en: The electronic and vibrational properties and electron-phonon couplings of one-dimensional materials will be key to many prospective applications in nanotechnology. Dimensionality strongly affects these properties and has to be correctly accounted for in first-principles calculations. Here we develop and implement a formulation of density-functional and density-functional perturbation theory that is tailored for one-dimensional systems. A key ingredient is the inclusion of a Coulomb cutoff, a reciprocal-space technique designed to correct for the spurious interactions between periodic images in periodic-boundary conditions. This restores the proper one-dimensional open-boundary conditions, letting the true response of the isolated one-dimensional system emerge. In addition to total energies, forces and stress tensors, phonons and electron-phonon interactions are also properly accounted for. We demonstrate the relevance of the present method on a portfolio of realistic systems: BN atomic chains, BN armchair nanotubes, and GaAs nanowires. Notably, we highlight the critical role of the Coulomb cutoff by studying previously inaccessible polar-optical phonons and Fröhlich electron-phonon couplings. We also develop and apply analytical models to support the physical insights derived from the calculations and we discuss their consequences on electronic lifetimes. The present work unlocks the possibility to accurately simulate the linear response properties of one-dimensional systems, sheds light on the transition between dimensionalities and paves the way for further studies in several fields, including charge transport, optical coupling and polaritronics.

en: Density functional perturbation theory for one-dimensional systems: implementation and relevance for phonons and electron-phonon interactions

ab-initio

display_name: ab-initio

charge-transport

display_name: charge-transport

density-functional-theory

display_name: density-functional-theory

electron-phonon-coupling

display_name: electron-phonon-coupling

marvel

display_name: marvel

one-dimensional-materials

display_name: one-dimensional-materials

phonons

display_name: phonons


$oid: OBSOLETE

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

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

materialscloud

approval_status: approved

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

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

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

coreshell

display_name: coreshell

metal-organic-frameworks

display_name: metal-organic-frameworks

photocatalyst

display_name: photocatalyst


en: The deposited structure models of DNA adsorbed on prisitne PG/p-Si and metal (Au/W) doped PG/p-Si sheets, have been subjected to first principles calculations  based on DFT (PBE+vdW). The calculated binding affinity on PG and p-Si surface by M062X/6-31G* level of theory and adsorption energies by DFT predicts that PG has higher sensitivity towards DNA nucleic bases compared to p-Si with evident changes in their band structure properties.

en: 2D pentagonal-graphene and pentagonal-silicene sheets engineered for the detection of DNA nucleobases for genetic biomarker: A DFT study

dft

display_name: dft

electronic-structure

display_name: electronic-structure


en:
Using high-throughput automation of ab-initio impurity-embedding simulations we created a database of 3d and 4d transition metal defects embedded into the prototypical topological insulators (TIs) Bi₂Te₃ and Bi₂Se₃. We simulate both single impurities as well as impurity dimers at different impurity-impurity distances inside the topological insulator matrix. We extract changes to magnetic moments, analyze the polarizability of non-magnetic impurity atoms via nearby magnetic impurity atoms and calculate the exchange coupling constants for a Heisenberg Hamiltonian.
We uncover chemical trends in the exchange coupling constants and discuss the impurities' potential with respect to magnetic order in the fields of quantum anomalous Hall insulators. In particular, we predict that co-doping of different magnetic dopants is a viable strategy to engineer the magnetic ground state in magnetic TIs.

en: High-throughput magnetic co-doping and design of exchange interactions in topological insulators

co-doping

display_name: co-doping

jukkr

display_name: jukkr

magnetic-doping

display_name: magnetic-doping

quantum-anomalous-hall

display_name: quantum-anomalous-hall

topological-insulator

display_name: topological-insulator

topological-materials

display_name: topological-materials


en: We present a density functional theory study of the oxygen reduction reaction (ORR) on a single atom catalyst embedded in graphene, namely, TM-N₄-C (TM = Fe and Co), using the effective screening medium method combined with the reference interaction site model (ESM-RISM). It was found that Fe-N₄-C and Co-N₄-C show comparable ORR activities from the constant electrode potential simulations, in contrast to the results obtained using the constant (neutral) charge simulation, in which the superior performance of Co-N₄-C has been predicted. The constant potential method allows the variable charge and thus, resulting in a potential dependence of the reaction free energies different from that with the constant charge method in which the potential dependence is included as an ad hoc manner. We suggest the importance of the variable charge in the simulation of the electrochemical reaction, which is enabled by ESM-RISM.

en: Oxygen reduction reaction on single-atom catalysts from density functional theory calculations combined with an implicit solvation model

implicit-solvation-model

display_name: implicit-solvation-model

oxygen-reduction-reaction

display_name: oxygen-reduction-reaction

single-atom-catalyst

display_name: single-atom-catalyst


en: We present a comprehensive vibrational analysis of hydrogen boride sheet through first-principles calculations, focusing on a variety of local structural configurations including surfaces, edges, and bilayer models - some of which contains vacancy and substitutional defects. A database of vibrational modes and simulated infrared spectra was constructed, revealing distinct spectral features that serve as characteristic fingerprints for different bonding environments. By analyzing mode distributions with respect to coordination environments, we identify the distinct contributions of terminal and bridging hydrogen atoms to specific spectral regions. The results show that structural modifications, such as interlayer stacking and edge termination, have a significant influence on vibrational characteristics, particularly in the intermediate and high frequency regions. The vibrational database enables the precise interpretation of infrared spectra and offers a reliable reference for model selection and peak analysis in both experimental and theoretical studies on hydrogen boride sheet and related materials. This archive contains the database compiled from this study, comprising (i) optimized structure files, (ii) energy and force datasets used to construct the Hessian matrix and (iii) the results from the vibrational analytics, such as simulated IR spectra and mode visualizations. The database was designed for maximum accessibility; consequently, all data are supplied in multiple standard formats.

en: Dataset for vibrational recognition of local structures in hydrogen boride sheets

density-functional-theory

display_name: density-functional-theory

hydrogen-boride-sheets

display_name: hydrogen-boride-sheets

spectroscopy

display_name: spectroscopy

two-dimensional

display_name: two-dimensional


en:
Our study reveals how Gd adatoms and dimers on a superconducting Nb(110) surface induce Yu-Shiba-Rusinov (YSR) states, offering valuable insights into magnetic interactions of rare-earth atoms on superconducting surfaces. By engineering Gd dimers along the [1-10] and [001] directions, we uncover an indirect coupling between the Gd magnetic moments and the Nb substrate via their valence d electrons, leading to significant alterations in the YSR spectrum around the dimers. We further demonstrate the possibility for Néel-type spin-spiral ground states in chains of Gd atoms on Nb(110). These findings highlight the potential of 4f elements like Gd as a promising platform for controlling a spin-spiral ground state, a crucial prerequisite for realizing a topological superconductor that can host Majorana zero modes. The combination of theoretical modeling based on density functional theory, atomistic spin-dynamics simulations and experimental techniques, including scanning tunneling microscopy and spectroscopy, provides a comprehensive understanding of the coupling mechanisms and their impact on the electronic properties of these systems and establishes rare-earth magnets on Nb as a promising platform in the field.

This dataset collects the experimental and theoretical results of this work.

en: Rare-earth atoms on Nb(110) as a platform to engineer topological superconductivity

adatoms

display_name: adatoms

dftu

display_name: dftu

magnetism

display_name: magnetism

majorana-zero-mode

display_name: majorana-zero-mode

rare-earth

display_name: rare-earth

spin-dynamics

display_name: spin-dynamics

spin-spiral

display_name: spin-spiral

stm

display_name: stm

superconductivity

display_name: superconductivity

topological-superconductor

display_name: topological-superconductor


en:
Lithium ortho-thiophosphate (Li₃PS₄) has emerged as a promising candidate for solid-state-electrolyte batteries, thanks to its highly conductive phases, cheap components, and large electrochemical stability range. Nonetheless, the microscopic mechanisms of Li-ion transport in Li₃PS₄ are far to be fully understood, the role of PS₄ dynamics in charge transport still being controversial. We build machine learning potentials targeting state-of-the-art DFT references (PBEsol, r²SCAN, and PBE0) to tackle this problem in all known phases of Li₃PS₄ (α, β and γ), for large system sizes and timescales. We discuss the physical origin of the observed superionic behavior of Li₃PS₄: the activation of PS₄ flipping drives a structural transition to a highly conductive phase, characterized by an increase of Li-site availability and by a drastic reduction in the activation energy of Li-ion diffusion. We also rule out any paddle-wheel effects of PS₄ tetrahedra in the superionic phases–previously claimed to enhance Li-ion diffusion–due to the orders-of-magnitude difference between the rate of PS₄ flips and Li-ion hops at all temperatures below melting.
This archive provides all the relevant data and input files that were used to fit the ML interatomic potentials used in this work, along with the relevant Density-Functional Theory calculations that were used for the training set construction, the validation of the ML models and the calculation of the electronic band structure of the β and γ structure. Furthermore, it provides input files of all the molecular dynamics trajectories needed to investigate Li-ion diffusion properties of Li₃PS₄ and the rotational dynamics of PS₄ tetrahedra. Finally, it provides the raw data to reproduce the figures of the manuscript associated with this archive.

en: Mechanism of charge transport in lithium thiophosphate

electrical-conductivity

display_name: electrical-conductivity

green-kubo-time-correlation-functions

display_name: green-kubo-time-correlation-functions

lithium-ion-diffusion

display_name: lithium-ion-diffusion

lithium-thiophosphate

display_name: lithium-thiophosphate

machine-learning

display_name: machine-learning

marvel

display_name: marvel

molecular-dynamics

display_name: molecular-dynamics

phase-transitions

display_name: phase-transitions

post-gga-density-functional-theory

display_name: post-gga-density-functional-theory

snsf-sinergia

display_name: snsf-sinergia


en:
Simulations of chemical reactivity in condensed phase systems represent an ongoing challenge in computational chemistry, where traditional quantum chemical approaches typically struggle with both the size of the system and the potential complexity of the reaction. Here, we introduce a workflow aimed at efficiently training neural network potentials (NNPs) to explore energy barriers in solution at the hybrid density functional theory level. The computational burden associated with training at the PBE0-D3(BJ) level is bypassed through the use of active and transfer learning techniques, whereas extensive sampling of the transition state region is accelerated by well-tempered metadynamics simulations using multiple time-step integration. These NNPs serve to explore a puzzling solute--solvent reactivity route involving the ring opening of N-enoxyphthalimide experimentally observed in methanol but not in 2,2,2-trifluoroethanol (TFE). This reaction represents a challenging example characterized by intricate hydrogen bonding networks and structurally ambiguous solvent-sensitive transition states. The methodology successfully delivers detailed free energy surfaces and relative energy barriers in quantitative agreement with experiment. These barriers are associated with an ensemble of transition states involving direct participation of up to five solvent molecules. While this picture contrasts with the single transition state structure assumed by current static models, no drastic qualitative difference is observed between the formed hydrogen bonding networks and the number of participating solvent molecules in methanol or TFE. The dichotomy between the two solvents thus essentially arises from an electronic effect (i.e., distinct nucleophilicity) and from the larger conformational entropy contributions in methanol. This example  underscores the critical role dynamic simulations at the ab initio levels play in capturing the full complexity of solute-solvent interactions.

The files used in our studies are listed below, ensuring reproducibility and providing resources for future studies related to this work.

en: Capturing dichotomic solvent behavior in solute–solvent reactions with neural network potentials

erc

display_name: erc

free-energy

display_name: free-energy

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation


en: Comprehending the structure and dynamics of water is crucial in various fields such as water desalination, ion separation, electrocatalysis, and biochemical processes. While reported works show that the ab-initio molecular dynamics (AIMD) can accu- rately portray water's structure, the artificial high temperature (AHT) from 120K to 30K is needed to mimic the quantum nature of hydrogen-bond network from GGA, metaGGA to hybrid functionals. The AHT proves to be an inadequate approach for systems involving aqueous multiphase mixtures, such as water-solid interfaces and aque- ous solutions. This is due to the activation of additional phonons in other phases, which can lead to an overestimation of the dynamics for nearby water molecules. In this work, we find the regularized SCAN (rSCAN) functional can well capture both the structure and dynamics of liquid water at ambient conditions without AHT. Moreover, rSCAN can well match the experimental results of hydration structures for alkali, alkali earth and halide ions. We anticipate that the versatile and accurate rSCAN functional will emerge as a key tool based on ab-initio simulation for investigating chemical processes in aqueous environments.

en: Ab-initio simulation of liquid water without artificial high temperature

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).

aimd

display_name: aimd

density-functional-theory

display_name: density-functional-theory

machine-learning-potential

display_name: machine-learning-potential

metagga

display_name: metagga

water

display_name: water


en: Electronic band structures is a cornerstone of condensed matter physics and materials science. Conventional methods like Wannier interpolation (WI), which are commonly used to interpolate band structures onto dense k-point grids, often encounter difficulties with complex systems, such as those involving entangled bands or topological obstructions. In this work, we introduce the Hamiltonian transformation (HT) method, a novel framework that directly enhances interpolation accuracy by localizing the Hamiltonian. Using a pre-optimized transformation, HT produces a far more localized Hamiltonian than WI, achieving up to two orders of magnitude greater accuracy for entangled bands. Although HT utilizes a slightly larger, nonlocal numerical basis set, its construction is rapid and requires no optimization, resulting in significant computational speedups. These features make HT a more precise, efficient, and robust alternative to WI for band structure interpolation, as further verified by high-throughput calculations.

en: Hamiltonian transformation for accurate and efficient band structure interpolation

materialscloud

en: Materials Cloud

envi

en: Environment

band-structure-interpolation

display_name: band-structure-interpolation

hamiltonian-transformation

display_name: hamiltonian-transformation

localized-hamiltonian

display_name: localized-hamiltonian


en:
Two-dimensional (2D) materials have attracted considerable attention due to their remarkable electronic, mechanical and optical properties, making them prime candidates for next-generation electronic and optoelectronic applications. Despite their widespread use in combination with substrates in practical applications, including the fabrication process and final device assembly, computational studies often neglect the effects of substrate interactions for simplicity.
In this record, we provide the results of the computational study of the stable 2D molybdenum-sulfur (Mo-S) structures on a c-cut sapphire (Al₂O₃). In particular, we provide the results of the evolutionary search in the Mo-S / Al₂O₃ (0001) system, the machine learning interatomic potential (MLIP) used for local relaxation of the systems during the evolutionary search together with its training set, post-processing data on electronic and phonon band structures of the stable 2D Mo-S structures, and the predicted stability patterns from the perspective of CVD synthesis.

en: Substrate-aware computational design of two-dimensional materials

crystal-structure-prediction

display_name: crystal-structure-prediction

machine-learning

display_name: machine-learning


en: Understanding electronic interactions in high-temperature superconductors is an outstanding challenge. In the widely studied cuprate materials, experimental evidence points to strong electron-phonon (e-ph) coupling and broad photoemission spectra. Yet, the microscopic origin of this behavior is not fully understood. Here we study e-ph interactions and polarons in a prototypical parent (undoped) cuprate, La₂CuO₄ (LCO), by means of first-principles calculations. Leveraging parameter-free Hubbard-corrected density functional theory, we obtain a ground state with band gap and Cu magnetic moment in nearly exact agreement with experiments. This enables a quantitative characterization of e-ph interactions. Our calculations reveal two classes of longitudinal optical (LO) phonons with strong e-ph coupling to hole states. These modes consist of Cu-O plane bond-stretching and bond-bending as well as vibrations of apical O atoms. The hole spectral functions, obtained with a cumulant method that can capture strong e-ph coupling, exhibit broad quasiparticle peaks with a small spectral weight (Z≈0.25) and pronounced LO-phonon sidebands characteristic of polaron effects. Our calculations predict features observed in photoemission spectra, including a 40-meV peak in the e-ph coupling distribution function not explained by existing models. These results show that the universal strong e-ph coupling found experimentally in lanthanum cuprates is an intrinsic feature of the parent compound, and elucidates its microscopic origin.

en: First-principles electron-phonon interactions and polarons in the parent cuprate La₂CuO₄

dftu

display_name: dftu

electron-phonon-coupling

display_name: electron-phonon-coupling

hubbard-u

display_name: hubbard-u

marvel

display_name: marvel

parent-cuprate

display_name: parent-cuprate

phonons

display_name: phonons

polarons

display_name: polarons

spectral-function

display_name: spectral-function


en:
Understanding the thermodynamics of adsorbates on surfaces is central to many (electro)catalysis applications. In first-principles calculations, additional challenges arise when considering charged adsorbates owing to long-range electrostatic interactions in the in-plane and normal directions. Here, we derive an analytical correction to obtain the energy profiles of individual charged adsorbates on metallic surfaces from finite-cell calculations in periodic boundary conditions. The method is illustrated by calculating the adsorption energy profiles of Li+, Na+, and K+ on graphite from first-principles, highlighting the very slow convergence with system size of the periodic calculations and the need to correctly recover the infinite limit.
In this record, we provide the generic input file used to generate the calculated energies of Fig. 4.

en: Charged adsorbates on metallic surfaces from periodic to open boundary conditions

charged-defects

display_name: charged-defects

dft

display_name: dft

electrochemistry

display_name: electrochemistry

surface--interface

display_name: surface--interface


en: We use symmetry analysis and density functional theory to show that changes in magnetic order at a surface with respect to magnetic order in the bulk can be generically determined by considering local magnetoelectric responses of the crystal. Specifically, analysis of the atomic-site magnetoelectric responses, or equivalently the corresponding local magnetic multipoles, can be used to predict all surface magnetic modifications arising purely from symmetry lowering via termination of the bulk magnetic order. This analysis applies even in materials with no bulk magnetoelectric response or surface magnetization. We then demonstrate our arguments for two example antiferromagnets, metallic CuMnAs and rock-salt NiO. We find that the (010) and (1-10) surfaces of CuMnAs and NiO respectively exhibit a series of antiferroically, as well as roughness-sensitive, ferroically ordered, modifications of the surface magnetic dipole moments, via canting or changes in sublattice magnitude, consistent with the bulk ordering of the magnetic multipoles. Our findings demonstrate a universal bulk-boundary correspondance allowing the general prediction of minimal possible surface and interface magnetic modifications, even in non-magnetoelectric materials. Furthermore, it paves the way for more accurate interpretations of a wide variety of surface-sensitive measurements.

en: Local magnetoelectric effects as predictors of surface magnetic order

magnetism

display_name: magnetism

surface-science

display_name: surface-science

symmetry-analysis

display_name: symmetry-analysis


en:
Phase change materials are the most promising candidates for the realization of artificial synapsis for neuromorphic computing. Different resistance levels corresponding to analogic values of the
synapsis conductance can be achieved by modulating the size of an amorphous region embedded in its crystalline matrix. Recently, it has been proposed that a superlattice made of alternating layers of the phase change compound Sb₂Te₃ and of the TiTe₂ confining material allows for a better control of multiple intermediate resistance states and for a lower drift with time of the electrical resistance of the amorphous phase. In this work, we consider to substitute Sb₂Te₃ with the Ge₂Sb₂Te₅ prototypical phase change compound that should feature better data retention. By exploiting molecular dynamics simulations with a machine learning interatomic potential, we have investigated the crystallization kinetics of Ge₂Sb₂Te₅ nanoconfined in geometries mimicking Ge₂Sb₂Te₅/TiTe₂ superlattices. It turns out that nanoconfinement induces a slight reduction in the crystal growth velocities with respect to the bulk, but also an enhancement of the nucleation rate due to heterogeneous nucleation. The results support the idea of investigating Ge₂Sb₂Te₅/TiTe₂ superlattices for applications in neuromorphic devices with improved data retention. The effect on the crystallization kinetics of the addition of van der Waals interaction to the interatomic potential is also discussed.

en: Simulation of the crystallization process of Ge₂Sb₂Te₅ nanoconfined in superlattice geometries for phase change memories

crystallization

display_name: crystallization

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

neural-network-potential

display_name: neural-network-potential

neuromophic-computing

display_name: neuromophic-computing

phase-change-materials

display_name: phase-change-materials


en: Promoting hydrogen (H) insertion into and extraction from metal oxides plays a crucial role in tuning their properties. In our recent work, we found that the oxygen vacancy ordering in Sr(Fe,Co)Oy (SFCO) thin films can be stabilized by increasing the Co content and revealed that this stabilization is key to promoting the H insertion into SFCO oxides. This archive provides the computational inputs and crystal structures to elucidate the energies of oxygen reduction and hydrogen insertion reactions for SFCO perovskite oxides.

en: Stabilization of oxygen vacancy ordering and electrochemical-proton-insertion-and- extraction-induced large resistance modulation in strontium iron cobalt oxides Sr(Fe,Co)Oy

dftu

display_name: dftu

hydrogen

display_name: hydrogen

thermodynamics

display_name: thermodynamics


en: Isostructural metal-organic frameworks (MOFs), namely MFU-4 and MFU-4-Br, in which the pore apertures are defined by anionic side ligands (Cl− and Br−, respectively), were synthesized and loaded with noble gases. By selecting the type of side ligand, one can fine-tune the pore aperture size, allowing for precise regulation of the entry and release of gas guests. In this study, we conducted experiments to examine gas loading and release using krypton and xenon as model gases, and we complemented our findings with computational modeling. Remarkably, the loaded gas guests remained trapped inside the pores even after being exposed to air under ambient conditions for extended periods, in some cases for up to several weeks. Therefore, we focused on determining the energy barrier preventing gas release using both theoretical and experimental methods. The results were compared in relation to the types of hosts and guests, providing valuable insights into the gas trapping process in MOFs, as well as programmed gas release in air under ambient conditions. Furthermore, the crystal structure of MFU-4-Br was elucidated using the three-dimensional electron diffraction (3DED) technique, and the bulk purity of the sample was subsequently verified through Rietveld refinement. The data in this record are supplementary data to the manuscript with DOI 10.26434/chemrxiv-2024-vb8pj.

en: Experimental and Theoretical Insights on Gas Trapping in MOFs: A Case Study with Noble Gases and MFU-4 Type MOFs

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).

computational-modeling

display_name: computational-modeling

crystal-engineering

display_name: crystal-engineering

electron-diffraction

display_name: electron-diffraction

gas-storage

display_name: gas-storage

metal-organic-framework

display_name: metal-organic-framework


en: Through quasiparticle self-consistent GW, we investigate the electronic structure of the antiferromagnetic ground state of four transition-metal monoxides: MnO, FeO, CoO, and NiO. In addition to the random-phase approximation, we consider two different schemes incorporating effective vertex corrections. The first scheme includes in the polarizability a vertex function derived from the solution of the Bethe-Salpeter equation (BSE), whereas the second scheme includes in both the polarizability and self-energy a vertex function, which carries a long-range part satisfying the Ward identity and a short-range part derived from the adiabatic local density approximation. Our results include fundamental band gaps, macroscopic dielectric constants, and local magnetic moments, emphasizing the role of vertex corrections in the description of these key electronic properties. We provide quasiparticle band structures and projected densities of states allowing us to establish a connection with the Mott insulator picture and to highlight the overall good agreement with experimental photoemission spectra. The inclusion of the vertex in the self-energy preserves the overall shape of the band structures but generally produces larger rigid energy shifts for d bands than for sp bands. Through the solution of the Bethe-Salpeter equation, we determine excitonic effects and obtain dielectric response functions. Despite the experimental uncertainties, which appear sizable in the case of FeO, it is possible to discern a good general correspondence between the calculated and measured spectra, particularly for the absorption onsets and the peak positions. Furthermore, deep excitons are found to occur in the absorption spectra of FeO, CoO, and NiO, in qualitative agreement with the available characterization. Our work indicates that many-body perturbation theory including effective vertex corrections is able to provide a quantitatively accurate description of key electronic-structure features of transition-metal monoxides.

en: Quasiparticle self-consistent GW with effective vertex corrections in the polarizability and the self-energy applied to MnO, FeO, CoO, and NiO

materialscloud

en: Materials Cloud

electronic-structure

display_name: electronic-structure

gw

display_name: gw

many-body-perturbation-theory

display_name: many-body-perturbation-theory

transition-metal-oxides

display_name: transition-metal-oxides

vertex-corrections

display_name: vertex-corrections


en: Dislocation-grain boundary play a major role in the strength and ductility of structural materials. An understanding of governing parameters such as grain boundary local atomic structures on the outcome of this interaction can accelerate new alloy design strategies that tailors materials' microstructures. Previous studies have focused only on the subset of minimum-energy grain boundary structures. We present a comprehensive database of dislocation-grain boundary interaction for edge, screw, and 60o mixed dislocation with 587 < 110 > and < 112 > symmetric tilt grain boundary in FCC Cu consisting of 73 minimum-energy grain boundary structures and 514 metastable ones. The dataset contains the outcomes for 5234 unique interactions with a particular dislocation type, grain boundary structure, and applied shear stress.

en: Dislocation-grain boundary interaction dataset for FCC Cu

envi

en: Environment

dislocation-grain-boundary-interaction

display_name: dislocation-grain-boundary-interaction

grain-boundary-structure

display_name: grain-boundary-structure

material-science

display_name: material-science


en:
This study investigates an electrolyte system composed of lithium hexafluorophosphate (LiPF6), ethylene carbonate (EC) and ethyl methyl carbonate (EMC). For the assembly of full cells, electrodes based on graphite and lithium nickel dioxide (LNO) are used. This work provides insight into the similarity of formulations of an electrolyte optimized for maximum conductivity and another one optimized for maximum cycle life are expected to be in this chemical system. The goal is to assess whether it is promising to target research efforts on finding an electrolyte formulation within this chemical space which can fulfill both requirements.
A campaign utilizing the latest version of FINALES is designed to determine conductivity values and predict end of life for various electrolyte formulations containing the aforementioned chemicals. The campaigns were able to reproducibly identify regions of high ionic conductivity of the aforementioned chemical composition. The ML methodology applied for the EOL optimisation allowed accelerated early life-time predictions.

en: FINALES - Electrolyte optimization for maximum conductivity and for maximum cycle life

battery

display_name: battery

big-map

display_name: big-map

electrolyte-formulation

display_name: electrolyte-formulation

end-of-lifetime

display_name: end-of-lifetime

experimental

display_name: experimental

ionic-conductivity

display_name: ionic-conductivity

machine-learning

display_name: machine-learning

map

display_name: map

material-acceleration-platform

display_name: material-acceleration-platform

polis

display_name: polis


en:
The combination of the high wear resistance and mechanical strength of W with the high thermal conductivity of Cu makes the Cu/W system an attractive candidate material for heat sink plasma and radiation tolerance applications. However, the resulting mechanical properties of multilayers and coatings strongly depend on the microstructure of the layers.
In this work, the mechanical properties of Cu/W nanomultilayers with different densities of internal interfaces are systematically investigated for two opposite in-plane stress states and critically discussed in comparison with literature. Atomistic simulations with the state-of-the-art neural network potential are used to explain the experimental findings. The results suggest that the microstructure, specifically the excess free volume associated with porosity and interface disorder interconnected with the stress state, has a great impact on the mechanical properties, notably Young's modulus of Cu/W nanomultilayers.

en: Effect of residual stress and microstructure on mechanical properties of sputter-grown Cu/W nanomultilayers

atomistic-simulations

display_name: atomistic-simulations

cuw

display_name: cuw

hardness

display_name: hardness

nanoindentation

display_name: nanoindentation

nanomultilayers

display_name: nanomultilayers

youngs-modulus

display_name: youngs-modulus


en: In this paper, we proposed a model coupling the lattice Boltzmann and the phase field methods with anisotropic effects is proposed, which is used to numerically describe the growth and movement of dendrites in rapid solidification of alloys. The model was applied to investigate the effects of dendrite movement and interfacial non-equilibrium on evolution of dendritic patterns for Si-9.0at%As and the CET for Al-3.0wt%Cu alloys. Both the growth and remelt processes of isolated dendrites are studied, and the result reveals the remelting influences on dendrite growth and solute micro-segregation in the condition of directional solidification. This dataset contains the underlying data for the above. This work demonstrates that the proposed model has a wide range of applicability and great potential to simulate the microstructure evolution with various solidification conditions.

en: An anisotropic lattice Boltzmann - phase field model for dendrite growth and movement in rapid solidification of binary alloys

dendrite-growth

display_name: dendrite-growth

lattice-boltzmann

display_name: lattice-boltzmann

phase-field

display_name: phase-field

rapid-solidification

display_name: rapid-solidification

solid-sedimentation

display_name: solid-sedimentation


en: Nitrate is a significant contaminant in Polar snow. Its photolysis in environmental sunlight generates reactive nitrogen, which impacts the oxidative capacity of the atmosphere, influencing the fate and lifetimes of pollutants. The photolysis of nitrate can produce either NO2 or NO2- , with recent experiments suggesting that the process is accelerated at the air-ice interface compared to the bulk solution. In this study, we employed multiscale modeling approaches to investigate the enhanced photoreactivity of nitrate at the ice surface in the presence of two different cations. We characterized the solvation shell of NO3- and explored its pairing with cations in water and ice using ab initio molecular dynamics and enhanced sampling. Molecular trajectories were used to calculate light absorption spectra at different solvation conditions and finite temperature. Our analysis revealed that the pairing of nitrate with cations may alter the molar absorption coefficient of nitrate at the air-ice interface affecting the rate of photolysis observed in experiments of ammonium nitrate deposited on snow.

en: Solvation Structure and UV-Visible Absorption Spectra of the Nitrate Anion at the Air-Ice Interface by First Principles Molecular Simulations

dft

display_name: dft

ice-surface

display_name: ice-surface

molecular-dynamics

display_name: molecular-dynamics

nitrate

display_name: nitrate

solvation

display_name: solvation

tddft

display_name: tddft

uv-vis-absorption

display_name: uv-vis-absorption


neural-network-potential-for-zr-h

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

en: Neural network potential for Zr-H

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

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

neural-network-potentials

display_name: neural-network-potentials

zirconium-hydrides

display_name: zirconium-hydrides


en: In situ interfacial rheology and numerical simulations are used to investigate microgel monolayers in a wide range of packing fractions, ζ2D. The heterogeneous particle compressibility determines two flow regimes characterized by distinct master curves. To mimic the microgel architecture and reproduce experiments, an interaction potential combining a soft shoulder with the Hertzian model is introduced. In contrast to bulk conditions, the elastic moduli vary nonmonotonically with ζ2D at the interface, confirming long-sought predictions of reentrant behavior for Hertzian-like systems.

en: Interfacial fluid rheology of soft particles

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).

elastic-properties

display_name: elastic-properties

hertzian-model

display_name: hertzian-model

liquid-liquid-interfaces

display_name: liquid-liquid-interfaces

microgels

display_name: microgels

softness-and-elasticity

display_name: softness-and-elasticity


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

materialscloud

en: Materials Cloud

machine-learning

display_name: machine-learning

marvel

display_name: marvel

nmr

display_name: nmr

nmr-crystallography

display_name: nmr-crystallography


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

envi

en: Environment

halide-perovskites

display_name: halide-perovskites

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

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).

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

materialscloud

en: Materials Cloud

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

envi

en: Environment

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

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

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).

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

materialscloud

en: Materials Cloud

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

envi

en: Environment

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

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

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).

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

materialscloud

en: Materials Cloud

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)

envi

en: Environment

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

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

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).

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

materialscloud

en: Materials Cloud

atuliflapon

display_name: atuliflapon

nmr-crystallography

display_name: nmr-crystallography

solid-state-nmr

display_name: solid-state-nmr