en: Amorphous formulations are increasingly used in the pharmaceutical industry due to their increased solubility, but their structural characterization at atomic-level resolution remains extremely challenging. Here, we characterize the complete atomic-level structure of an amorphous glucagon-like peptide-1 receptor (GLP-1R) agonist using chemical shift driven NMR crystallography. The structure is determined from measured chemical shift distributions for 17 of the 32 carbon atoms and 16 of the 31 hydrogen atoms in the molecule. The chemical shifts are able to provide a detailed picture of the atomic-level conformations and interactions, and we identify the structural motifs that play a major role in stabilization of the amorphous form. In particular, hydrogen bonding of the carboxylic acid proton is strongly promoted although no carboxylic acid dimer is formed. Two orientations of the benzodioxole ring are promoted in the NMR structure, corresponding to a significant stabilization mechanism. Our observation that inclusion of water leads to stabilization of the carboxylic acid group might be used as a strategy in future formulations where hydrogen bonding between neighboring molecules may otherwise be hindered by sterics.

en: The three-dimensional atomic-level structure of an amorphous glucagon-like peptide-1 receptor agonist

envi

en: Environment

machine-learning

display_name: machine-learning

marvel

display_name: marvel

nmr

display_name: nmr

nmr-crystallography

display_name: nmr-crystallography


$oid: OBSOLETE

en: The potential of lead-halide perovskites for realistic applications is currently hindered by their limited long-term stability under functional activation. While the role of lattice flexibility in the thermal response of perovskites has become increasingly evident, the description of thermally-induced distortions is still unclear. In this work, we provide a unified picture of thermal activation in CsPbBr₃ across length scales, showing that lattice symmetry does not increase at high temperatures. We combine temperature-dependent XRD, Br K-edge XANES, ab initio MD simulations, and calculations of the XANES spectra by first-principles, accounting for both thermal fluctuations and core hole final state effects. We find that the octahedral tilting of the Pb-Br inorganic framework statistically adopts multiple local configurations over time - in the short-range. In turn, the stochastic nature of the local thermal fluctuations uplifts the longer-range periodic octahedral tilting characterizing the low temperature structure, with the statistical mean of the local configurations resulting in a cubic-like time-averaged lattice. These observations can be rationalized in terms of displacive thermal phase transitions through the soft mode model, in which the phonon anharmonicity of the flexible inorganic framework causes the excess free energy surface to change as a function of temperature. Our work demonstrates that the effect of thermal dynamics on the XANES spectra can be effectively described for largely anharmonic systems, provided ab initio MD simulations are performed to determine the dynamically fluctuating structures, and core hole final state effects are included in order to retrieve an accurate XANES line shape. Moreover, it shows that the soft mode model, previously invoked to describe displacive thermal phase transitions in oxide perovskites, carries a more general validity.

en: Atomic-level description of thermal fluctuations in inorganic lead halide perovskites

materialscloud

approval_status: approved

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

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

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

fr: Environnement

it: Ambiente

halide-perovskites

display_name: halide-perovskites

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

xas-simulation

display_name: xas-simulation


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

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

ab-initio-calculations

display_name: ab-initio-calculations

cuw

display_name: cuw

degradation-kinetics

display_name: degradation-kinetics

in-plane-strain

display_name: in-plane-strain

nano-multilayers

display_name: nano-multilayers

vacancy-driven-diffusion

display_name: vacancy-driven-diffusion


en: Materials are often represented in machine learning applications by (chemical-)geometric descriptions of their atomic structure. In this work, we propose an alternative framework for representing materials using descriptions of their electronic structure called Spectral Operator Representations (SOREPs). This record contains the code and data used to study carbon nanotubes (CNTs), barium titanate polymorphs, and the accelerated screening of transparent conducting materials with SOREPs. A data set for each application is provided: pz tight binding band structures for the three CNT configurations studied; the structures, band dispersions, and SOREP features of 127 BaTiO₃ polymorphs; and the SOREP features and ML targets for the MC3D materials considered in the accelerated screening. Additionally, code including patch files for Quantum ESPRESSO, the "sorep" python package, and the set of scripts used to prepare these data, train ML models, and plot results is provided.

en: Spectral operator representations

electronic-bands

display_name: electronic-bands

electronic-structure

display_name: electronic-structure

epfl

display_name: epfl


en: The performance of Ni-based oxygen evolution reaction (OER) electrocatalysts is enhanced upon Fe incorporation into the structure or Fe uptake from the electrolyte. In light of the promising potential of metal-organic framework (MOF) electrocatalysts for water splitting, Ni-MOF-74 is used as a model catalyst to study the effect of Fe incorporation from KOH electrolyte on the electrocatalyst's OER activity and stability. The insights obtained from X-ray diffraction and operando X-ray absorption spectroscopy characterizations of Ni-MOF-74 and an amorphous Ni metal organic compound (Ni-MOC*)  reveal that Fe uptake enhances OER by two processes: higher Ni oxidation states and enhanced flexibility of the electronic state and local structure when cycling the potential below and above the OER onset. To demonstrate the impressive OER activity and stability in Fe containing KOH, an Ni-MOC* anode was implemented in an anion exchange membrane water electrolyzer (AEM-WE) with 3 ppm Fe containing 1 M KOH electrolyte resulting in an outstanding cell voltage of 1.7 V (at an anode potential of 1.51 V) at 60 °C and 0.5 A cm-2 exceeding 130 h of stable continuous operation.

en: Revising known concepts for novel applications: Fe incorporation into Ni-MOF-74 derived oxygen evolution electrocatalysts for anion exchange membrane water electrolysis

alkalineaem-water-electrolysis

display_name: alkalineaem-water-electrolysis

electrocatalysis

display_name: electrocatalysis

fe-effect-on-ni-based-oer-catalysts

display_name: fe-effect-on-ni-based-oer-catalysts

oer

display_name: oer

operando-xasxrd

display_name: operando-xasxrd


delta-project-archive-of-old-website

en: The widespread popularity of density functional theory has given rise to an extensive range of dedicated codes for predicting molecular and crystalline properties. However, each code implements the formalism in a different way, raising questions about the reproducibility of such predictions. In K. Lejaeghere et al., Science 351 (6280), aad3000 (2016) (https://doi.org/10.1126/science.aad3000), we reported the results of a community-wide effort that compared 15 solid-state codes, using 40 different potentials or basis set types, to assess the quality of the Perdew-Burke-Ernzerhof equations of state for 71 elemental crystals. We conclude that predictions from recent codes and pseudopotentials agree very well, with pairwise differences that are comparable to those between different high-precision experiments. Older methods, however, have less precise agreement. Our benchmark provides a framework for users and developers to document the precision of new applications and methodological improvements. The underlying data of this paper were accessible during 2016-2023 at https://molmod.ugent.be/deltacodesdft. Several additions that made use of the same protocol were added later. As this website is obsolete and to ensure long-term continued access, these data are stored now under the present record of the Materials Cloud Archive.

en: Delta project — archive of old website

title_for_slug: delta-project-archive-of-old-website

delta-project

display_name: delta-project

dft-benchmarking

display_name: dft-benchmarking

precision

display_name: precision

unary-crystals

display_name: unary-crystals

verification

display_name: verification


en: A major challenge in first-principles high-throughput materials simulations is automating the selection of parameters used by simulation codes in a way that robustly ensures numerical precision and computational efficiency. Here, we propose a rigorous methodology to assess the quality of self-consistent DFT calculations with respect to smearing and k-point sampling across a wide range of crystalline materials. To achieve this, we develop criteria to reliably control average errors in total energies, forces, and other properties as a function of the desired computational efficiency, while consistently suppressing uncontrollable k-point sampling errors. Our results provide automated protocols for selecting optimized parameters based on different precision and efficiency tradeoffs. This archive contains all data related to the material structures and calculation workflows developed in this work.

en: Accurate and efficient protocols for high-throughput first-principles materials simulations

high-throughput-workflows

display_name: high-throughput-workflows

marvel

display_name: marvel

protocols

display_name: protocols

quantum-espresso

display_name: quantum-espresso


en: Antiferromagnetic Heisenberg chains exhibit two distinct types of excitation spectra: gapped for integer-spin chains and gapless for half-integer-spin chains. However, in finite-length half-integer-spin chains, quantization induces a gap, requiring precise control over sufficiently long chains to study its evolution. In a recent publication, we created length-controlled spin-1/2 Heisenberg chains by covalently linking olympicenes—Olympic ring-shaped magnetic nanographenes. With large exchange interactions, tunable lengths, and negligible magnetic anisotropy, this system is ideal for investigating length-dependent spin excitations, probed via inelastic electron tunneling spectroscopy. We observe a power-law decay of the lowest excitation energy with length L, following a 1/L dependence in the large-L regime, consistent with theory. For L=50, a V-shaped excitation continuum confirms gapless behavior in the thermodynamic limit. Additionally, low-bias current maps reveal the standing wave of a single spinon in odd-numbered chains. Our findings provide evidence for the realization of a one-dimensional analog of a gapless spin liquid within an artificial graphene lattice. This record includes all the data that support the results discussed in the publication.

en: Spin excitations in nanographene-based antiferromagnetic spin-½  Heisenberg chains

cscs

display_name: cscs

dft

display_name: dft

fractional-excitation

display_name: fractional-excitation

spin-excitation

display_name: spin-excitation

spin-liquid

display_name: spin-liquid

spinon

display_name: spinon


en:
The discovery of chiral helical magnetism (CHM) in Cr1/3NbS2 and the stabilization of a chiral soliton lattice (CSL) has attracted considerable interest in view of their potential technological applications. However, there is an ongoing debate regarding whether the sister compound, Mn1/3NbS2, which shares the same crystal structure, exhibits similar nontrivial properties, which rely on the stabilization of the lack of inversion symmetry at the magnetic ion. In this study, we conduct a comprehensive investigation of the magnetically ordered states of both compounds, using 53Cr, 55Mn and 93Nb nuclear magnetic resonance.  Our results, supported by density functional  calculations, detect in a high quality single crystal of Cr1/3NbS2 all the signatures of the monoaxial CHM in  a magnetic field, identifying it as a textbook NMR case. The detailed understanding of this prototypic  behavior provides a reference for Mn1/3NbS2.
Despite the much larger density of specific defects in this second single crystal, we confirm the presence of a CHM phase in the Mn compound, characterized by a very large critical field for the forced ferromagnetic phase (~ 10 T for H || c).

en: Disorder-resilient transition of Helical to Conical ground states in M1/3NbS2, M=Cr,Mn

envi

en: Environment

chiral-helical-magnetism

display_name: chiral-helical-magnetism

chiral-soliton-lattice

display_name: chiral-soliton-lattice

nuclear-magnetic-resonance

display_name: nuclear-magnetic-resonance


en: Deuterium boride (DB) nanosheets were synthesized through ion exchange, and their Fourier-transform infrared absorption spectra showed isotope effects with a shift in the B-H stretching mode. Temperature-programmed desorption (TPD) experiments indicated that hydrogen release from DB and HB nanosheets primarily results from inter-layer hydrogen recombination at lower temperatures, with intra-layer recombination occurring at higher temperatures. This repository contains supplementary data related to the computational findings presented in the accompanying publication. The goal of sharing these data files is to enhance result transparency and support better reproducibility.

en: Inter-layer hydrogen recombination from hydrogen boride nanosheets elucidated by isotope labeling

materialscloud

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

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

density-functional-theory

display_name: density-functional-theory

deuterium-boride

display_name: deuterium-boride

hydrogen-boride

display_name: hydrogen-boride

hydrogen-carrier

display_name: hydrogen-carrier

hydrogen-release

display_name: hydrogen-release

inter-layer-hydrogen-recombination

display_name: inter-layer-hydrogen-recombination

temperature-programmed-desorption

display_name: temperature-programmed-desorption


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

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

materialscloud

en: Materials Cloud

surface-properties

display_name: surface-properties

topological-materials

display_name: topological-materials

weyl-semimetal

display_name: weyl-semimetal


en: Twisted van der Waals heterostructures have recently emerged as a versatile platform for engineering interaction-driven, topological phenomena with a high degree of control and tunability. Since the initial discovery of correlated phases in twisted bilayer graphene, a wide range of moiré materials have emerged with fascinating electronic properties. While the field of twistronics has rapidly evolved and now includes a range of multi-layered systems, moiré systems comprised of double trilayer graphene remain elusive. Here, we report electrical transport measurements combined with tight-binding calculations in twisted double trilayer graphene (TDTLG). We demonstrate that small-angle TDTLG (~1.7−2.0ᵒ) exhibits an intrinsic bandgap at the charge neutrality point. Moreover, by tuning the displacement field, we observe a continuous insulator-semimetal-insulator transition at the CNP, which is also captured by tight-binding calculations. These results establish TDTLG systems as a highly tunable platform for further exploration of magneto-transport and optoelectronic properties.

en: Electric field tunable bandgap in twisted double trilayer graphene

experimental

display_name: experimental

marvel

display_name: marvel

tight-binding

display_name: tight-binding

twisted-multilayer-graphene

display_name: twisted-multilayer-graphene

wanniertools

display_name: wanniertools


en: Kagome metals have emerged as pivotal materials in condensed matter physics due to their unique geometric arrangement and intriguing electronic properties. Understanding the origin of magnetism in these materials, particularly in iron rich Fe-Sn binary compounds like Fe₃Sn, holds a significant importance, as they represent potential candidates for permanent magnets with a high Curie temperature and a strong magnetic anisotropy. In the present study, we employ density-functional theory and dynamical mean-field theory to analyze the electronic structure and magnetic properties of Fe₃Sn. Our investigation reveals the presence of several nearly-flat bands and Weyl nodes at low excitation energies. The inclusion of local correlation effects is shown to push these features even closer to the Fermi energy, which may be important for their manipulation via external stimuli. Regarding magnetism, the Hubbard-like interaction leads to an increase of orbital polarization at the expenses of a minor reduction of the spin moment. The magnetic anisotropy energy exhibits a strong dependence on the particular choice of the Coulomb interaction parameters. Additionally, our detailed analysis of the interatomic exchange interactions indicates a significant contribution from the antisymmetric exchange, i.e. the Dzyaloshinskii-Moriya interaction, which showcases the existence of magnetic chirality in the system. Overall, our investigation highlights a strong interplay between the flat bands near the Fermi level, the local Coulomb interaction and the triangular geometry of the lattice, which plays a crucial role in driving the magnetic properties of this material.

en: Exploring strong electronic correlations in the breathing kagome metal Fe₃Sn

dftdmft

display_name: dftdmft

dzyaloshinskii-moriya-interaction-dmi

display_name: dzyaloshinskii-moriya-interaction-dmi

electronic-structure

display_name: electronic-structure

flat-band

display_name: flat-band

kagome-metal

display_name: kagome-metal

magnetism

display_name: magnetism


en: Density-functional theory with extended Hubbard functionals (DFT+U+V) provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements. It does so by mitigating self-interaction errors inherent to semi-local functionals which are particularly pronounced in systems with partially-filled d and f electronic states. However, achieving accuracy in this approach hinges upon the accurate determination of the on-site U and inter-site V Hubbard parameters. In practice, these are obtained either by semi-empirical tuning, requiring prior knowledge, or, more correctly, by using predictive but expensive first-principles calculations.  This archive entry contains Hubbard parameters, occupation matrices and other data calculated for 28 materials and covers all steps in a self-consistent procedure where, at each step new Hubbard parameters are obtained via linear-response, a process that is repeated until the parameters no longer change. The primary purpose of this dataset is to support the development and validation of machine learning models that can be used to predict Hubbard parameters, sidestepping the need for expensive ab-initio density functional perturbation theory calculations.

en: Dataset of self-consistent Hubbard parameters for Ni, Mn and Fe from linear-response

density-functional-theory

display_name: density-functional-theory

hubbard-corrections

display_name: hubbard-corrections

machine-learning

display_name: machine-learning


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

en: Teaching oxidation states to neural networks

dftuv

display_name: dftuv

max

display_name: max

oxidation-states

display_name: oxidation-states


en: Band-to-band tunnelling field-effect transistors (TFETs) have long been considered as promising candidates for future low-power logic applications. However, fabricated TFETs rarely reach sub-60 mV/dec sub-threshold swings (SS) at room temperature. Previous theoretical studies identified Auger processes as possible mechanisms for the observed degradation of SS. Through first-principles quantum transport simulations incorporating carrier-carrier interactions within the Non-equilibrium Green's Function formalism through self-consistent GW approximation, we confirm here that Auger processes are indeed at least partly responsible for the poor performance of TFETs. Using a carbon nanotube TFET as testbed, we show that carrier-carrier scattering alone significantly increases the OFF-state current of these devices, thus worsening their sub-threshold behavior. The results are in the folder uploaded.

en: Influence of carrier-carrier interactions on the sub-threshold swing of band-to-band tunnelling transistors

transport

display_name: transport

wannier-functions

display_name: wannier-functions


en: The dataset uploaded here records atomic positions, lattices vectors and Python scripts for tight-binding calculations related to the paper entitled "Modulated Dirac bands and integer hopping ratios in a honeycomb lattice of phenalenyl-tessellation molecules." In a family of nanographene called phenalenyl-tessellation molecules (PTMs), two types of zero modes appear: one is uniformly extended over the entire molecule and the other is localized around vacancies. Therefore, it is expected that energy bands reflecting the properties of these two types of zero modes will appear in a honeycomb PTM (H-PTM). In this study, we show that modulated energy gap and the Fermi velocity of Dirac bands appear in H-PTMs and that the effective two-site model has positive integer hopping ratios based on the uniformly extended zero mode and  the number of connections between PTMs. Furthermore, we find that the localized zero modes around vacancies can coexist with the modulated Dirac bands. The tight-binding calculations confirm that modulated Dirac bands with integer hopping ratios and coexisting localized zero modes that are consistent with analytical solutions for H-PTMs.

en: Tight-binding calculation files of modulated Dirac bands and integer hopping ratios in a honeycomb lattice of phenalenyl-tessellation molecules

envi

en: Environment

band-gap

display_name: band-gap

flat-bands

display_name: flat-bands

graphene

display_name: graphene

honeycomb-lattice

display_name: honeycomb-lattice

tight-binding-model

display_name: tight-binding-model


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

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

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).

aluminium

display_name: aluminium

interaction-potential

display_name: interaction-potential

nickel

display_name: nickel


en: We use spin dynamics simulations to determine the origin of the unusual correlated diffuse scattering, characterised by half-moon shapes bridging the magnetic Bragg peaks, observed in the polarised elastic neutron scattering from manganese tungstate, MnWO₄. We first fit a Heisenberg Hamiltonian with twelve nearest-neighbour exchange interactions and single-ion anisotropy to the experimental ground-state magnon dispersion. We then show via spin dynamics simulations that our model Hamiltonian both reproduces the experimentally observed half-moon features and captures their persistence into the paramagnetic regime. Moreover, we identify the three-dimensional, competing antiferromagnetic interactions driving this behavior. Our work complements earlier studies of half-moon-shaped signatures in pyrochlore and triangular structures, by providing insight into their origin in a zigzag chain geometry with three-dimensional competing exchange interactions.

en: Paramagnetic half-moon shaped diffuse scattering arising from 3D magnetic frustration

materialscloud

en: Materials Cloud

diffuse-scattering

display_name: diffuse-scattering

erc

display_name: erc

horizon-europe

display_name: horizon-europe

spin-dynamics

display_name: spin-dynamics

uppasd

display_name: uppasd


en: X-ray absorption spectroscopy (XAS) is a powerful characterization technique for probing the local chemical environment of absorbing atoms. However, analyzing XAS data presents with significant challenges, often requiring extensive, computationally intensive simulations, as well as significant domain expertise. These limitations hinder the development of fast, robust XAS analysis pipelines that are essential in high-throughput studies and for autonomous experimentation. We address these challenges with a suite of transfer learning approaches for XAS prediction, each uniquely contributing to improved accuracy and efficiency, as demonstrated on simulated K-edge X-ray absorption near-edge structure (XANES) spectra database covering eight 3d transition metals (Ti-Cu). This database contains FEFF and VASP K-edge XANES spectra of 3d transition metal-containing oxide materials used to develop the above machine learning models. The materials structures are sourced from the Materials Project, corresponding to primary, secondary, ternary and quaternary oxide materials. Specifically, FEFF9 was used to compute the spectra of Ti, V, Cr, Mn, Fe, Co, Ni and Cu compounds; VASP 6.2.1 was used to compute the spectra of Ti and Cu compounds.

en: Simulated 3d transition metal oxides cation K-edge XANES dataset

materials

display_name: materials

vasp

display_name: vasp

x-ray-absorption-spectroscopy

display_name: x-ray-absorption-spectroscopy


en: Spin excitations play a fundamental role in understanding magnetic properties of materials, and have significant technological implications for magnonic devices. However, accurately modeling these in transition-metal and rare-earth compounds remains a formidable challenge. Here, we present a fully first-principles approach for calculating spin-wave spectra based on time-dependent (TD) density-functional perturbation theory (DFPT), using nonempirical Hubbard functionals. This approach is implemented in a general noncollinear formulation, enabling the study of magnons in both collinear and noncollinear magnetic systems. Unlike methods that rely on empirical Hubbard U parameters to describe the ground state, and Heisenberg Hamiltonians for describing magnetic excitations, the methodology developed here probes directly the dynamical spin susceptibility (efficiently evaluated with TDDFPT throught the Liouville-Lanczos approach), and treats the linear variation of the Hubbard augmentation (in itself calculated non-empirically) in full at a self-consistent level. Furthermore, the method satisfies the Goldstone condition without requiring empirical rescaling of the exchange-correlation kernel or explicit enforcement of sum rules, in contrast to existing state-of-the-art techniques. We benchmark the novel computational scheme on prototypical transition-metal monoxides NiO and MnO, showing remarkable agreement with experiments and highlighting the fundamental role of these newly implemented Hubbard corrections. The method holds great promise for describing collective spin excitations in complex materials containing localized electronic states.

en: Magnons from time-dependent density-functional perturbation theory and nonempirical Hubbard functionals

first-principles

display_name: first-principles

hubbard-functionals

display_name: hubbard-functionals

magnetism

display_name: magnetism

marvel

display_name: marvel


en:
Quantifying the uncertainty of regression models is essential to ensure their reliability, particularly since their application often extends beyond their training domain. Based on the solution of a constrained optimization problem, this work proposes 'prediction rigidities' as a formalism to obtain uncertainties of arbitrary pre-trained regressors. A clear connection between the suggested framework and Bayesian inference is established, and a last-layer approximation is developed and rigorously justified to enable the application of the method to neural networks. This extension affords cheap uncertainties without any modification to the neural network itself or its training procedure. The effectiveness of this approach is shown for a wide range of regression tasks, ranging from simple toy models to applications in chemistry and meteorology.
This record includes computational experiments supporting the MLST paper titled "A prediction rigidity formalism for low-cost uncertainties in trained neural networks".

confidence-interval

display_name: confidence-interval

laplace-approximation

display_name: laplace-approximation

machine-learning

display_name: machine-learning

neural-networks

display_name: neural-networks

neural-tangent-kernel

display_name: neural-tangent-kernel

prediction-rigidity

display_name: prediction-rigidity

regressors

display_name: regressors

uncertainty-quantification

display_name: uncertainty-quantification


en: An atomic-level understanding of radiation-induced damage in simple polymers like polyethylene is essential for determining how these chemical changes can alter the physical and mechanical properties of important technological materials such as plastics. We performed ensembles of quantum simulations of radiation damage in a polyethylene analog using the Density Functional Tight Binding method to help bind its radiolysis and subsequent degradation as a function of radiation dose. Chemical degradation products are categorized with a graph theory approach, and we compute occurrence rates of unsaturated carbon bond formation, crosslinking, cycle formation, chain scission reactions, and out-gassing products. Statistical correlations between product pairs show significant correlations between chain scission reactions, unsaturated carbon bond formation, and out-gassing products, though these correlations decrease with increasing atom recoil energy. Our results present relatively simple chemical descriptors as possible indications of network rearrangements in the middle range of excitation energies. The data set contains how DFTB+ simulations were setup and running isomorphic analysis and graphs to obtain chemical structures.

en: Quantum simulations of radiation damage in a molecular polyethylene analog

dftb

display_name: dftb

graph-theory

display_name: graph-theory

isomorphic-analysis

display_name: isomorphic-analysis

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation


en: The effect of dimensionality reduction on the crystallization kinetics of phase change materials is of relevance for the operation of ultrascaled memory devices. Therefore, the crystallization of amorphous nanoparticles (NPs) of the prototypical phase change compounds, GeTe and Ge₂Sb₂Te₅, has been addressed by several experimental works in recent years. In this work, we performed molecular dynamics simulations of the crystallization process of amorphous GeTe NPs with diameter in the range 3-6 nm (512-4096 atoms) by exploiting a machine-learned interatomic potential. We saw a few crystal nucleation events in the larger NPs but no crystallization in the smallest NP, 3 nm in diameter, in simulations lasting up to 80 ns in the temperature range 500-750 K. The analysis of the crystallization kinetics suggests that the nucleation rate per volume decreases with the NP size to an extent that prevents us from seeing crystallization in the smallest NP on our simulation time scale. This result is consistent with the large raise in crystallization temperature observed experimentally for NPs with diameters shorter than 3.5 nm.

en: Atomistic simulations of the crystallization of amorphous GeTe nanoparticles

crystallization

display_name: crystallization

gete

display_name: gete

neural-network-potential

display_name: neural-network-potential

neuromophic-computing

display_name: neuromophic-computing

phase-change-materials

display_name: phase-change-materials


en:
A database of 600 atomistic models of reduced graphene oxide (rGO) and hydrogenated rGO (H-rGO) was constructed, comprising 120 rGO and 480 H-rGO structures. The dataset spans a broad range of oxygen concentrations, –O–:–OH (epoxy/ether to hydroxyl) ratios, and hydrogenation levels. The database is designed for applications in computational modeling, machine learning, and structure–property analyses. The models were generated following a three-step simulation protocol:
 – Step 1 | Generation of Pseudo-GO Models: Graphene sheets were functionalized with oxygen groups (–O– or –OH), randomly distributed according to three –O–:–OH ratios (25:75, 50:50, and 75:25). Functionalization was applied symmetrically on both sides of the sheet, avoiding adjacent carbon atoms.
 – Step 2 | Thermal Reduction: Each pseudo-GO structure was subjected to annealing at four different temperatures (1000, 1500, 2000, and 2500 K). After initial relaxation at 300 K, the models were heated, equilibrated, annealed back to room temperature, and re-optimized. This step produced 120 rGO models exhibiting a range of oxygen contents, functional group distributions, and defect patterns.
 – Step 3 | Hydrogenation of rGO Models: rGO models were exposed to atomic hydrogen using a combined Grand Canonical Monte Carlo and molecular dynamics (GCMC/MD) scheme at 300 K and four hydrogen pressures (1, 2, 4, and 8 bar). Hydrogenation was performed with 50 GCMC exchange steps every 1 ps of MD, followed by relaxation and removal of unstable species. This step generated 480 H-rGO models with varying hydrogen coverage and bonding configurations.
All simulations were performed using the LAMMPS software package with the ReaxFF reactive force field.

en: Reduced graphene oxide membrane models for hydrogen storage

functionalized-carbon-nanostructures

display_name: functionalized-carbon-nanostructures

graphene-oxide-models

display_name: graphene-oxide-models

hydrogenated-graphene-oxide

display_name: hydrogenated-graphene-oxide

reduced-graphene-oxide

display_name: reduced-graphene-oxide


en: Cement hydration is crucial for the strength development of cement-based materials; however, the mechanism that underlies this complex reaction remains poorly understood at the molecular level. An in-depth understanding of cement hydration is required for the development of environmentally friendly cement and consequently the reduction of carbon emissions in the cement industry. Here, we use molecular dynamics simulations with a reactive force field to investigate the initial hydration processes of tricalcium silicate (C₃S) and dicalcium silicate (C₂S) up to 40 ns. Our simulations provide theoretical support for the rapid initial hydration of C₃S compared to C₂S at the molecular level. The dissolution pathways of calcium ions in C₃S and C₂S are revealed, showing that, two dissolution processes are required for the complete dissolution of calcium ions in C₃S. Our findings promote the understanding of the calcium dissolution stage and serve as a valuable reference for the investigation of the initial cement hydration.

en: The initial stages of cement hydration at the molecular level

envi

en: Environment

cement-hydration

display_name: cement-hydration

reaxff-reactive-force-field

display_name: reaxff-reactive-force-field


en:
We investigate popular resampling methods for estimating the uncertainty of statistical models, such as subsampling, bootstrap and the jackknife, and their performance in high-dimensional supervised regression tasks. We provide a tight asymptotic description of the biases and variances estimated by these methods in the context of generalized linear models, such as ridge and logistic regression, taking the limit where the number of samples n and dimension d of the covariates grow at a comparable fixed rate α = n/d. Our findings are three-fold: i) resampling methods are fraught with problems in high dimensions and exhibit the double-descent-like behavior typical of these situations; ii) only when α is large enough do they provide consistent and reliable error estimations (we give convergence rates); iii) in the over-parametrized regime α < 1 relevant to modern machine learning practice, their predictions are not consistent, even with optimal regularization.
This record provides the code to reproduce the numerical experiments of the related paper "Analysis of bootstrap and subsampling in high-dimensional regularized regression".

en: Analysis of bootstrap and subsampling in high-dimensional regularized regression (code)

materialscloud

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

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

neural-networks

display_name: neural-networks

numerical-simulation

display_name: numerical-simulation


en: Topological Weyl semimetals represent a novel class of non-trivial materials, where band crossings with linear dispersions take place at generic momenta across reciprocal space. These crossings give rise to low-energy properties akin to those of Weyl fermions, and are responsible for several exotic phenomena. Up to this day, only a handful of Weyl semimetals have been discovered, and the search for new ones remains a very active area. The main challenge on the computational side arises from the fact that many of the tools used to identify the topological class of a material do not provide a complete picture in the case of Weyl semimetals. In this work, we propose an alternative and inexpensive, criterion to screen for possible Weyl fermions, based on the analysis of the band structure along high-symmetry directions in the absence of spin-orbit coupling. We test the method by running a high-throughput screening on a set of 6000 inorganic bulk materials and identify 49 possible candidates for topological properties. A further analysis, carried out by identifying and characterizing the crossings in the Brillouin zone, shows us that 3 of these candidates are Weyl semimetals. Interestingly, while these 3 materials underwent other high-throughput screenings, none had revealed their topological behavior before.

en: High-throughput screening of Weyl semimetals

materialscloud

en: Materials Cloud

high-throughput

display_name: high-throughput

topological-materials

display_name: topological-materials

weyl-semimetal

display_name: weyl-semimetal


en: Metal-organic frameworks (MOFs) as electrocatalysts for the alkaline oxygen evolution reaction (OER) show promising catalytic activity by offering great variability and high surface areas, enabling performance optimization and mechanistic studies. However, their stability during reaction and the structure-performance relationship defining the origin of the high OER activity, are still vigorously debated. Herein, we leverage operando X-ray absorption spectroscopy and operando X-ray diffraction to unveil the structural and electronic transformations of Ni-MOF-74 during OER. We identify the irreversible destruction of the MOF-74 crystal into a highly OER active, amorphous NiOOH-metal organic compound. Based on these findings, we propose an amorphous Ni metal organic compound (Ni-MOC*) for achieving high current densities both in a three-electrode cell (14 A gNi-1 at 1.5 VRHE) and in an anion exchange membrane water electrolyzer (AEM-WE) with a stable AEM-WE performance exceeding 100 h at 500 mA cm-2.

en: From operando investigations to implementation of Ni-MOF-74 oxygen evolution electrocatalysts

alkalineaem-water-electrolysis

display_name: alkalineaem-water-electrolysis

electrocatalysis

display_name: electrocatalysis

marvel

display_name: marvel

oer

display_name: oer

operando-xasxrd

display_name: operando-xasxrd


en:
We reassess the modeling of amorphous silica bilayers as a two-dimensional classical system whose particles interact with an effective pairwise potential. We show that it is possible to reparameterize the potential developed by Roy, Heyde, and Heuer to quantitatively match the structural details of the experimental samples. We then study the glassy dynamics of the reparameterized model at low temperatures.  Using appropriate cage-relative correlation functions, which suppress the effect of Mermin-Wagner fluctuations, we highlight the presence of two well-defined Arrhenius regimes separated by a narrow crossover region, which we connect to the thermodynamic anomalies and the changes in the local structure. We find that the bond-orientational order grows steadily below the crossover temperature and is associated to transient crystalline domains of nanometric size.  These findings raise fundamental questions about the nature of glass structure in two dimensions and provide guidelines to interpret the experimental data.

This dataset is associated with "Glassy dynamics and crystalline local order in two-dimensional amorphous silica", Marco Dirindin and Daniele Coslovich (2024) [arXiv:2410.10500].
It includes scripts and data files to allow for the replication of the figures. The EPS figures in the Figures folder were generated using gnuplot version 5.4.

glass-transition

display_name: glass-transition

silica-bilayer

display_name: silica-bilayer


en: Covalent organic frameworks (COFs) are materials of growing interest for electronic applications due to their tunable structures, chemical stability, and layered architectures that support extended π-systems and directional charge transport. While their electronic properties are strongly influenced by the choice of molecular building blocks and the stacking arrangement, experimental control over these features remains limited, and the number of well-characterized COFs is still relatively small. Here, we explore two alternative strategies, hydrostatic pressure and metal intercalation, to tune the electronic structure of COFs. Using periodic density functional theory (DFT) calculations, we show that the band gap of pristine COF-1 decreases by ∼1 eV under compression up to 10 GPa. Metal intercalation induces an even greater reduction, in some cases leading to metallic behavior. We demonstrate that pressure and intercalation offer effective, continuous control over COF electronic properties, providing powerful means to complement and extend conventional design approaches.

en: Extensive band gap tunability in covalent organic frameworks via metal intercalation and high pressure

band-gap

display_name: band-gap

covalent-organic-frameworks

display_name: covalent-organic-frameworks

dft

display_name: dft

electronic-bands

display_name: electronic-bands

high-pressure

display_name: high-pressure

intercalation

display_name: intercalation


en: The dataset contains records of Charpy V-notch impact tests of nuclear structural materials. The focus is on studying the influence of specimen dimensions and geometry on Charpy impact test properties. The dataset was created through an extensive literature review of scientific articles. The extracted data points from the literature review are organized into a tabular format database containing 4,961 test records with 55 parameters, including material type and composition, manufacturing information, irradiation conditions, specimen size and dimensions, and Charpy properties. Materials science experts conducted systematic validation checks to ensure the accuracy of the information related to material type, manufacturing processes, treatment methods, chemical composition, testing conditions, as well as other pertinent information.

en: Dataset of Charpy V-notch impact test records of nuclear structural materials

charpy-v-notch-impact-test

display_name: charpy-v-notch-impact-test

miniature-specimens

display_name: miniature-specimens

nuclear-structural-materials

display_name: nuclear-structural-materials

reactor-pressure-vessel-steels

display_name: reactor-pressure-vessel-steels

specimen-size-effect

display_name: specimen-size-effect

sub-sized-specimens

display_name: sub-sized-specimens


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

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

tensile-properties

display_name: tensile-properties


en: This dataset contains the results of density functional theory (DFT) calculations performed using Quantum ESPRESSO to study surface binding energies (SBE) and the formation energies of interstitial hydrogen (H-IFE) in various plasma-facing materials (PFMs). These calculations support the findings reported in the article Comprehensive Screening of Plasma-Facing Materials for Nuclear Fusion, where a combination of peer-reviewed data from the PAULING FILE database and first-principles calculations are used to evaluate potential PFM candidates. Key results include a detailed comparison of tungsten and alternative refractory materials, focusing on their behavior under intense neutron bombardment and plasma interactions in nuclear fusion reactors. The dataset includes input and output files from the Quantum ESPRESSO simulations, offering valuable insight into defect energetics in candidate materials.

en: DFT calculations of surface binding and interstitial hydrogen formation energies for plasma-facing materials

eurofusion

display_name: eurofusion

interstitial-formation-energy

display_name: interstitial-formation-energy

marvel

display_name: marvel

nuclear-fusion

display_name: nuclear-fusion

plasma-facing-materials

display_name: plasma-facing-materials

surface-binding-energy

display_name: surface-binding-energy


en: We determine the complete atomic-level structure of the amorphous form of the drug atuliflapon, a 5-lipooxygenase activating protein (FLAP) inhibitor, via chemical-shift-driven NMR crystallography. The ensemble of preferred structures allows us to identify a number of specific conformations and interactions that stabilize the amorphous structure. These include preferred hydrogen-bonding motifs with water and with other drug molecules, as well as conformations of the cyclohexane and pyrazole rings that stabilize structure by indirectly allowing for optimization of hydrogen bonding.

en: Atomic-level structure of the amorphous drug Atuliflapon via NMR crystallography

envi

en: Environment

atuliflapon

display_name: atuliflapon

nmr-crystallography

display_name: nmr-crystallography

solid-state-nmr

display_name: solid-state-nmr


en: Predicting the ground state spin of transition metal complexes is a challenging task. Previous attempts have been focused on specific regions of chemical space, whereas a more general automated approach is required to process crystallographic structures for high-throughput quantum chemistry computations. In this work, we developed a method to predict ground state spins of transition metal complexes. We started by constructing a dataset which contains 2,063 first row transition metal complexes taken from experimental crystal structures and their computed ground state spins. This dataset showed large chemical diversity in terms of metals, metal oxidation states, coordination geometries, and ligands. Then, we analyzed the trends between structural and electronic features of the complexes and their ground state spins, and put forward an empirical spin state assignment model. We also used simple descriptors to build a statistical model with >95% predictive accuracy across the board. With this, we provide a practical and automated way to determine the ground state spin of transition metal complex from structure, enabling the high-throughput exploration of crystallographic repositories.

en: Automated prediction of ground state spin for transition metal complexes

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).

machine-learning

display_name: machine-learning

spin-state

display_name: spin-state

transition-metal

display_name: transition-metal

transition-metal-complex

display_name: transition-metal-complex


en: We employ ab-initio electronic-structure calculations to investigate the charge-density waves and periodic lattice distortions in bilayer 2H-NbSe₂. We demonstrate that the vertical stacking can give rise to a variety of patterns that may lower the symmetry of the charge-density waves exhibited separately by the two composing 1H-NbSe₂ monolayers. The general tendency to a spontaneous symmetry breaking observed in the ground state and the first excited states is shown to originate from a non-negligible interlayer coupling. Simulated images for scanning tunneling microscopy as well as geometric structure factors show signatures of the different stacking orders. This may not only be useful to reinterpret past experiments on surfaces and thin films, but it may also be exploited to devise ad hoc experiments for the investigation of the stacking order in 2H-NbSe₂. We anticipate that our analysis not only applies to the 2H-NbSe₂, but is also relevant for thin films and bulk, whose smallest centrosymmetric component is indeed the bilayer. Finally, our results illustrate clearly that the vertical stacking is not only important for 1T structures, as exemplified by the metal-to-insulator transition observed in 1T-TaS₂, but seems to be a general feature of metallic layered transition metal dichalcogenides as well. The dataset includes the fully relaxed atomic positions of the most favorable charge-density waves, as well as the main output files of the electronic structure calculations. Selected files for the charge densities are also included.

en: Stacking of charge-density waves in 2H-NbSe₂ bilayers

materialscloud

en: Materials Cloud

charge-density-waves

display_name: charge-density-waves

dft

display_name: dft

marie-curie-fellowship

display_name: marie-curie-fellowship

stacking-order

display_name: stacking-order

transition-metal-dichalcogenides

display_name: transition-metal-dichalcogenides


en: Identification of the hydrogen lattice location in crystals is key to understanding and controlling hydrogen-induced properties. Combining nuclear reaction analysis with the ion channeling technique, we experimentally determined the locations of H and D in epitaxial nanofilms of titanium hydrides. It was found that 11 at.% of H are located at the octahedral site with the remaining H atoms in the tetrahedral site. Density functional theory calculations revealed that the structures with the partial octahedral site occupation are stabilized by the Fermi level shift and Jahn-Teller effect induced by hydrogen. In contrast, D was found to solely occupy the tetrahedral site owing to the mass effect on the zero-point vibrational energy. These findings suggest that site occupation of hydrogen can be controlled by changing the isotope mixture ratio, which leads to promising manifestation of novel hydrogen-related phenomena.

en: Isotope-dependent site occupation of hydrogen in epitaxial titanium hydride nanofilms

hydrogen

display_name: hydrogen


en: Combining the excellent thermal and electrical properties of Cu with the high abrasion resistance and thermal stability of W, Cu-W nanoparticle-reinforced metal matrix composites and nano-multilayers (NMLs) are finding applications as brazing fillers and shielding material for plasma and radiation.  Due to the large lattice mismatch between fcc Cu and bcc W, these systems have complex interfaces that are beyond the scales suitable for ab initio methods, thus motivating the development of chemically accurate interatomic potentials. Here, a neural network potential (NNP) for Cu-W is developed within the Behler-Parrinello framework using a curated training dataset that captures metallurgically-relevant local atomic environments. The Cu-W NNP accurately predicts (i) the metallurgical properties (elasticity, stacking faults, dislocations, thermodynamic behavior) in elemental Cu and W, (ii) energies and structures of Cu-W intermetallics and solid solutions, and (iii) a range of fcc Cu/bcc W  interfaces, and exhibits physically-reasonable behavior for solid W/liquid Cu systems.  As will be demonstrated in forthcoming work, this near-ab initio-accurate NNP can be applied to understand complex phenomena involving interface-driven processes and properties in Cu-W composites.

en: Machine learning potential for the Cu-W system

copper-tungsten

display_name: copper-tungsten

marvel

display_name: marvel

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

nanomultilayer

display_name: nanomultilayer

neural-network-potentials

display_name: neural-network-potentials


en: We introduce an automated, flexible framework (aiida-hubbard) to self-consistently calculate Hubbard U and V parameters from first-principles. By leveraging density-functional perturbation theory, the computation of the Hubbard parameters is efficiently parallelized using multiple concurrent and inexpensive primitive cell calculations. Furthermore, the intersite V parameters are defined on-the-fly during the iterative procedure to account for atomic relaxations and diverse coordination environments. We devise a novel, code-agnostic data structure to store Hubbard related information together with the atomistic structure, to enhance the reproducibility of Hubbard-corrected calculations. We demonstrate the scalability and reliability of the framework by computing in high-throughput fashion the self-consistent onsite U and intersite V parameters for 115 Li-containing bulk solids with up to 32 atoms in the unit cell. Our analysis of the Hubbard parameters calculated reveals a significant correlation of the onsite U values on the oxidation state and coordination environment of the atom on which the Hubbard manifold is centered, while intersite V values exhibit a general decay with increasing interatomic distance. We find, e.g., that the numerical values of U for the 3d orbitals of Fe and Mn can vary up to 3 eV and 6 eV, respectively; their distribution is characterized by typical shifts of about 0.5 eV and 1.0 eV upon change in oxidation state, or local coordination environment. For the intersite V a narrower spread is found, with values ranging between 0.2 eV and 1.6 eV when considering transition metal and oxygen interactions. This framework paves the way for the exploration of redox materials chemistry and high-throughput screening of d and f compounds across diverse research areas, including the discovery and design of novel energy storage materials, as well as other technologically-relevant applications.

en: First-principles Hubbard parameters with automated and reproducible workflows

aiida

display_name: aiida

dftu

display_name: dftu

dftuv

display_name: dftuv

hubbard

display_name: hubbard

oxidation-state

display_name: oxidation-state


en: Comprehending water dynamics is crucial in various fields such as water desalination, ion separation, electrocatalysis, and biochemical processes. While ab-initio molecular dynamics (AIMD) accurately portray water's structure, computing its dynamic properties over nanosecond timescales proves cost-prohibitive. This study employs machine learning potentials (MLPs) to accurately determine the dynamical properties of liquid water with ab-initio accuracy. Our findings reveal diversity in the calculated diffusion coefficient (D) and viscosity of water (η) across different methodologies. Specifically, while the GGA, meta-GGA, and hybrid functional methods struggle to predict dynamic properties under ambient conditions, whereas methods on the higher level of Jacob's ladder of DFT approximation perform significantly better. Intriguingly, we discovered that all D and η adhere to the established Stokes-Einstein (SE) relation for all the ab-initio water. The diversity observed among different methods can be attributed to distinct structural entropy, affirming the applicability of excess entropy scaling relations across all functionals. The correlation between D and η provides valuable insights for identifying the ideal temperature to accurately replicate liquid water's dynamic properties. Furthermore, our findings can validate the rationale behind employing artificially high temperatures in the simulation of water via AIMD. These outcomes not only pave the path toward designing better functionals for water but also underscore the significance of water's many-body characteristics.

en: The dynamic diversity and invariance of ab-initio water

aimd

display_name: aimd

density-functional-theory

display_name: density-functional-theory

diffusion

display_name: diffusion

dynamical-properties

display_name: dynamical-properties

machine-learning-potential

display_name: machine-learning-potential

water

display_name: water


en: On-surface synthesis relies on carefully designed molecular precursors that are thermally activated to afford desired, covalently coupled architectures. In a recent publication, we studied the reactions of vinyl groups on poly-para-phenylene and provided a comprehensive description of all the reaction steps taking place on the Au(111) surface under ultrahigh vacuum conditions. We find that vinyl groups successfully cyclize with the phenylene rings in the ortho positions, forming a dimethyl-dihydroindenofluorene as the repeating unit, which can be further dehydrogenated to a dimethylene-dihydroindenofluorene structure. Interestingly, the obtained polymer can be transformed cleanly into thermodynamically stable polybenzo[k]tetraphene at higher temperature, involving a previously elusive pentagon-to-hexagon transformation via ring opening and rearrangement on a metal surface. Our insights into the reaction cascade unveil fundamental chemical processes involving vinyl groups on surfaces. Because the formation of specific products is highly temperature-dependent, this innovative approach offers a valuable tool for fabricating complex, low-dimensional nanostructures with high precision and yield. This record contains the data that support the scientific results in the publication.

en: On-surface cyclization of vinyl groups on poly-para-phenylene involving an unusual pentagon to hexagon transformation

cscs

display_name: cscs

dft

display_name: dft

on-surface-synthesis

display_name: on-surface-synthesis

spm

display_name: spm

vinyl-cyclization

display_name: vinyl-cyclization


en:
The dynamics of (few) electrons dissolved in an ionic fluid—as when a small amount of metal is added to a solution while upholding its electronic insulation—manifests interesting properties that can be ascribed to nontrivial topological features of particle transport (e.g., Thouless' pumps). In the adiabatic regime, the charge distribution and the dynamics of these dissolved electrons are uniquely determined by the nuclear configuration. Yet, their localization into effective potential wells and their diffusivity are dictated by how the self-interaction is modeled. In this article, we investigate the role of self-interaction in the description of localization and transport properties of dissolved electrons in non-stoichiometric molten salts. Although the account for the exact (Fock) exchange strongly localizes the dissolved electrons, decreasing their tunneling probability and diffusivity, we show that the dynamics of the ions and of the dissolved electrons are largely uncorrelated, irrespective of the degree to which the electron self-interaction is treated, and in accordance with topological arguments.
This record contains input and analysis scripts to reproduce the findings of this article.

en: Self-interaction and transport of solvated electrons in molten salts

envi

en: Environment

ionic-transport

display_name: ionic-transport

marie-curie-fellowship

display_name: marie-curie-fellowship

max

display_name: max

molten-salts

display_name: molten-salts

topological-materials

display_name: topological-materials

transport

display_name: transport


en: Emerging two-dimensional (2D) materials bring unprecedented opportunities for electronic applications. The design of high-performance devices requires an accurate prediction of carrier mobility in 2D materials, which can be obtained using state-of-the-art ab initio calculations. However, various factors impact the computational accuracy, leading to contradictory estimations for the mobility. In this work, targeting accurate and efficient ab initio calculations, transport properties in III-V monolayers are reported using the Boltzmann transport equation, and the influences of pseudopotential, quadrupole correction, Berry connection, and spin-orbit coupling (SOC) on mobilities are systematically investigated. Our findings are as follows: (1) The inclusion of semi-core states in pseudopotentials is important to obtain accurate calculations. (2) The variations induced by dynamical quadrupole and Berry connection when treating long range fields can be respectively 40% and 10%. (3) The impact of SOC can reach up to 100% for materials with multi-peak bands. Importantly, although SOC notably modifies the electronic wavefunctions, it negligibly impacts the dynamical matrices and scattering potential variations. As a result, the combination of fully-relativistic electron calculation and scalar-relativistic phonon calculation can strike a good balance between accuracy and cost. This work compares computational methodologies, providing guidelines for accurate and efficient calculations of mobilities in 2D semiconductors.

en: Guidelines for accurate and efficient calculations of mobilities in two-dimensional semiconductors

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).

electron-phonon-coupling

display_name: electron-phonon-coupling


en: An excellent single-atomic photocatalyst, Ti@C₄N₃, is theoretically found to effectively convert CO₂ to C₂H₆ by density functional theory (DFT) calculations and non-adiabatic molecular dynamics (NAMD) simulations. The Ti@C₄N₃ photocatalyst has remarkable stability both thermally, chemically, and mechanically. Electronically, it has strong absorption properties, suitable band positions, and a long photogenerated electron lifetime, allowing photogenerated electrons to migrate to the surface. Notably, the high-valence active site effectively activates two CO₂ through dual activation: Under light irradiation, the weakly adsorbed CO₂ undergoes photo-induced activation by the photoelectron of conduction band minimum (CBM); without light, the high Lewis acidity of the Ti site induces CO₂ activation through back-donating π-bond. Contrast simulation results uncovered that dual activation of CO₂ is attributed to the thermal and photonic synergy. Furthermore, two activated CO₂ species under light easily couple to form oxalate with the barrier of 0.19 eV, and further reduced to C₂H₆ with a low activation energy of 1.09 eV.

en: Dual activation and C-C coupling on single atom catalyst for CO₂ photoreduction

materialscloud

en: Materials Cloud

c-c-coupling

display_name: c-c-coupling

dual-activation

display_name: dual-activation

single-atom-catalyst

display_name: single-atom-catalyst


en:
Passivation oxide formation is the key for corrosion control of metal alloys.  The kinetics of competing oxide formation and dissolution determines alloy corrosion behaviors in aqueous solution. Despite the important role of the multi-component oxide evolution, little has been known on the kinetics from the atomistic level.  We have built a computational framework that enables simulations of competing kinetic processes in multi-component oxides from first principles. The effects of applied voltage, pH and temperature on oxide growth, dissolution and reprecipitation can all be captured in this model. Combining with our experimental measurements on Alloy 22 and a Ni80%-Cr20% model alloy, we identified three voltage regimes with distinct oxide thicknesses and compositions.
The oxide energetics of various stoichiometries are calculated by the density functional theory (DFT). Then the obtained data are used to train a surrogate lattice Hamiltonian with the cluster expansion (CE) method. Finally, kinetic Monte Carlo (KMC) simulations are run with the cation hopping barriers calculated on-the-fly based on the local environments from the combination of the above Hamiltonian and the linear Brønsted−Evans−Polanyi (BEP) relation.

en: Atomic-scale understanding of oxide growth and dissolution kinetics of Ni-Cr alloys

cluster-expansion

display_name: cluster-expansion

kinetics

display_name: kinetics

ni-cr-oxide

display_name: ni-cr-oxide

vasp

display_name: vasp


en: Koopmans spectral functionals are a powerful extension of Kohn-Sham density-functional theory (DFT) that enable the prediction of spectral properties with state-of-the-art accuracy. The success of these functionals relies on capturing the effects of electronic screening through scalar, orbital-dependent parameters. These parameters have to be computed for every calculation, making Koopmans spectral functionals more expensive than their DFT counterparts. In this work, we present a machine-learning model that — with minimal training — can predict these screening parameters directly from orbital densities calculated at the DFT level. We show on two prototypical use cases that using the screening parameters predicted by this model, instead of those calculated from linear response, leads to orbital energies that differ by less than 20 meV on average. Since this approach dramatically reduces run-times with minimal loss of accuracy, it will enable the application of Koopmans spectral functionals to classes of problems that previously would have been prohibitively expensive, such as the prediction of temperature-dependent spectral properties. More broadly, this work demonstrates that measuring violations of piecewise linearity (i.e. curvature in total energies with respect to occupancies) can be done efficiently by combining frozen-orbital approximations and machine learning.

en: Predicting electronic screening for fast Koopmans spectral functional calculations

electronic-structure

display_name: electronic-structure

koopmans-spectral-functionals

display_name: koopmans-spectral-functionals

machine-learning

display_name: machine-learning

orbital-density-dependent-functionals

display_name: orbital-density-dependent-functionals

snsf

display_name: snsf


en: Hopf et al. first reported the high-temperature 6π-electrocyclization of cis-hexa-1,3-diene-5-yne to benzene in 1969. Subsequent studies using this cyclization have been limited by its very high reaction barrier. Here, we show that the reaction barrier for two model systems, (E)-1,3,4,6-tetraphenyl-3-hexen-1,5-diyne (1a) and (E)-3,4-bis(4-iodophenyl)-1,6-diphenyl-3-hexen-1,5-diyne 1b, is decreased by nearly half on a Au(111) surface. In recent work, we have used scanning tunneling microscopy (STM) and non-contact atomic force microscopy (nc-AFM) to monitor the Hopf cyclization of enediynes 1a,b on Au(111). Enediyne 1a undergoes two sequential, quantitative Hopf cyclizations, first to naphthalene derivative 2, and finally to chrysene 3. Density functional theory (DFT) calculations reveal that a gold atom from the Au(111) surface is involved in all steps of this reaction, and that it is crucial to lowering the reaction barrier. Our findings have important implications for the synthesis of novel graphene nanoribbons. Ullman coupling of enediyne 1b at 20 ˚C on Au(111), followed by a series of Hopf cyclizations and aromatization reactions at higher temperatures, produces nanoribbons 12, and eventually 13 upon further heating. These results show for the first time that graphene nanoribbons can be synthesized on-surface using the Hopf cyclization mechanism. This record contains all simulation data that support our scientific work.

en: Dramatic acceleration of the Hopf cyclization on gold(111): from enediynes to unusual graphene nanoribbons

cscs

display_name: cscs

dft

display_name: dft

nc-afm

display_name: nc-afm

on-surface-synthesis

display_name: on-surface-synthesis

stm

display_name: stm


en: Achieving fine control over the dispersion of supported platinum nanoparticles (Pt) is a promising avenue to enhance their catalytic activity and selectivity. Experimental observations suggest that exposing ceria-supported Pt nanoparticles to O₂ at 500 °C promote their dispersion into smaller particles and eventually single atoms. In the associated paper we have combined several approaches and types of models in a consistent atomistic framework to evaluate the relative stability of ceria-supported Pt as a function of the degree of oxidation of Pt and of the particle size, ranging from single atoms to nanoparticles of 1.5 nm of diameter.

en: The role of oxidizing conditions in the dispersion of supported platinum nanoparticles explored by ab initio modeling

cerium-oxide

display_name: cerium-oxide

dftu

display_name: dftu

platinum

display_name: platinum


en: The use of perovskite oxides as bifunctional catalysts for oxygen electrocatalysis is a promising strategy for developing high-performance unified regenerative fuel cells (URFCs), thanks to their highly tunable structure. This work explores the exsolution of well-dispersed nanoparticles containing reduced Pt species from La0.6Sr0.4Fe0.95Pt0.05O3-δ (LSFPt) as an effective approach to design bifunctional catalysts for oxygen electrocatalysis. A 10 h reduction treatment at 500 °C in 5% H2/Ar promotes the exsolution of ~1.2 nm Pt-containing nanoparticles on the surface, increasing oxygen deficiency without compromising the perovskite structure. The resulting catalyst exhibits significantly improved activity for oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) in alkaline media, with over 2.5-fold performance improvement compared to the as-prepared LSFPt. Electrochemical impedance spectroscopy (EIS), combined with distribution of relaxation times (DRT) deconvolution analysis, revealed a notable decrease in medium-frequency resistance, indicating enhanced charge transfer at the electrode surface for OER and ORR. Post-mortem O K-edge spectra obtained by soft X-ray absorption spectroscopy in total electron yield mode further suggest an increased surface restructuring in the reduced LSFPt, attributed to the synergistic presence of surface defects and exsolved nanoparticles containing reduced Pt species. This one-step reduction protocol offers a promising strategy for designing efficient bifunctional electrocatalysts.

en: Harnessing bifunctional oxygen electrocatalysis: Exsolved Pt nanoparticles from La0.6Sr0.4Fe0.95Pt0.05O3-δ

bifunctional-electrocatalyst

display_name: bifunctional-electrocatalyst

distribution-of-relaxation-times-drt

display_name: distribution-of-relaxation-times-drt

oxygen-evolution-reaction-oer

display_name: oxygen-evolution-reaction-oer

oxygen-reduction-reaction-orr

display_name: oxygen-reduction-reaction-orr

perovskite

display_name: perovskite

pt-exsolution

display_name: pt-exsolution

unified-regenerative-fuel-cells

display_name: unified-regenerative-fuel-cells


en: Cluster expansions are commonly employed as surrogate models to link the electronic structure of an alloy to its finite-temperature properties. Using cluster expansions to model materials with several alloying elements is challenging due to a rapid increase in the number of fitting parameters and training set size. We introduce the embedded cluster expansion (eCE) formalism that enables the parameterization of accurate on-lattice surrogate models for alloys containing several chemical species. The eCE model simultaneously learns a low dimensional embedding of site basis functions along with the weights of an energy model. A prototypical senary alloy comprised of elements in groups 5 and 6 of the periodic table is used to demonstrate that eCE models can accurately reproduce ordering energetics of complex alloys without a significant increase in model complexity. Further, eCE models can leverage similarities between chemical elements to efficiently extrapolate into compositional spaces that are not explicitly included in the training dataset. The eCE formalism presented in this study unlocks the possibility of employing cluster expansion models to study multicomponent alloys containing several alloying elements.

en: Constructing multicomponent cluster expansions with machine-learning and chemical embedding

envi

en: Environment

ab-initio

display_name: ab-initio

cluster-expansion

display_name: cluster-expansion

machine-learning

display_name: machine-learning


en: Hydrogen boride (HB) sheet is a newly developed two-dimensional boron-based nanosheet. To explore its potential in electronic and catalytic applications, it's crucial to assess its chemical stability with common substances. In this study, we examine its stability in water, a substance frequently encountered in ambient conditions and various applications. Focusing on the HB sheet's edge, we utilized a nanoribbon model to observe water-edge interactions. This record includes the crystal structures optimized through first-principles calculations, which were used to analyze the properties and interactions between water and the HB sheet. Additionally, input files for system calculations are provided to facilitate reproducibility of the study's results.

en: Interplay of hydrogen boride sheets with water: An insight into edge stability

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).

boron

display_name: boron

density-functional-theory

display_name: density-functional-theory

hydrogen-boride-sheet

display_name: hydrogen-boride-sheet

water

display_name: water


en: This study proposes an eco-friendly approach to zeolite agglomeration for petroleum sorbents. The novelty lies in integrating agglomeration and deagglomeration within a single high-pressure grinding roll (HPGR) system, enhancing sorption capacity by creating a secondary porosity network. This eliminates energy-intensive calcination, making it a sustainable alternative to wet granulation. We examine the impact of binder and water dosages on sorption capacity, mechanical resistance, and textural properties of roll-compacted zeolite agglomerates. Feed materials were characterized using N₂ adsorption, XRD, XRF, particle size distribution, and SEM. Structural and functional properties were assessed via mercury intrusion porosimetry, petroleum sorption efficiency (Westinghouse test), sorption capacity, gravitational drop tests, and SEM. All sorbents (0.5–1 mm) met the 50 wt.% oil absorbency threshold for petroleum spill cleanup in Poland. The fabricated zeolite agglomerates exhibited superior sorption capacities compared to zeolite powder, Na-P1, and commercial sorbents. The optimal feed composition yielded sorbents with the best properties and versatile performance. An analysis of sorption characteristics and pore size distribution showed that a higher proportion of 10–100 µm pores improved efficiency. This study addresses the knowledge gap in zeolite powder agglomeration and demonstrates the effectiveness of integrating agglomeration and deagglomeration in a high-pressure roller press.

en: Sustainable approach to zeolite agglomeration for petroleum spill management - Agglomeration and deagglomeration integrated in one HPGR system

materialscloud

en: Materials Cloud

binder-powder

display_name: binder-powder

natural-zeolite-powder

display_name: natural-zeolite-powder

xrd-data

display_name: xrd-data


en: We investigate the superconducting properties of molybdenum disulphide (MoS₂) monolayer across a broad doping range, successfully recreating the so far unresolved superconducting dome. Our first-principles findings reveal several dynamically stable phases across the doping-dependent phase diagram. We observe a doping-induced increase in the superconducting transition temperature Tc, followed by a reduction in Tc due to the formation of charge density waves (CDWs), polaronic distortions, and structural transition from the H to the 1T′ phase. Our work reconciles various experimental observations of CDWs in MoS₂ with its doping-dependent superconducting dome structure, which occurs due to the 1×1 H to 2×2 CDW phase transition.

en: Understanding the origin of superconducting dome in electron-doped MoS₂ monolayer

charge-density-wave

display_name: charge-density-wave

first-principles

display_name: first-principles

lattice-model

display_name: lattice-model

migdal-eliashberg

display_name: migdal-eliashberg

nonadiabaticity

display_name: nonadiabaticity

polarons

display_name: polarons

prace

display_name: prace

superconductivity

display_name: superconductivity


en:
In this entry is a database of 324,426 hypothetical Metal-Organic Frameworks (MOFs) that were used in a study to screen potential carbon dioxide scrubbers.  Using a method to assemble these materials with topological blueprints, we only selected materials that could be accurately represented with the MEPO-QEq charge generation method. By ensuring that the electrostatic potential is accurately represented in these materials, screening for CO2 adsorption properties would result very few false positives/negatives. The atom-centered charges reported in the CIF file for each MOF were derived from the MEPO-QEq method, which can be found under the '_atom_type_partial_charge' column in each CIF file.

The relevant data for each MOF is reported in accompanying .csv files. Post-combustion flue gas was simulated at a temperature of both 298K and 0.15 bar CO2, and 313K and 0.15 bar CO2. Mixture adsorption was simulated with the conditions 298K and 0.15:0.85 CO2/N2 with a total pressure of 1 bar. The data file reports working capacities, which is the difference of adsorption of CO2 between two thermodynamic state points. The adsorption state point(s) are mentioned above, and two desorption values were simulated; 0.1 bar CO2 at 363K (vacuum swing adsorption) and 0.7 bar CO2 at 413K (temperature swing adsorption). The data presented in the main manuscript correspond to vacuum swing conditions.

Over 8,000 materials were selected for more refined simulations, including re-defining partial atomic charges with the REPEAT method, and more detailed simulations to obtain common chemical patterns surrounding CO2 binding sites (adsorbaphores). There is an additional .csv file with these refined calculations that accompany this entry titled 'top_MOFs_screening_data.csv'.

en: Data-driven design of metal-organic frameworks for wet flue gas CO2 capture

adsorbaphore

display_name: adsorbaphore

gas-adsorption

display_name: gas-adsorption

high-throughput-screening

display_name: high-throughput-screening

nanoporous-materials

display_name: nanoporous-materials


en: Materials that efficiently promote the thermodynamically uphill water-splitting reaction under solar illumination are essential for generating carbon-free ("green") hydrogen. Mapping out the combinatorial space of potential photocatalysts for this reaction can be expedited using data-intensive materials exploration. The calculated band gaps and band alignments can serve as key indicators and metrics to computationally screen photoactive materials. Ternary main-group metal sulfides containing p- and s-block elements represent a promising, albeit underexplored, class of photocatalysts. Here, we computationally screen 86 candidate ternary main-group metal sulfides containing p- and s-block elements. By validating electronic structure predictions against experimental band gaps and band edges for synthetically accessible materials, we propose eight potential photocatalysts. Using computed Pourbaix diagrams, we further narrowed the candidate pool to four materials based on the predicted aqueous stability. We then synthesized and characterized these four materials and experimentally screened them for photoresponsiveness under photocatalytically relevant conditions. We also characterized their experimental band gaps and band edge positions and compared them with computational predictions. Based on the experimental screening protocols, we identify MgIn₂S₄ and BaSn₂S₅ as photoresponsive materials with sufficient aqueous stability to be considered in greater depth as potential photocatalysts for overall water-splitting. This record contains the computational predictions for the four candidates discussed in our manuscript.

en: Data-intensive exploration of the photoelectrochemical responses of main-group metal sulfides

photocatalysts

display_name: photocatalysts

sulfides

display_name: sulfides

water-splitting

display_name: water-splitting


en: Within many-body perturbation theory, we calculate band offsets for a set of epitaxial interfaces, including AlP/GaP, AlAs/GaAs, Ge/AlAs, Ge/GaAs, Ge/ZnSe, Si/GaP, ZnSe/GaAs, and CaF2/Si. We consider various quasiparticle self-consistent 𝐺𝑊 schemes with or without including vertex functions. In particular, we consider two types of effective vertex functions complying with the Ward identity in the long range, one of which additionally carries a short-range part, which has been found to improve ionization potentials. The obtained band offsets correspond to model interface structures that match the experimental lattice parameters of the bulk components. Strain, zero-phonon renormalization, and spin-orbit coupling effects are properly accounted for. For the band offsets of the semiconductor-semiconductor interfaces, all the self-consistent 𝐺𝑊 schemes yield similar mean absolute errors on the order of 0.2 eV. In the case of the CaF2/Si interface, the calculated band offsets show large indetermination spanning an interval up to 1 eV, the discrepancy with respect to experiment being correlated with the error by which the band gap of the insulator is described. Through 𝐺𝑊 calculations for selected interface models, we further assess the effect of self-consistently updating the charge density. Our result support the practice of relying on semilocal or hybrid-functional schemes for determining the line-up potential.

en: Band alignments through quasiparticle self-consistent 𝐺𝑊 with efficient vertex corrections

band-alignments

display_name: band-alignments

epitaxial-interfaces

display_name: epitaxial-interfaces

gw-calculations

display_name: gw-calculations


en: A machine-learned interatomic potential for Ge-rich GexTe alloys has been developed aiming at uncovering the kinetics of phase separation and crystallization in these materials. The results are of interest for the operation of embedded phase change memories which exploits Ge-enrichment of GeSbTe alloys to raise the crystallization temperature. The potential is generated by fitting a large database of energies and forces computed within Density Functional Theory with the neural network scheme implemented in the DeePMD-kit package. The potential is highly accurate and suitable to describe the structural and dynamical properties of the liquid, amorphous and crystalline phases of the wide range of compositions from pure Ge and stoichiometric GeTe to the Ge-rich Ge₂Te alloy. Large scale molecular dynamics simulations revealed a crystallization mechanism which depends on temperature. At 600 K, segregation of most of Ge in excess was observed to occur on the ns time scale followed by crystallization of nearly stoichiometric GeTe regions. At 500 K, nucleation of crystalline GeTe was observed to occur before phase separation, followed by a slow crystal growth due to the concurrent expulsion of Ge in excess.

en: Crystallization kinetics in Ge-rich GexTe alloys from large scale simulations with a machine-learned interatomic potential

crystallization

display_name: crystallization

electronic-memories

display_name: electronic-memories

machine-learning

display_name: machine-learning

molecular-dynamics

display_name: molecular-dynamics

phase-change-materials

display_name: phase-change-materials


en: This file contains all the data, as well as the code necessary to reproduce the exact diagonalization calculations in Phys. Rev. Lett. 133, 076502 (2024) in which the phenomenon of a non-Hermitian Mott skin effect is introduced theoretically. Skin effects are a key differentiating feature of (one-dimensional) non-Hermitian systems, and are characterized by the exponential accumulation of charge towards one side of the system in all its eigenstates. It appears in noninteracting systems. This work finds a novel analogous effect which is specific to interacting quantum particles, where the particle density is constant, but a flavour/spin degree of freedom shows the exponential localization. This is demonstrated with exact diagonalization calculations of the non-Hermitian many-body Hamiltonian as well as the time-evolution of the Lindbladian.

en: Non-Hermitian Mott skin effect

envi

en: Environment

correlated-electron-systems

display_name: correlated-electron-systems

exact-diagonalization

display_name: exact-diagonalization

mott-insulators

display_name: mott-insulators

non-hermitian-systems

display_name: non-hermitian-systems

skin-effect

display_name: skin-effect


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

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

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).

nuclear-magnetic-resonance

display_name: nuclear-magnetic-resonance

superconductivity

display_name: superconductivity