We provide a workflow for performing first-principles carrier mobility calculations in anharmonic systems, advancing the predictive modeling of perovskite solar cells.

en: Impact of anharmonicity on the carrier mobility of the Pb-free CsSnBr₃ perovskite

ab-initio

display_name: ab-initio

anharmonicity

display_name: anharmonicity

first-principles

display_name: first-principles

mobility

display_name: mobility

perovskite

display_name: perovskite


en: Cu(In,Ga)Se2 (CIGSe) is a highly efficient absorber material in thin-film solar cells that demonstrates a unique ability to tolerate massive Cu deficiency, which is often manifested in the formation of ordered defect compounds (ODCs). The conventional explanation is that the off-stoichiometry is mediated by the charge-neutral complexes forming in high concentrations. In this work, we refute this simplistic picture by treating the off-stoichiometry as an integral part of the crystal structure. We discover a topotactic transformation series of stable ODC-like structures with different compositions that enable large Cu deficiency in the material while being practically invisible to X-ray and neutron diffraction. These structures reproduce the known band gap widening and allow reconcile several lasting conflicts between experimental findings and first-principles calculations. This dataset contains the PBEsol-optimized primitive cells of the near-stable ODC structures in the topotactic series.

en: Functional off-stoichiometry in Cu(In,Ga)Se2

copper-deficiency

display_name: copper-deficiency

ordered-vacancy-compounds

display_name: ordered-vacancy-compounds

topotactic-transformation

display_name: topotactic-transformation

vacancy-arrangements

display_name: vacancy-arrangements


en:
Accurate determination of transition states is central to an understanding of reaction kinetics. Double-endpoint methods where both initial and final states are specified, such as the climbing image nudged elastic band (CI-NEB), identify the minimum energy path between the two and thereby the saddle point on the energy surface that is relevant for the given transition, thus providing an estimate of the transition state within the harmonic approximation of transition state theory. Such calculations can, however, incur high computational costs and may suffer stagnation on exceptionally flat or rough energy surfaces. Conversely, methods that only require specification of an initial set of atomic coordinates, such as the minimum mode following (MMF) method, offer efficiency but can converge on saddle points that are not relevant for transition of interest. Here, we present an adaptive hybrid algorithm that dynamically switches between the CI-NEB with the MMF method so as to get faster convergence to the relevant saddle point. The method is benchmarked for the Baker-Chan (BC) saddle point test set using the PET-MAD machine-learned potential as well as 59 transitions of a heptamer island on Pt(111) from the OptBench benchmark set. A Bayesian analysis of the performance shows a median change in energy and force calculations of -46% [95% CrI: -55%, -37%] relative to CI-NEB for the BC set, while a 28\% reduction is found for the transitions of the heptamer island. These results establish this hybrid method as a highly effective tool for high-throughput automated chemical discovery of atomic rearrangements.

This record provides the reproduction data, transition state search logs, and statistical models for the Replica Optimization of Nudged Elastic Bands (RONEB) algorithm. It includes the complete outputs to accompany the Github repository of scripts which support the manuscript.

en: Enhanced Climbing Image Nudged Elastic Band method with Hessian Eigenmode Alignment

machine-learning

display_name: machine-learning

nudged-elastic-band

display_name: nudged-elastic-band

saddle-search

display_name: saddle-search

transition-state

display_name: transition-state


en: The development of machine-learning models for atomic-scale simulations has benefitted tremendously from the large databases of materials and molecular properties computed in the past two decades using electronic-structure calculations. More recently, these databases have made it possible to train “universal” models that aim at making accurate predictions for arbitrary atomic geometries and compositions. The construction of many of these databases was however in itself aimed at materials discovery, and therefore targeted primarily to sample stable, or at least plausible, structures and to make the most accurate predictions for each compound – e.g. adjusting the calculation details to the material at hand. Here we introduce a dataset designed specifically to train models that can provide reasonable predictions for arbitrary structures, and that therefore follows a different philosophy. Starting from relatively small sets of stable structures, the dataset is built to contain “massive atomic diversity” (MAD) by aggressively distorting these configurations, with near-complete disregard for the stability of the resulting configurations. The electronic structure details, on the other hand, are chosen to maximize consistency rather than to obtain the most accurate prediction fora given structure, or to minimize computational effort. The MAD dataset we present here, despite containing fewer than 100k structures, has already been shown to enable training universal interatomic potentials that are competitive with models trained on traditional datasets with two to three orders of magnitude more structures. We describe in detail the philosophy and details of the construction of the MAD dataset. We also introduce a low-dimensional structural latent space that allows us to compare it with other popular datasets, and that can also be used as a general-purpose materials cartography tool.

en: Massive Atomic Diversity: a compact universal dataset for atomistic machine learning

atomistic-simulations

display_name: atomistic-simulations

dataset

display_name: dataset

density-functional-theory

display_name: density-functional-theory

foundation-models

display_name: foundation-models

interatomic-potentials

display_name: interatomic-potentials


en: We perform a high-throughput computational search for novel phonon-mediated superconductors, starting from the Materials Cloud 3-dimensional database (MC3D) of experimentally known inorganic stoichiometric compounds. We first compute the Allen-Dynes critical temperature Tc for 4533 non-magnetic metals using a direct and progressively finer sampling of the electron-phonon couplings. For the candidates with the largest Tc, we use automated Wannierizations and electron-phonon interpolations to obtain a high-quality dataset for the most promising 250 dynamically stable structures, for which we calculate spectral functions, superconducting bandgaps, and isotropic Migdal-Eliashberg critical temperatures. For 139 of these, we also provide anisotropic Migdal-Eliashberg superconducting gaps and critical temperatures. The approach is remarkably successful in finding known superconductors, and we find 24 unknown ones with a predicted anisotropic Tc above 10 K. Among them, we identify a possible double gap superconductor (p-doped BaB2), a non-magnetic half-Heusler ZrRuSb, and the perovskite TaRu3C, all exhibiting significant Tc. Finally, we introduce a sensitivity analysis to estimate the robustness of the predictions.

en: Charting the landscape of Bardeen-Cooper-Schrieffer superconductors in experimentally known compounds

electron-phonon-coupling

display_name: electron-phonon-coupling

high-throughput

display_name: high-throughput

migdal-eliashberg

display_name: migdal-eliashberg

superconductivity

display_name: superconductivity


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. This 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. For each system the most stable configuration will be identified, and the analysis of the electronic properties will be conducted.

en: Density functional theory study of silicon nanowires 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: The limited choice of oxygen evolution reaction catalysts for proton exchange membrane water electrolyzers hinders their large-scale commercialization. Cobalt-based catalysts are promising candidates and usually undergo surface reconstruction into CoOOH-like structures. Despite this, the directly synthesized CoOOH has not yet been investigated in acidic environments. Here, we show that the CoOOH is active across the whole pH range, while its redox features is pH dependent. Operando hard X-ray absorption spectroscopy characterizations show a pH-induced change Co oxidation onset, but no change in the coverage of redox-active Co species before the oxygen evolution reaction. The pH-dependent catalytic performance is connected to the interfacial Co oxidative transformations under OER conditions. Combining the kinetic isotope effect and the apparent activation energy with theoretical verification, we provide the mechanistic insight on the possible OER pathway for CoOOH. In addition, CoOOH demonstrate a stable cell potential at 100 mA cm-2 for 400 hours in a proton exchange membrane water electrolyzer. These results provide insight into both the fundamental electrochemical properties of CoOOH and its potential for practical device performance.

materialscloud

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

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

en: Directly synthesized cobalt oxyhydroxide as an oxygen evolution catalyst in proton exchange membrane water electrolyzers

coooh

display_name: coooh

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction


materialscloud

en: Materials Cloud


envi

en: Environment

first-principles

display_name: first-principles


en: Metal organic frameworks (MOFs) have a promising perspective as oxygen evolution reaction (OER) electrocatalysts due to their high surface areas and tunable structures. However, one of the main challenges for their further application is inferior stability during alkaline OER. Herein, operando x-ray absorption spectroscopy and operando x-ray diffraction of NiCo-MOF-74 materials unveil their electrochemical transformations differentiating between electrolyte-induced, beam-induced, and electrochemically induced changes of the electronic state and local structure around the transition metal centers in addition to their overall crystal structure. An inferior electrolyte- and beam stability of Co-MOF-74 is revealed in comparison to a more stable performance of Ni-MOF-74 and Ni0.25Co0.75-MOF-74. Based on the operando measurement results, good experimental practices for future MOF OER electrocatalyst studies are presented.

en: Operando X-ray absorption and diffraction investigations on Ni- and Co-MOF-74 oxygen evolution electrocatalysts

chemical-stability

display_name: chemical-stability

operando-x-ray-absorption-spectroscopy

display_name: operando-x-ray-absorption-spectroscopy

operando-x-ray-diffraction

display_name: operando-x-ray-diffraction

radiation-stability

display_name: radiation-stability

stability-of-mofs

display_name: stability-of-mofs


en: The substantial consumption of lithium (Li) ions and sluggish reaction kinetics at the anode detrimentally impact the deliverable energy and fast-charging capability of lithium-ion batteries (LIBs) with silicon-based anodes. The prevailing contact prelithiation method using an electrolyte medium can replenish the active Li, but it may cause materials/electrode instability and bring barrier for Li-ion transport due to the nonuniform reaction. Here we explore a contact prelithiation methodology employing cyclic carbonate mediums that can enable spatially and temporally uniform prelithiation reaction. These mediums enable a delicate equilibrium between a Li-ion diffusion and the intrinsic prelithiation reaction rate of the material throughout the electrode depth. To gain an in-depth insight into the mechanism for producing Li-ions under the contact between Li metal and cyclic carbonate solvents, we undertook molecular dynamics simulations. Our calculation results of the self-produced Li alkyl carbonates reveal that while the long-chain Li alkyl carbonates are capable of forming an extensive polymer network in FEC and participating in the formation of a stable SEI on the Li metal surface, short-chain Li alkyl carbonates can dissolve into the solvent to produce Li-ions.

en: A salt-free medium facilitating electrode prelithiation towards fast-charging, high-energy lithium-ion batteries

battery

display_name: battery

lithium

display_name: lithium

molecular-dynamics

display_name: molecular-dynamics


en: The behavior of charge carriers in polar materials is governed by electron-phonon interactions, which affect their mobilities via phonon scattering and may localize carriers into self-induced deformation fields, forming self-trapped polarons. We present a first-principles study of self-trapped polaron formation in paradigmatic polar semiconductors and insulators using the variational polaron equations framework and self-consistent gradient optimization. Our method incorporates long-range corrections to the electron-phonon interaction, essential for finite-size systems. We demonstrate how the variational approach enables the identification of multiple polaronic states and supports the analysis of polarons with arbitrarily large spatial extent via energy filtering. The potential energy surfaces of the resulting polarons exhibit multiple local minima, reflecting distinct, symmetry-broken polaronic configurations in systems with degenerate band edges. Our findings align with previous theoretical studies and establish a robust foundation for future ab initio studies of polarons, especially those employing variational methods.This archive contains the data necessary to reproduce the results of the "Variational first-principles approach to self-trapped polarons" article by Vasilii Vasilchenko, Matteo Giantomassi, Samuel Poncé, and Xavier Gonze.

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles-calculations

display_name: first-principles-calculations

polarons

display_name: polarons

variational-wave-functional-methods

display_name: variational-wave-functional-methods


en: Variational first-principles approach to self-trapped polarons


en: Maximally-localized Wannier functions (MLWFs) are widely employed as an essential tool for calculating the physical properties of materials due to their localized nature and computational efficiency. Projectability-disentangled Wannier functions (PDWFs) have recently emerged as a reliable and efficient approach for automatically constructing MLWFs that span both occupied and lowest unoccupied bands. Here, we extend the applicability of PDWFs to magnetic systems and/or those including spin-orbit coupling, and implement such extensions in automated workflows. Furthermore, we enhance the robustness and reliability of constructing PDWFs by defining an extended protocol that automatically expands the projectors manifold, when required, by introducing additional appropriate hydrogenic atomic orbitals. We benchmark our extended protocol on a set of 200 chemically diverse materials, as well as on the 40 systems with the largest band distance obtained with the standard PDWF approach, showing that on our test set the present approach delivers a 100% success rate in obtaining accurate Wannier-function interpolations, i.e., an average band distance below 15meV between the DFT and Wannier-interpolated bands, up to 2 eV above the Fermi level.

en: Robust Wannierization including magnetization and spin-orbit coupling via projectability disentanglement

aiida

display_name: aiida

automation

display_name: automation

marvel

display_name: marvel

projectability-disentanglement

display_name: projectability-disentanglement

snsf

display_name: snsf

wannier-functions

display_name: wannier-functions

wannierization

display_name: wannierization


en: The direct conversion of methane-to-methanol remains a critical challenge in methane valorization. In this study, we unveil the crucial role of PdAu/CeO2 catalysts in enabling selective methane transformation under mild conditions, using only water as the sole oxidant. Through a combination of experimental techniques, including XPS and catalytic testing, alongside density functional theory (DFT) calculations, we demonstrate that a Pd0.3Au0.7/CeO2 catalyst, which predominantly exposes isolated Pd atoms, achieves remarkable methanol selectivity (∼80%) at 500 K with a 1:1 methaneto-water ratio. While Pd/CeO2 efficiently activates methane, its tendency for overreaction leads to complete methanol decomposition, thereby limiting selectivity. Alloying Pd withAu on ceria mitigates this over-reactivity, preventing methanol degradation while maintaining sufficient catalytic activity. The PdAu/CeO2 composite exhibits a synergistic effect: Pd in contact with the ceria support facilitates methane activation and water dissociation, while Au fine-tunes reactivity to promote methanol formation. DFT calculations confirm that isolated Pd sites at the PdAu/CeO2 interface play a key role in balancing activity and selectivity. This work underscores the importance of alloy/oxide interfaces in controlling selective methane conversion with water and offers valuable insights for designing highly efficient catalysts for methanol synthesis.

en: Engineering PdAu/CeO2 alloy/oxide interfaces for selective methane-to-methanol conversion with water

alloy

display_name: alloy

bimetallic

display_name: bimetallic

ceria

display_name: ceria

computational-chemistry

display_name: computational-chemistry

dft

display_name: dft

gofee

display_name: gofee

gold

display_name: gold

heterogeneous-catalysis

display_name: heterogeneous-catalysis

methane-to-methanol

display_name: methane-to-methanol

nanoparticle

display_name: nanoparticle

palladium

display_name: palladium


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

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: Polymers are versatile materials with a wide range of applications. The improvement of polymer properties rises the importance on the way that the repeating units are connected (head-to-tail,head-to-head,tail-to-tail) to build the polymer structure since it directly influences the morphology, chain topology and consequently its properties. Artificial intelligence (AI) based approaches are beginning to impact several domains of human life, science and technology. Polymer informatics is one such domain where AI and machine learning (ML) tools are being used in the efficient development, design and discovery of polymer. One key enabling factor for the essential foundations for Polymer Informatics is the machine-readable polymer representation. Polymer have been represented in a string format with special characters used to tag the head and tail positions indicating where the linking bond happens between repeat units. Available tools to assign the head and tail position limits its applicability in a broad sense. In this work we show a new tool to assign the head and tail atoms for a given monomer. From a database of 206 polymer precursors curated from the literature, our algorithm correctly predicted the class of 201 data points, which represents 97.6% of accuracy and regarding the the head and tail assignment, correctly assigned the positions for 188 data points, which translates to 91.3% of accuracy.

en: HTA - An open-source software for assigning heads and tails to SMILES in polymerization reactions

head-and-tail-atom-position

display_name: head-and-tail-atom-position

polymer

display_name: polymer

repeat-unit

display_name: repeat-unit


materialscloud

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

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


en:
GapExtractor v2.0© is a free software designed to determine the band gap energy of non-translucent samples by analyzing their diffuse reflectance spectra. The analysis is based on the Tauc-method and the Kubelka and Munk formalism. The algorithm is coded in the Python 3.11.5 programming language. By coping, running or distributing this software, you indicate your acceptance of the license terms.

Steps to reproduce: Unzip GapExtractor v2.0 file; run gapextractor_v2.0.py; check the required phyton libraries; install if necessary.

en: Materials Cloud

en: GapExtractor v2.0

band-gap-energy

display_name: band-gap-energy

diffuse-reflectance-spectroscopy

display_name: diffuse-reflectance-spectroscopy

polyphasic-samples

display_name: polyphasic-samples

tauc-method

display_name: tauc-method


en: Diboron trioxide (B2O3) represents an unusual case among polymorphic oxides, for its vitrified state features superstructural units – planar boroxol groups – that are never observed in its three-dimensional crystalline polymorphs. Crystalline polymorphs that incorporate boroxol groups have only been predicted theoretically, although their formation is crucial to rationalize the ability of B2O3 to vitrify. Here we present the synthesis of a two-dimensional crystalline B2O3 polymorph constituted by boroxol groups arranged in an atomically thin honeycomb lattice. By combining surface science experimental techniques with ab initio calculations, we characterize the structural and electronic properties of this B2O3 polymorph down to the atomic level. This discovery enlarges the family of two-dimensional materials and enables the atomic tracking of individual structural units in trioxides.

en: Two-dimensional diboron trioxide crystal composed by boroxol groups

envi

en: Environment

boroxol-groups

display_name: boroxol-groups


en:
Water inherently contains trace amounts of various salts, yet the microscopic processes by which salts influence some of its physical properties remain elusive. Notably, the mechanisms that reduce the dielectric constant of water upon salt addition are still debated. The primary absorption peak for electromagnetic radiation-commonly used in microwave heating-shifts towards higher frequencies in saline solutions, suggesting faster water molecular dynamics. This observation, however, contrasts with the simultaneous increase in viscosity and experimental reports that ionic solutes would slow down water molecular motion. In this work, we use molecular dynamics (MD) simulations with deep-neural-network models trained on high-quality quantum mechanical data to mimic interatomic forces and molecular dipoles, to compute the dielectric spectra of perchlorate water saline solution, which may be relevant to the recent discovery of liquid water beneath the thick ice crust at Mars’s south pole. Our results reveal that both the reduction in the dielectric constant and the absorption peak shift can be attributed to ion-induced changes in the orientational ordering of water molecules. Additionally, we demonstrate that the self-part of the molecular dipole-dipole correlation function reveals clear signatures of the slowing dynamics within the first cationic solvation shell, consistent with the experimentally observed increase in viscosity.

This record contains the DeepMD neural network potential and the underlying training data as well as the neural network for prediciting Wannier centroids and the underlying training data.

en: How salt solvation slows water dynamics while blue-shifting its dielectric spectrum

deepmd

display_name: deepmd

max

display_name: max

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

neural-network-potential

display_name: neural-network-potential

salt

display_name: salt

water

display_name: water


en: Understanding the behavior of materials under irradiation is crucial for the design and safety of nuclear reactors, spacecraft, and other radiation environments. The threshold displacement energy (Ed) is a critical parameter for understanding radiation damage in materials, yet its determination often relies on costly experiments or simulations.This work presents a compilation of threshold displacement energies (Ed) and fundamental material parameters (e.g., density, atomic mass, melting temperature) designed to enable the application of the machine learning-based Sure Independence Screening and Sparsifying Operator (SISSO) method. The goal is to develop accurate, analytical models for predicting Ed based on intrinsic material properties. The models outperform traditional approaches for monoatomic materials, capturing key trends with high accuracy. While predictions for polyatomic materials highlight challenges due to dataset complexity, they reveal opportunities for improvement with expanded data. This study identifies cohesive energy and melting temperature as key factors influencing Ed, offering a robust framework for efficient, data-driven predictions of radiation damage in diverse materials.

en: Machine learning-driven analytical models for threshold displacement energy prediction in materials

materials

display_name: materials

radiation-damage

display_name: radiation-damage

threshold-displacement-energy

display_name: threshold-displacement-energy


en: Thermal transport in low-dimensional materials is of great fundamental and applied interest due to their unusual and widely tunable properties, as demonstrated by numerous studies of two- and one-dimensional (1D) crystals. In grim contrast, low-dimensional amorphous materials remain largely unexplored, despite their potentially unique characteristics and applications. Here, we theoretically explore thermal transport in quasi-1D single-walled amorphous carbon nanotubes (a-CNTs) by combining homogeneous nonequilibrium molecular dynamics and lattice dynamics, based on a custom-trained high-accuracy machine-learned potential. For both zigzag and armchair a-CNTs of different diameters, the quantum-corrected thermal conductivity (κ) consistently drops by about an order of magnitude as the degree of disorder increases from 0.01 to 0.08, which is quantified by the concentration of Stone-Wales defects. Spectral analysis reveals the predominant role of the low-frequency (<3 THz) vibrational modes, the mean free paths (MFPs) of which are long but finite, thus leading to converged κ as the tube length reaches several micrometers. Further, lattice dynamics calculations yield much larger MFPs for the longitudinal acoustic modes compared to the transverse acoustic modes, which is primarily attributed to the distinctly higher group velocity of the former. Moreover, the κ suppression appears to be dictated by the structural disorder while anharmonicity barely contributes. Our work expands the understanding of thermal transport in low-dimensional amorphous materials and may help promote their eventual application. This record contains data and scripts that support the findings of this article.

en: Thermal transport in amorphous carbon nanotubes

amorphous-carbon-nanotube

display_name: amorphous-carbon-nanotube

lattice-dynamics

display_name: lattice-dynamics

machine-learned-potential

display_name: machine-learned-potential

molecular-dynamics

display_name: molecular-dynamics

thermal-transport

display_name: thermal-transport


en: Spinodal decomposition is a phenomenon involving spontaneous phase separation in a solid alloy. It is often considered as the prototype of second order phase transitions and is known for the formation of non-trivial patterns. In the presence of a lattice mismatch f between the components, even more complex, qualitatively different patterns emerge, depending on the specific f values. Despite being interesting in practical and theoretical settings, modeling this class of phenomena may be hindered by computational costs. Lately, applications of Machine Learning (ML) promise to mitigate these issues. The availability of suitable datasets is therefore of primary importance for the development of said models. We here provide a collection of phase field simulations of spinodal decomposition involving elastic effects under different mismatch conditions f. These may be used in training a ML model both for the forward problem of predicting the evolution given an initial condition and for the inverse problem of extracting the misfit parameter from a sequence. The dataset is conveniently already divided into training, validation and test sets. The data also support the ML framework, based on a convolutional recurrent scheme, discussed in the related publication in refrences and allows for the full reproduction of the reported findings. Once trained, the neural network is able to accurately reproduce ground-truth evolution even in critical regions of the parameter space (e.g., near the onset of metastability) and predict the misfit parameters to a high degree of accuracy (~0.01% absolute strain).

en: Strained alloy microstructure dataset for ML property prediction and time-evolution simulation

alloy-microstructures

display_name: alloy-microstructures

convolutional-neural-network

display_name: convolutional-neural-network

elastic-field

display_name: elastic-field

lattice-mismatch

display_name: lattice-mismatch

machine-learning

display_name: machine-learning

phase-field-simulations

display_name: phase-field-simulations

recurrent-neural-network

display_name: recurrent-neural-network

spinodal-decomposition

display_name: spinodal-decomposition

surrogate-modeling

display_name: surrogate-modeling


en: Gallium nitride (GaN) is a wide-bandgap semiconductor of significant interest for applications in solid-state lighting, power electronics, and radio-frequency amplifiers. An important limitation of this semiconductor is its low intrinsic hole mobility, which hinders the development of p-channel devices and the large-scale integration of GaN CMOS in next-generation electronics. Prior research has explored the use of strain to improve the hole mobility of GaN, but a systematic analysis of all possible strain conditions and their impact on the mobility is lacking. In this study, we introduce a piezomobility tensor notation to characterize the relationship between applied strain and hole mobility in GaN. To map the strain-dependence of the hole mobility, we solve the ab initio Boltzmann transport equation, accounting for electron-phonon scattering and GW quasiparticle energy corrections.

en: Design of high-mobility p-type GaN via the piezomobility tensor

electron-phonon-coupling

display_name: electron-phonon-coupling

hole-mobility

display_name: hole-mobility

nitrides

display_name: nitrides


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).


materials

display_name: materials

radiation-damage

display_name: radiation-damage

threshold-displacement-energy

display_name: threshold-displacement-energy


en: Super-exchange most often leads to antiferromagnetism in transition-metal perovskite oxides, yet ferromagnetism or ferrimagnetism would be preferred for many applications, for example in data storage. While alloying, epitaxial strain and defects were shown to lead to ferromagnetism, engineering this magnetic order remains a challenge. We propose, based on density functional theory calculations, a novel route to defect-engineer ferrimagnetism, which is based on preferential displacements of oxygen vacancies due to finite temperature vibrations. This mechanism has an unusual temperature dependence, as it is absent at 0K, strengthens with increasing temperature before vanishing once oxygen vacancies disorder, giving it a unique experimentally detectable signature.

en: Ferrimagnetism induced by thermal vibrations in oxygen-deficient manganite heterostructures

ferrimagnetism

display_name: ferrimagnetism

oxygen-vacancy

display_name: oxygen-vacancy

perovskite-manganite

display_name: perovskite-manganite


materialscloud

en: Materials Cloud


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.

deutsche-forschungsgemeinschaft

display_name: deutsche-forschungsgemeinschaft

lps

display_name: lps

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: Reconstructions and dynamics of 𝛽 -lithium thiophosphate surfaces

machine-learning

display_name: machine-learning


en: ZnO nanoparticle powders are an important and common starting material for functional devices that require the highest purity for electronic and optical applications. In this combined ab initio and experimental study, we use electron paramagnetic resonance (EPR) spectroscopy to detect shallow donor states in the ZnO lattice via their characteristic EPR resonance at g = 1.96. Using DFT calculations that account for the chemical environment and the high temperatures during the gas-phase synthesis in conjunction with control experiments with atomic hydrogen, we show that these paramagnetic defects correspond to neutral hydrogenated oxygen sites (OHO0). As a key implication of these findings, ZnO particle powders, although produced under pure gas-phase synthesis conditions and subsequently annealed in vacuum to 873 K, were found to be doped with hydrogen at a base concentration of 10-5 at% and higher. This, in turn, highlights the often-overlooked role of unwanted hydrogen in nanoscale ZnO as components for applications in optics, electronics, and sensing.

en: Ubiquitous shallow trap states and lattice hydrogenation of ZnO particles

envi

en: Environment

defect

display_name: defect

hydrogen

display_name: hydrogen

zno

display_name: zno


en: Fullerides forms a big familiy of molecular crystals exhibiting various useful electro- and magnetochemical properties. Therefore, an efficient in silico method is desirable to look into such chemical system. To alleviate the burden of computationally-heavy first-principles calculations, here we present the successful attempts of using Machine Learning Force Field (MLFF) in predicting dynamical properties of the alkali-doped fulleride K₃C₆₀. Two on-the-fly Gaussian-Process-MLFF schemes based on different atomistic descriptors, Smooth Overlap of Atomic Position (SOAP) and Atomic Cluster Expansion (ACE), have been experimented. The performance of generated K₃C₆₀ MLFFs are validated by accurate prediction on energy and forces of 1,000 randomly disturbed K₃C₆₀ structures compared to respective DFT results. Several other dynamical properties including phonon dispersion, elastic moduli and heat capacity obtained by MLFFs have also shown good agreement with results from DFT calculations, whose computational cost on such post-processing is several times more expensive than training a single MLFF that performs effortless post-processing. This shows the potential of applying MLFF to complex molecular crystal systems and pave the way to the investigation of much more intricate fulleride properties.

en: Efficient modeling of dynamic properties in K₃C₆₀ using machine learning force fields

dynamical-properties

display_name: dynamical-properties

machine-learning-force-field

display_name: machine-learning-force-field

molecular-crystal

display_name: molecular-crystal


en:
This database contains the reference data used for direct force training of Artificial Neural Network (ANN) interatomic potentials using the atomic energy network (ænet) and ænet-PyTorch packages (https://github.com/atomisticnet/aenet-PyTorch). It also includes the GPR-augmented data used for indirect force training via Gaussian Process Regression (GPR) surrogate models using the ænet-GPR package (https://github.com/atomisticnet/aenet-gpr).
Each data file contains atomic structures, energies, and atomic forces in XCrySDen Structure Format (XSF). The dataset includes all reference training/test data and corresponding GPR-augmented data used in the four benchmark examples presented in the reference paper, "Scalable Training of Neural Network Potentials for Complex Interfaces Through Data Augmentation".
A hierarchy of the dataset is described in the README.txt file, and an overview of the dataset is also summarized in supplementary Table S1 of the reference paper.

en: Database of scalable training of neural network potentials for complex interfaces through data augmentation

aenet

display_name: aenet

ann

display_name: ann

first-principles

display_name: first-principles

gpr

display_name: gpr

li-metal-battery

display_name: li-metal-battery

potential-energy-surface

display_name: potential-energy-surface


en: To investigate charged defects in gapped materials by first-principles calculations, they must be charged by either adding/removing electrons or compensating donors/acceptors. The former approach is more common, but it is not without drawbacks. We tested the latter method for a collection of model systems consisting of charge-compensated point defects in diamond (NV/SiV-centers, substitutional nitrogen/phosphorous/oxygen/sulfur donors, and substitutional boron/beryllium acceptors), comparing the geometrical and electronic properties of the compensated defect pairs with those of the individual defects in charged supercells. We find that the charging by explicit donors/acceptors works well and can be advantageous if properly designed, although interpretation of the results can be challenging. In this archive, we share the final optimized geometries of all studied structural models (in CIF format).

en: Neutral supercells for charged impurities by explicit acceptor/donor compensation - defects in diamond

compensating-defect

display_name: compensating-defect

jellium-counter-charge

display_name: jellium-counter-charge

nv-center

display_name: nv-center

siv-center

display_name: siv-center


en:
Experiments involving resonant optical excitation of infrared-active phonons in crystals have emerged as a powerful new way to tune materials properties. A puzzling and so far unexplained aspect of some so-called nonlinear phononics experiments is that the observed lifetimes of the optically created metastable phases are sometimes orders of magnitude longer than expected based on the nonlinear phononics mechanism assumed in most works. We use a combination of phenomenological theory and first-principles calculations to demonstrate that strong coupling between different lattice degrees of freedom (strains and Raman-active phonons) can give rise to a long-lived metastable phase recently observed in experiments on perovskite LaAlO3 [Hortensius et al. npj Quantum Mater. 5 95 (2020)]. We show that the long-timescale oscillatory response in the experimental optical reflectivity data is not due solely to shear strains, as originally suggested, but arises from a “hybrid” mode involving displacements of Raman-active phonons of the same symmetry. Our work suggests that strong coupling between different order parameters can provide a mechanism for long-lived optically created metastable phases and points towards strategies, such as strain engineering, for modifying or increasing the lifetime of light-induced phases in ultrafast optical experiments.

This archive contains the data files used to produce Figures 2-4 of the described paper.

en: Understanding long-lived metastable phases in ultrafast optical experiments

complex-oxides

display_name: complex-oxides


materialscloud

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

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


en: 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


materialscloud

en: Materials Cloud


en: Ultrathin nanowires could play a central role in next-generation downscaled electronics. Here, we explore some of the most promising candidates identified from previous high-throughput screening: CuC2, TaSe3, and AuSe2, to gain insight into the thermodynamic and anharmonic behaviors of nanowires that could be exfoliated from weakly-bonded three-dimensional materials. We analyze thermal stability, linear thermal expansion, and anharmonic heat capacity using the stochastic self-consistent harmonic approximation. Notably, our work unveils exotic features common among all the 1D wires: a colossal record negative thermal expansion and very large deviations from the Dulong-Petit law due to strong anharmonicity.

en: Extreme anharmonicity and thermal contraction of 1D wires

anharmonicity

display_name: anharmonicity

density-functional-theory

display_name: density-functional-theory

heat-capacity

display_name: heat-capacity

negative-thermal-expansion

display_name: negative-thermal-expansion

one-dimensional-materials

display_name: one-dimensional-materials

sscha

display_name: sscha


en: The confining walls made by 2D materials are often considered as solid boundary conditions in studies of fluid transport through nanochannels, while the atomically thin walls inherently exhibit thermal fluctuations at finite temperature. In this work, we investigate the solid-liquid interfacial friction properties of water confined within flexible nanochannels using machine-learning-potential molecular dynamics. Surprisingly, we find that the friction coefficient (λ) increases with lateral size L in the flexible nanochannels, following a linear relationship with 1/L, which is absent in rigid channels. For thicker nanochannel walls with large bending stiffness (D), this size dependence weakens. This 1/L scaling resembles the size-dependent thermal fluctuation amplitude of 2D sheets, suggesting that λ can be well controlled by applying the mechanical strain to the channel walls via controlling the phonon modes associated with surface fluctuations. Further analysis reveals that the λ can be decomposed into the contribution from lattice roughness and the thermal-fluctuation-induced ripple of 2D surface, where the second term scales with D−1/2. These findings offer insights into the manipulation of nanoscale flow through precise control of local curvature and fluid-solid coupling.

en: Machine-learning potential molecular dynamics reveals the critical role of flexibility in solid-liquid nanofluidic friction

flexibility

display_name: flexibility

liquidsolid-friction

display_name: liquidsolid-friction

machine-learning-potential

display_name: machine-learning-potential


envi

en: Environment


en: Quantum confinement endows two-dimensional (2D) layered materials with exceptional physics and novel properties compared to their bulk counterparts. Although certain two- and few-layer configurations of graphene have been realized and studied, a systematic investigation of the properties of arbitrarily layered graphene assemblies is still lacking. We introduce theoretical concepts and methods for the processing of materials information, and as a case study, apply them to investigate the electronic structure of multi-layer graphene-based assemblies in a high-throughput fashion. We provide a critical discussion of patterns and trends in tight binding band structures and we identify specific layered assemblies using low-dispersion electronic bands as indicators of potentially interesting physics like strongly correlated behavior. A combination of data-driven models for visualization and prediction is used to intelligently explore the materials space. This work more generally aims to increase confidence in the combined use of physics-based and data-driven modeling for the systematic refinement of knowledge about 2D layered materials, with implications for the development of novel quantum devices.

en: Electronic structure calculations of twisted multi-layer graphene superlattices

graphene

display_name: graphene

high-throughput-calculations

display_name: high-throughput-calculations

machine-learning

display_name: machine-learning

quantum-devices

display_name: quantum-devices

tight-binding-band-structures

display_name: tight-binding-band-structures

twistronics

display_name: twistronics


en:
Density functional theory (DFT) is a widely used method to compute properties of materials, which are often collected in databases and serve as valuable starting points for further studies. In this article, we present the Materials Cloud Three-Dimensional Structure Database (MC3D), an online database of computed 3D inorganic crystal structures. Close to a million experimentally reported structures were imported from the COD, ICSD and MPDS databases; these were parsed and filtered to yield a collection of 72589 unique and stoichiometric structures, of which 95% are, to date, classified as experimentally known. The geometries of structures with up to 64 atoms were then optimized using DFT with automated workflows and curated input protocols. The procedure was repeated for different functionals (and computational protocols), with the latest version (MC3D PBEsol-v2) comprising 32013 unique structures. All versions of the MC3D are made available on the Materials Cloud portal, which provides a graphical interface to explore and download the data. The database includes the full provenance graph of all the calculations driven by the automated workflows, thus establishing full reproducibility of the results and more-than-FAIR procedures.

This data entry includes the data with full provenance for all three versions: PBE-v1, PBEsol-v1 and PBEsol-v2.

en: MC3D: The Materials Cloud computational database of experimentally known stoichiometric inorganics

aiida

display_name: aiida

database

display_name: database

marvelhpc

display_name: marvelhpc

max

display_name: max

pasc

display_name: pasc

prace

display_name: prace


density-functional-theory

display_name: density-functional-theory


en: ZnO nanoparticle powders are an important and common starting material for functional devices that require the highest purity for electronic and optical applications. In this combined ab initio and experimental study, we use electron paramagnetic resonance (EPR) spectroscopy to detect shallow donor states in the ZnO lattice via their characteristic EPR resonance at g = 1.96. Using DFT calculations that account for the chemical environment and the high temperatures during the gas-phase synthesis in conjunction with control experiments with atomic hydrogen, we show that these paramagnetic defects correspond to neutral hydrogenated oxygen sites (OHO0). As a key implication of these findings, ZnO particle powders, although produced under pure gas-phase synthesis conditions and subsequently annealed in vacuum to 873 K, were found to be doped with hydrogen at a base concentration of 10-5 at% and higher. This, in turn, highlights the often-overlooked role of unwanted hydrogen in nanoscale ZnO as components for applications in optics, electronics, and sensing.

en: Ubiquitous shallow trap states and lattice hydrogenation of ZnO particles

defect

display_name: defect

hydrogen

display_name: hydrogen

zno

display_name: zno


en: Neural-network interatomic potentials (NNIPs) have transformed atomistic simulations, by enabling molecular dynamics simulations with near ab initio accuracy at reduced computational costs and improved scalability. Despite these advances, crafting NNIPs remains a complex task, demanding specialized expertise in both machine learning and electronic structure calculations. Here, we introduce an automated, open-source, and user-friendly workflow that streamlines the creation of accurate NNIPs. Our approach integrates density-functional theory with classical molecular dynamics to systematically explore the potential energy landscape using random distortions, strain, interfaces, neutral vacancies, clusters, and molecular dynamics trajectories at varied temperatures and pressures. We leverage active learning combined with on-the-fly calibration of committee disagreement against true errors to ensure reliability. The method is validated on the fully automated training of a NNIP for a diverse set of carbon allotropes, reaching state-of-the-art accuracy and data efficiency. This platform democratizes NNIP development, empowering users to achieve high-precision simulations with minimal human intervention.

en: Automated training of neural-network interatomic potentials

active-learning

display_name: active-learning

aiida-trainspot

display_name: aiida-trainspot

carbon-allotropes

display_name: carbon-allotropes

mace

display_name: mace

neural-network-interatomic-potentials

display_name: neural-network-interatomic-potentials

uncertainty-quantification

display_name: uncertainty-quantification


en:
A mathematical parameter is proposed to describe the p-p-s electronic coupling strength in transition metal dichalcogenides (TMDCs). Two key descriptors, Iband(p−s)Iband(p−s) and Cband(p−p′)Cband(p−p′), are defined as follows:

Iband(p−s)=ϵp−ϵsϵp,Cband(p−p′)=ϵp′−ϵpϵp′Iband(p−s)=ϵpϵp−ϵs,Cband(p−p′)=ϵp′ϵp′−ϵp

where ϵϵ represents the p-band or s-band center positions of the atoms. Here, pp, p′p′, and ss refer to the p-band of the doped anion atom, the p-band of the first neighboring six S/Se atoms around the doped anion atom, and the s-band of Li atoms, respectively. Iband(p−s)Iband(p−s) characterizes the energy gap between the doped TMDCs and lithium polysulfides (LiPS), where a smaller value indicates a reduced band center energy gap, leading to enhanced interfacial electron transfer dynamics and a stronger interaction between the doped TMDCs and LiPS. On the other hand, Cband(p−p′)Cband(p−p′) quantifies the coupling strength between the p-band of the doped anion atoms and the neighboring S/Se atoms, where a smaller value indicates stronger coupling.

The relationship between the Gibbs energy barrier (ΔGΔG) and the coupling descriptors Iband(p−s)Iband(p−s) and Cband(p−p′)Cband(p−p′) is established through a machine learning approach, represented as:

ΔGi=f(Iband(i)(p−s),Cband(i)(p−p′))ΔGi=f(Iband(i)(p−s),Cband(i)(p−p′))

The model demonstrates that Iband(p−s)Iband(p−s) and Cband(p−p′)Cband(p−p′) effectively capture the relationship between the p-p-s coupling strength and the Gibbs free energy barrier, with a fitting result achieving an average Root Mean Square Error (RMSE) of approximately 0.07 eV.

materialscloud

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

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

en: A machine-learning (ML) assisted approach revealing p-p-s electronic coupling descriptor

catalytic-activity

display_name: catalytic-activity

electronic-coupling

display_name: electronic-coupling

li-s-batteries

display_name: li-s-batteries

machine-learning-prediction

display_name: machine-learning-prediction


materialscloud

en: Materials Cloud


en: Due to their abundant use in all-solid-state lasers, nonlinear optical (NLO) crystals are needed for many applications across diverse fields such as medicine and communication. However, because of conflicting requirements, the design of suitable inorganic crystals with strong second-harmonic generation (SHG) has proven to be challenging to both experimentalists and computational scientists. In this work, we leverage a data-driven approach to accelerate the search for high-performance NLO materials. We construct an extensive pool of candidates using databases within the OPTIMADE federation and employ an active learning strategy to gather optimal data while iteratively improving a machine learning model. The result is a publicly accessible dataset of ∼2,200 computed SHG tensors using density-functional perturbation theory. We further assess the performance of machine learning models on SHG prediction and introduce a multi-fidelity correction-learning scheme to refine data accuracy. This study represents a significant step towards data-driven materials discovery in the NLO field and demonstrates how new materials can be screened in an automated fashion.

en: Accelerating the discovery of high-performance nonlinear optical materials using active learning and high-throughput screening

density-functional-perturbation-theory

display_name: density-functional-perturbation-theory

first-principles

display_name: first-principles

high-throughput

display_name: high-throughput

non-linear-optics

display_name: non-linear-optics

second-harmonic-generation

display_name: second-harmonic-generation


density-functional-theory

display_name: density-functional-theory


en: Machine-learning interatomic potentials (MLIPs) have greatly extended the reach of atomic-scale simulations, offering the accuracy of first-principles calculations at a fraction of the cost. Leveraging large quantum mechanical databases and expressive architectures, recent ''universal'' models deliver qualitative accuracy across the periodic table but are often biased toward low-energy configurations. We introduce PET-MAD, a generally applicable MLIP trained on a dataset combining stable inorganic and organic solids, systematically modified to enhance atomic diversity. Using a moderate but highly-consistent level of electronic-structure theory, we assess PET-MAD's accuracy on established benchmarks and advanced simulations of six materials. Despite the small training set and lightweight architecture, PET-MAD is competitive with state-of-the-art MLIPs for inorganic solids, while also being reliable for molecules, organic materials, and surfaces. It is stable and fast, enabling the near-quantitative study of thermal and quantum mechanical fluctuations, functional properties, and phase transitions out of the box. It can be efficiently fine-tuned to deliver full quantum mechanical accuracy with a minimal number of targeted calculations.

en: PET-MAD, a lightweight universal interatomic potential for advanced materials modeling

atomistic-modeling

display_name: atomistic-modeling

foundation-models

display_name: foundation-models

interatomic-potentials

display_name: interatomic-potentials

machine-learning

display_name: machine-learning


en:
Topological Weyl semimetals represent a novel class of quantum materials that exhibit remarkable properties arising from their unique electronic structure. In this work, we employ state-of-the-art ab initio methods to investigate the role of the electron-phonon interactions on the charge transport properties of TaAs. Our calculations of the temperature-dependent electrical conductivity with the iterative Boltzmann transport equation show excellent agreement with experimental measurements above 100 K. Extending the analysis to doped systems, we demonstrate that even small shifts in the Fermi level can lead to substantial changes in conductivity, driven by the complex topology of the Fermi surface. In particular, modifications in Fermi surface nesting emerge as a key factor influencing scattering processes and carrier lifetimes. These findings offer critical insights into the microscopic mechanisms that govern transport in TaAs and highlight the sensitivity of Weyl semimetals to doping and carrier dynamics.
This record includes (i) the code and input files used for the calculations, and (ii) the data and plotting scripts used to generate the figures in the paper.

en: Phonon-limited carrier transport in the Weyl semimetal TaAs

envi

en: Environment

electron-phonon-coupling

display_name: electron-phonon-coupling

transport

display_name: transport

weyl-semimetals

display_name: weyl-semimetals


en: The development of machine-learning models for atomic-scale simulations has benefitted tremendously from the large databases of materials and molecular properties computed in the past two decades using electronic-structure calculations. More recently, these databases have made it possible to train “universal” models that aim at making accurate predictions for arbitrary atomic geometries and compositions. The construction of many of these databases was however in itself aimed at materials discovery, and therefore targeted primarily to sample stable, or at least plausible, structures and to make the most accurate predictions for each compound – e.g. adjusting the calculation details to the material at hand. Here we introduce a dataset designed specifically to train models that can provide reasonable predictions for arbitrary structures, and that therefore follows a different philosophy. Starting from relatively small sets of stable structures, the dataset is built to contain “massive atomic diversity” (MAD) by aggressively distorting these configurations, with near-complete disregard for the stability of the resulting configurations. The electronic structure details, on the other hand, are chosen to maximize consistency rather than to obtain the most accurate prediction fora given structure, or to minimize computational effort. The MAD dataset we present here, despite containing fewer than 100k structures, has already been shown to enable training universal interatomic potentials that are competitive with models trained on traditional datasets with two to three orders of magnitude more structures. We describe in detail the philosophy and details of the construction of the MAD dataset. We also introduce a low-dimensional structural latent space that allows us to compare it with other popular datasets, and that can also be used as a general-purpose materials cartography tool.

en: Massive Atomic Diversity: a compact universal dataset for atomistic machine learning

atomistic-simulations

display_name: atomistic-simulations

dataset

display_name: dataset


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

gpu-acceleration

display_name: gpu-acceleration


en:
A molecular level understanding of surface chemistry involved in the focused electron beam induced deposition (FEBID) with metalorganic molecules is crucial for enhancing metal content in the nanostructures. Here we investigate the FEBID process of trimethyl(methylcyclopentadienyl)platinum(IV) [MeCpPtMe₃] using focused electron beam induced mass spectrometry (FEBiMS), a recently developed in situ analytical technique. A comparison with gas-phase electron impact fragmentation, along with density-functional-theoretical calculations and molecular dynamics simulations are presented. The results indicate that charged fragments generated via dissociative ionization exhibit strong adsorption to the substrate and lack sufficient kinetic energy to desorb, suggesting that the most observed charged species during FEBID originate from gasphase fragmentation above the surface. Furthermore, we propose processes like charge neutralization and dissociative recombination, mechanisms not previously considered in FEBID, could be significant contributors for increasing metal content in the resulting nanostructures.

The data is discussed in the study "C. S. Jureddy, J. Jurczyk, K. Mackosz, H. Lyschuk, J. Kocisek, P. Weber, M. Ernst, A. Verkhovtsev, A. V. Solov'yov, J. Fedor and I. Utke, Phys. Chem. Chem. Phys., 2025"

en: Desorption of fragments upon electron impact on adsorbates: implications for electron beam induced deposition

dft

display_name: dft

experimental

display_name: experimental

febid

display_name: febid

molecular-dynamics

display_name: molecular-dynamics

surface-and-gas-phase-processes

display_name: surface-and-gas-phase-processes


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

equation-of-state

display_name: equation-of-state

pseudopotentials

display_name: pseudopotentials

verification

display_name: verification


materialscloud

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

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


en: Magnetic materials are crucial for manipulating electron spin and magnetic fields, enabling applications in data storage, spintronics, charge transport, and energy conversion, while also providing insight into fundamental quantum phenomena. In numerous applications, the interaction between electrons and lattice vibrations, known as electron-phonon coupling, can be of significant importance. In that regard, we extend the EPW package to be able to interpolate the electron-phonon matrix elements combining perturbation theory and maximally localized Wannier functions. This advance allows to use dense momentum grids at a reasonable computational cost when computing electron-phonon-related quantities and physical properties. We validate our implementation considering ferromagnetic iron and nickel where we explore the absence of phonon-driven superconductivity, finding that superconductivity is intrinsically suppressed. Furthermore, we evaluate the carrier resistivity at finite temperatures for both systems, considering the role of magnetism in carrier transport. Our findings indicate that in the case of Fe, the primary contributor to resistivity is electron-phonon scattering. In contrast, for Ni, electron-phonon scattering constitutes less than one-third of the resistivity, underscoring a fundamental difference in the transport properties of the two systems.

en: Materials Cloud

en: Electron-phonon coupling in magnetic materials using the local spin density approximation

magnetism

display_name: magnetism

wannier-functions

display_name: wannier-functions


en: The existence of the hydroxyl ligand on a single- atom catalyst embedded in graphene, especially Fe(OH)−N4−C is suggested to improve the oxygen reduction reaction (ORR) from the pristine Fe−N4−C. However, the theoretical electrocatalytic activity of the ORR process is shown to be highly dependent on the electrolyte solution and the applied electrode potential. Herein, we performed the constant-potential simulation and microkinetic modeling to investigate the mechanism of ORR on well studied Fe(OH)−N4−C and Co(OH)−N4−C catalysts and compared to the pristine ones, using density functional theory (DFT) calculation combined with the effective screening medium method and the reference interaction site model (ESM-RISM). It was found that the Fe(OH)−N4−C and Co(OH)−N4−C have comparable ORR activities to Fe−N4−C and Co−N4−C, and the calculated limiting potentials and half-wave potentials agree with the experimental values, in contrast to the results obtained using the constant-charge method (i.e., under the neutral condition) regardless of consideration of the solvation effect.

en: Effect of the OH functionalization on the catalytic activity of single-atom catalyst

density-functional-theory

display_name: density-functional-theory

implicit-solvation-model

display_name: implicit-solvation-model

oxygen-reduction-reaction

display_name: oxygen-reduction-reaction

single-atom-catalyst

display_name: single-atom-catalyst


en: The inner structure of polymeric microgels critically influences their responsiveness and potential applications, yet remaining challenging to resolve at molecular resolution. In this work, a structural characterization of thermoresponsive copolymer microgels is provided by integrating small-angle neutron scattering (SANS), dynamic light scattering (DLS), and nuclear magnetic resonance (NMR) measurements with multi-scale simulations. Specifically, Poly(N-isopropylacrylamide-co-N-isopropylmethacrylamide), P(NIPAM-co-NIPMAM), copolymer microgels, in which a random monomer distribution is conventionally assumed, are considered. By synthesizing different samples, including isotopically labeled microgels via selective deuteration, the microgels swelling behavior is probed and distinct polymer-specific signatures are revealed. To elucidate their internal distribution, monomer-resolved microgel simulations are performed across different copolymer models. A direct comparison between experimental and numerical form factors provides evidence of preferential organization into block structures, challenging the prevailing view of random distribution. 13C-NMR experiments confirm NIPAM-rich blocks and atomistic simulations link this unexpected block-like architecture to distinct local hydrogen-bonding patterns. This integrated approach provides the first direct evidence of preferential block formation in P(NIPAM-co-NIPMAM) microgels. Beyond this system, these results establish a generalizable strategy for unveiling hidden structural order in copolymer microgels, offering new strategies to tailor their design and to enhance control of material responsivity.

en: Thermoresponsive copolymer microgels synthesized via single-step precipitation polymerization: random or block structure?

copolymerization

display_name: copolymerization

microgels

display_name: microgels

molecular-modeling

display_name: molecular-modeling

nmr-spectroscopy

display_name: nmr-spectroscopy

selective-neutron-scattering

display_name: selective-neutron-scattering


envi

en: Environment


electron-phonon-coupling

display_name: electron-phonon-coupling