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

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: 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.

materialscloud

approval_status: approved

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

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

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

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

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

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:
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

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:
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


neural-network-potential-for-zr-h

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

en: Neural network potential for Zr-H

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

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

neural-network-potentials

display_name: neural-network-potentials

zirconium-hydrides

display_name: zirconium-hydrides


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

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

ab-initio-calculations

display_name: ab-initio-calculations

cuw

display_name: cuw

degradation-kinetics

display_name: degradation-kinetics

in-plane-strain

display_name: in-plane-strain

nano-multilayers

display_name: nano-multilayers

vacancy-driven-diffusion

display_name: vacancy-driven-diffusion


en: 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

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: 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: 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

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".

materialscloud

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

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

en: Materials Cloud

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: 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

envi

en: Environment

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

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

nuclear-structural-materials

display_name: nuclear-structural-materials

specimen-size-effect

display_name: specimen-size-effect

sub-sized-specimens

display_name: sub-sized-specimens

tensile-properties

display_name: tensile-properties


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

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

aimd

display_name: aimd

density-functional-theory

display_name: density-functional-theory

diffusion

display_name: diffusion

dynamical-properties

display_name: dynamical-properties

machine-learning-potential

display_name: machine-learning-potential

water

display_name: water


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

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

c-c-coupling

display_name: c-c-coupling

dual-activation

display_name: dual-activation

single-atom-catalyst

display_name: single-atom-catalyst


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

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

bifunctional-electrocatalyst

display_name: bifunctional-electrocatalyst

distribution-of-relaxation-times-drt

display_name: distribution-of-relaxation-times-drt

oxygen-evolution-reaction-oer

display_name: oxygen-evolution-reaction-oer

oxygen-reduction-reaction-orr

display_name: oxygen-reduction-reaction-orr

perovskite

display_name: perovskite

pt-exsolution

display_name: pt-exsolution

unified-regenerative-fuel-cells

display_name: unified-regenerative-fuel-cells


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

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

boron

display_name: boron

hydrogen-boride-sheet

display_name: hydrogen-boride-sheet


en:
The adsorption energies of perfluorobutanesulfonic acid, perfluorobutanoic acid, and trifluoroacetic acid on functionalized activated carbon are calculated from first principles. We introduce a novel approach based on a thermochemical cycle and a continuum solvation model to address neutral and charged adsorption complexes and account for concentration, pH, and pore-size effects. The results highlight the benefit of N-based functional groups for enhancing the removal of challenging, short-chain perfluoroalkyl substances from water by activated carbon.

This record includes the output files of Quantum ESPRESSO relaxation calculations of PFAS adsorption on functionalized activated carbon.

en: Adsorption of Short-Chain Perfluoroalkyl Substances (PFAS) on Functionalized Activated Carbon from First Principles

activated-carbon

display_name: activated-carbon

dft

display_name: dft

pfas

display_name: pfas


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 4,775 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.

materialscloud

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

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

en: 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 thermoresponsiveness of polymer-based soft materials opens perspectives in many applicative fields. This property in hydrated systems has the simple origin of a reversible de-mixing across a lower critical solution temperature, and has been explored for several chemically different amphiphilic polymers, each of them with specific critical conditions and phase behaviours. This work investigates for the first time by extensive atomistic molecular dynamics simulations the temperature dependence of the infinitely diluted aqueous solution of two such thermoresponsive macromolecules, poly(N-isopropyl-methacrylamide), PNIPMAM, and poly(2-isopropyl-2-oxazoline), PIPOX, in comparison to poly(N-isopropylacrylamide), PNIPAM, the much-more studied prototype system for polymers exhibiting a lower consolute boundary in water. The evolution of conformation and hydration water of the macromolecules is detected in a temperature range from well-below to well-above the transition temperature, with a favourable comparison to available experimental data. The resulting molecular description provides an explanation for deviations in solution properties of these macromolecules, including the effect of chaotropic anions and of solvent isotopic composition on the value of the lower critical solution temperature and the transition enthalpy. Overall, simulation findings show how the trade-off between gain in translational entropy of water molecules, associated with the reduction in solvent-excluded volume during the transition, and polymer-specific conformational features, determines the temperature response of these macromolecules in aqueous solution. In light of this molecular characterisation, a correlation between the single chain behaviour across the transition and the type I or type II phase diagram of the polymer aqueous solution can be postulated.

materialscloud

en: Materials Cloud

en: Understanding the lower critical solution temperature of amphiphilic synthetic polymers in water: the role of conformation and hydration

coil-to-globule-transition

display_name: coil-to-globule-transition

hydration-water

display_name: hydration-water

lcst

display_name: lcst

thermoresponsive-polymers

display_name: thermoresponsive-polymers


en: This study discloses the effect of hydrogen impurities on the local chemical bonding states and structure of amorphous alumina films by predicting measured Auger parameter shifts using a combination of atomistic and electrostatic modeling. Different amorphous alumina polymorphs with variable H-content and density, as grown by atomic layer deposition, were successfully modeled using a universal machine learning interatomic potential. The annealing of highly defective crystalline hydroxide structures with experimental H-contents at the corresponding atomic layer deposition temperatures led to excellent agreement between theory and experiment in the density and structure of the resulting amorphous alumina polymorphs. The measured Auger parameter shifts of Al cations in such polymorphs were accurately predicted with respect to the H content by assuming that all H atoms are present in the form of hydroxyl ligands in the randomly interconnected 4-fold, 5-fold, and 6-fold nearest-coordination spheres of Al. As revealed by a combination of atomistic and electrostatic modeling, the measured Auger shifts with an increase in the H content and an accompanying decrease in the oxide density depend on the complex correlations between local coordination, bond lengths, bond angles, and ligand type(s) around the core-ionized atoms. Moreover, cryogenic X-ray photoelectron spectroscopy is suggested to offer new insights into the local chemical and structural building blocks of crystalline and amorphous oxides by reducing thermal noise. These findings and fundamental knowledge contribute to advancing the design of e.g. hydrogen oxide barrier films, oxide membranes for H separation, H storage materials, and fuel cells for a hydrogen-based economy.

en: Effect of hydrogen on the local chemical bonding states and structure of amorphous alumina by atomistic and electrostatic modeling and first principles calculations of auger parameter shifts

envi

en: Environment

amorphous-oxide

display_name: amorphous-oxide

atomic-layer-deposition-ald

display_name: atomic-layer-deposition-ald

auger-parameter

display_name: auger-parameter

density-functional-theory-dft

display_name: density-functional-theory-dft

electrostatic-modeling

display_name: electrostatic-modeling

hydrogen

display_name: hydrogen

molecular-dynamics-md

display_name: molecular-dynamics-md

x-ray-photoelectron-spectroscopy-xps

display_name: x-ray-photoelectron-spectroscopy-xps


en:
A first-principles approach for calculating ion separation in solution through 2D membranes is proposed. Ionic energy profiles across the membrane are obtained first, where solvation effects are explicitly simulated by machine-learning molecular dynamics, electrostatic corrections are applied to remove finite-size capacitive effects, and a mean-field treatment of the electrochemical double layer charging is used. Entropic contributions are assessed analytically and through a thermodynamic integration scheme. Ionic separations are then inferred through a microkinetic model of the filtration process, accounting for steady-state charge separation effects across the membrane. The approach is applied to Li+, Na+, K+ sieving through a crown-ether functionalized graphene membrane, with a case study of the mechanisms for a highly selective and efficient extraction of lithium from aqueous solutions.
This record contains the MD trajectories used to generate the energy and free energy profiles of Fig. 4.

en: Ion sieving in 2D membranes from first principles

ion-sieving

display_name: ion-sieving

machine-learning

display_name: machine-learning

molecular-dynamics

display_name: molecular-dynamics


en:
Reproducibility in running scientific simulations on high-performance computing (HPC) environments is a persistent challenge due to variations in software and hardware stacks. Differences in software versions or hardware-specific optimizations often lead to discrepancies in simulation outputs. While Linux containers are commonly used to standardize software environments, tools like Docker lack reproducibility in image creation, requiring archiving of binary image blobs for future use. This method turns containers into black boxes, preventing verification of how the contained software was built.

In the linked paper, we demonstrate how we use GNU Guix to create our software stack bit-by-bit reproducible from a source bootstrap. Our approach incorporates a portable OpenMPI implementation, optimized software builds, and deployment via Apptainer images across three HPC environments. We show that our reproducible software stack facilitates consistent multi-physics simulations and complex workflows on diverse HPC platforms, exemplified by the OpenGeoSys software project. To ensure provenance of our findings, we utilized the AiiDA workflow manager.

This dataset includes the complete AiiDA provenance database underlying the results presented in the paper. The AiiDA workflow itself is defined in and can be reproduced with this repository: https://gitlab.opengeosys.org/bilke/hpc-container-study.

Version 1 of this record did not use optimized packages (Guix `--tune` option was not passed properly). This has been fixed in version 2 of this record.

en: Reproducible HPC software deployments, simulations and workflows

aiida

display_name: aiida

gnu-guix

display_name: gnu-guix

hpc

display_name: hpc

opengeosys

display_name: opengeosys

reproducibility

display_name: reproducibility

workflows

display_name: workflows


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

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

condensed-matter-physics

display_name: condensed-matter-physics

first-principles-simulations

display_name: first-principles-simulations

heisenberg-model

display_name: heisenberg-model

magnetism

display_name: magnetism

paramagnetism

display_name: paramagnetism

special-quasi-random-structures

display_name: special-quasi-random-structures

spin-fluctuations

display_name: spin-fluctuations


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

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

defect-engineering

display_name: defect-engineering

scanning-tunneling-microscopy-stm

display_name: scanning-tunneling-microscopy-stm

transition-metal-dichalcogenide-tmd

display_name: transition-metal-dichalcogenide-tmd


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

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

en: Phonon scattering rates data for silicon and HgTe

phonon-phonon-interactions

display_name: phonon-phonon-interactions

phonon-scattering-rate

display_name: phonon-scattering-rate

thermal-transport

display_name: thermal-transport


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

materialscloud

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

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

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

ab-initio

display_name: ab-initio

anharmonicity

display_name: anharmonicity

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

mobility

display_name: mobility

perovskite

display_name: perovskite


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

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

materialscloud

en: Materials Cloud

en: GapExtractor v2.0

band-gap-energy

display_name: band-gap-energy

diffuse-reflectance-spectroscopy

display_name: diffuse-reflectance-spectroscopy

polyphasic-samples

display_name: polyphasic-samples

tauc-method

display_name: tauc-method


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

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

envi

en: Environment

hole-mobility

display_name: hole-mobility

nitrides

display_name: nitrides


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

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

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

complex-oxides

display_name: complex-oxides


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

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

flexibility

display_name: flexibility

liquidsolid-friction

display_name: liquidsolid-friction

machine-learning-potential

display_name: machine-learning-potential


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

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

defect

display_name: defect

hydrogen

display_name: hydrogen

zno

display_name: zno


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

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

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

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

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

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

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

catalytic-activity

display_name: catalytic-activity

electronic-coupling

display_name: electronic-coupling

li-s-batteries

display_name: li-s-batteries

machine-learning-prediction

display_name: machine-learning-prediction


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

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

boltzmann-transport-equation

display_name: boltzmann-transport-equation

electron-phonon-coupling

display_name: electron-phonon-coupling

gpu-acceleration

display_name: gpu-acceleration


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

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

materialscloud

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

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

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

dft

display_name: dft

experimental

display_name: experimental

febid

display_name: febid

molecular-dynamics

display_name: molecular-dynamics

surface-and-gas-phase-processes

display_name: surface-and-gas-phase-processes


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

materialscloud

en: Materials Cloud

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

copolymerization

display_name: copolymerization

microgels

display_name: microgels

molecular-modeling

display_name: molecular-modeling

nmr-spectroscopy

display_name: nmr-spectroscopy

selective-neutron-scattering

display_name: selective-neutron-scattering


en: Open-shell nanographenes have attracted significant attention due to their structurally tunable spin ground state. While most characterization has been conducted on weakly interacting substrates such as noble metals, the influence of magnetic surfaces remains largely unexplored. In a recent publication,  we investigate how TbAu2, a rare-earth-element-based surface alloy, affects the magnetic properties of phenalenyl (or [2]triangulene (2T)), the smallest spin-1/2 nanographene. Scanning tunneling spectroscopy (STS) measurements reveal a striking contrast: while 2T on Au(111) exhibits a zero-bias Kondo resonance─a hallmark of a spin-1/2 impurity screened by the conduction electrons of the underlying metal─deposition on TbAu2 induces a symmetric splitting of this feature by approximately 20 mV. This opening is large compared to the few meV splitting induced by externally applied magnetic fields. We attribute this splitting to a strong proximity-induced interaction with the ferromagnetic out-of-plane magnetization of TbAu2. Moreover, our combined experimental and many-body model analysis demonstrates that this interaction is spatially modulated, following the periodicity of the TbAu2 surface superstructure. These findings highlight that TbAu2 serves as a viable platform for stabilizing and characterizing the magnetic properties of spin-1/2 nanographenes and for studying the interactions of more complex π-magnetic materials with magnetic substrates. The record contains data supporting the results presented in the publication

en: Proximity-driven magnetic coupling between an open-shell nanographene and a rare-earth surface alloy

envi

en: Environment

dftu

display_name: dftu

open-shell-molecules

display_name: open-shell-molecules

proximity-interaction

display_name: proximity-interaction

scanning-tunneling-microscopy

display_name: scanning-tunneling-microscopy

spin

display_name: spin


en: Polyacene analogues, consisting of short acene segments separated by nonbenzenoid rings, offer intriguing electronic properties and magnetic interactions. Pentalene-bridged polyacenes (PPs), in particular, hold promise for enhancing the electrical conductivity and potential open-shell ground states. However, PPs have remained elusive in solution chemistry due to poor solubility and limited synthetic protocols. In a recent publication,  we report the on-surface synthesis of PPs through the annulation between ortho-xylene groups. Scanning tunneling microscopy and atomic force microscopy reveal that the reconstructed Au(110) surface significantly enhances the chemoselectivity of the annulation process. Scanning tunneling spectroscopy combined with density functional theory suggests that PP exhibits a narrow direct band gap, similar to long acenes. This work demonstrates the potential for band structure engineering in polyacene analogues by incorporating nonbenzenoid rings, paving the way to advancements in organic electronics and spintronics. This record contains computational data supporting the results discussed in the manuscript.

en: Enhancing chemoselectivity of on-surface reactions on reconstructed Au(110): the case of a pentalene-bridged polyacene analogue

annulation

display_name: annulation

hydrocarbons

display_name: hydrocarbons

on-surface-synthesis

display_name: on-surface-synthesis


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

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

boltzmann-theory

display_name: boltzmann-theory

density-functional-calculations

display_name: density-functional-calculations

fermi-surface

display_name: fermi-surface

first-principles-calculations

display_name: first-principles-calculations

hall-effect

display_name: hall-effect

magnetoresistance

display_name: magnetoresistance

node-line-semimetals

display_name: node-line-semimetals

topological-materials

display_name: topological-materials


en:
In this paper, we present and benchmark our first-principles calculations of magnon dispersions and wave functions in monolayer Cr trihalides using the finite-momentum Bethe-Salpeter equation (BSE) to describe collective spin-flip excitations. We study the dependence of magnon dispersions on the halide species and resolve the small topological gap at the Dirac point in the magnon spectrum by including spin-orbit coupling. We analyze the obtained magnon wave functions to gain insight into magnon states in real and reciprocal space. We also compare our method with the Heisenberg spin model. This work advances the quantitative modeling of magnons, providing the starting point for studying magnon interactions in a first-principles BSE framework.

This archive includes all the inputs for DFT calculations in QuantumEspresso and GW-BSE with the Yambo code to reproduce the data in our paper.

en: Magnons in chromium trihalides calculated with the ab initio Bethe-Salpeter equation

bethe-salpeter-equation

display_name: bethe-salpeter-equation

dft

display_name: dft

magnons

display_name: magnons


en:
Traditional atomistic machine learning (ML) models serve as surrogates for quantum mechanical (QM) properties, predicting quantities such as dipole moments and polarizabilities, directly from compositions and geometries of atomic configurations. With the emergence of ML approaches to predict the "ingredients" of a QM calculation, such as the ground state charge density or the effective single-particle Hamiltonian, it has become possible to obtain multiple properties through analytical physics-based operations on these intermediate ML predictions. We present a framework to seamlessly integrate the prediction of an effective electronic Hamiltonian, for both molecular and condensed-phase systems, with PySCFAD, a differentiable QM workflow. This integration facilitates training models indirectly against functions of the Hamiltonian such as electronic energy levels, dipole moments, polarizability, etc. We then use this framework to explore various possible choices within the design space of hybrid ML/QM models, examining the influence of incorporating multiple targets on model performance and learning a reduced-basis ML Hamiltonian that can
reproduce targets computed from a much larger basis. Our benchmarks evaluate the accuracy and transferability of these hybrid models, compare them against predictions of atomic properties from their surrogate models, and provide indications to guide the design of the interface between the ML and QM components of the model. For our benchmarks we have used a subset of the QM7 and QM9 datasets as well as two extrapolative datasets for long chain polyalkenes/acenes and polyenoic acid series, along with a small graphene dataset.

en: Exploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework

automatic-differentiation

display_name: automatic-differentiation

hamiltonian

display_name: hamiltonian

machine-learning

display_name: machine-learning


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

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

acidic-oxygen-evolution-reaction

display_name: acidic-oxygen-evolution-reaction

cobalt-based-catalysts

display_name: cobalt-based-catalysts

cobalt-oxidation-state

display_name: cobalt-oxidation-state

experimental

display_name: experimental

perovskite-oxides

display_name: perovskite-oxides

x-ray-adsorption-spectroscopy

display_name: x-ray-adsorption-spectroscopy


en: The intrinsic stress in nanomultilayers (NMLs) is typically dominated by interface stress, which is particularly high in immiscible Cu/W NMLs. Here, atomistic simulations with a chemically-accurate neural network potential reveal the role of interfacial intermixing and metastable phase formation on the interface stress levels.  These results rationalize an experimentally-reported compressive-to-tensile transition as a function of NML deposition conditions and the extremely high interface stresses under some conditions.

materialscloud

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

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

en: Uncovering the origin of interface stress enhancement and compressive-to-tensile stress transition in immiscible nanomultilayers

cuw-nanomultilayers

display_name: cuw-nanomultilayers

interface-stress

display_name: interface-stress

intermixing

display_name: intermixing

metastable-phases

display_name: metastable-phases

molecular-statics

display_name: molecular-statics


en: Spin-wave excitations are fundamental to understanding the behavior of magnetic materials and hold promise for future information and communication technologies. Yet, modeling these accurately in transition-metal compounds remains challenging, starting from the self-interaction errors affecting localized and partially filled $d$-orbitals in density-functional theory (DFT) with (semi-)local functionals. In this work, we compare three advanced first-principles approaches for computing magnetic exchange parameters and magnon dispersions in NiO and MnO, all based on a common DFT+$U$ ground state with ab initio Hubbard $U$ values obtained from density-functional perturbation theory. Two methods extract exchange parameters directly: one via total-energy differences using the four-state mapping ($\Delta E$), and the other via the magnetic force theorem (MFT) using infinitesimal spin rotations. Magnon dispersions are then obtained from a Heisenberg Hamiltonian through linear spin-wave theory (LSWT). The third approach, time-dependent density-functional perturbation theory with $U$ (TDDFPT+$U$), yields magnon dispersions directly from the dynamical spin susceptibility, with exchange parameters fitted a posteriori, for comparison, via LSWT. Our results show that TDDFPT+$U$ and the Heisenberg model based on $\Delta E$-derived parameters align well with experimental neutron scattering data, whereas the MFT-based approach shows larger discrepancies, possibly due to some inherent approximations and limitations of the particular implementation used. This study benchmarks the accuracy of state-of-the-art first-principles techniques for spin-wave modeling and contributes to advancing reliable computational tools for the study and design of magnetic materials.

materialscloud

en: Materials Cloud

en: Comparative study of magnetic exchange parameters and magnon dispersions in NiO and MnO from first principles

dftu

display_name: dftu

four-state-mapping-analysis

display_name: four-state-mapping-analysis

heisenberg-j

display_name: heisenberg-j

linear-spin-wave-theory

display_name: linear-spin-wave-theory

magnetic-force-theorem

display_name: magnetic-force-theorem

magnon-dispersion

display_name: magnon-dispersion

mno

display_name: mno

nio

display_name: nio

quantum-espresso

display_name: quantum-espresso

spin-waves

display_name: spin-waves


en: This dataset contains the optimized structures of hole polarons in anatase and rutile TiO₂, calculated using the DFT+U and PBE0(α) functionals, with parameters tuned to satisfy the piecewise linearity condition. It also includes hole polaron migration paths in anatase and rutile, optimized using the nudged elastic band method at the DFT+U level. Additionally, pristine TiO₂ structures and an example input file for Quantum ESPRESSO are provided.

en: Migration of hole polarons in anatase and rutile TiO₂ through piecewise-linear functionals

envi

en: Environment

anatase

display_name: anatase

hopping

display_name: hopping

polaron

display_name: polaron

rutile

display_name: rutile

semiconductor

display_name: semiconductor


en:
Molecular dynamics (MD) provides insights into atomic-scale processes by integrating over time the equations that describe the motion of atoms under the action of interatomic forces. Machine learning models have substantially accelerated MD by providing inexpensive predictions of the forces, but they remain constrained to minuscule time integration steps, which are required by the fast time scale of atomic motion. In this work, we propose FlashMD, a method to predict the evolution of positions and momenta over strides that are between one and two orders of magnitude longer than typical MD time steps. We incorporate considerations on the mathematical and physical properties of Hamiltonian dynamics in the architecture, generalize the approach to allow the simulation of any thermodynamic ensemble, and carefully assess the possible failure modes of such a long-stride MD approach. We validate FlashMD's accuracy in reproducing equilibrium and time-dependent properties, using both system-specific and general-purpose models, extending the ability of MD simulation to reach the long time scales needed to model microscopic processes of high scientific and technological relevance.

This repository contains the scripts used for the training and analysis of the FlashMD models presented in the associated paper/preprint.

en: FlashMD: long-stride, universal prediction of molecular dynamics

atomistic-machine-learning

display_name: atomistic-machine-learning

long-stride-simulations

display_name: long-stride-simulations

ml-accerelated-simulations

display_name: ml-accerelated-simulations

molecular-dynamics

display_name: molecular-dynamics


en: Understanding electrical resistivity in metals remains a central challenge in quantifying charge transport at finite temperature. Current first-principles calculations based on the Boltzmann transport equation often match experiments, yet they almost always neglect the effect of thermal expansion and phonon anharmonicity. We show that both effects exert an opposite impact on electron–phonon coupling and on electrical resistivity. Thermal expansion enhances the coupling and leads to overestimation of resistivity, whereas anharmonic effects reduce it. By explicitly incorporating both effects, we establish a more complete description of resistivity in elemental metals, demonstrated here for Pb, Nb, and Al. This archive contains all our input, output files and the data.

en: Opposite impact of thermal expansion and phonon anharmonicity on the phonon-limited resistivity of elemental metals from first principles

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

transport

display_name: transport


en:
Dual function materials (DFMs) enable reactive carbon capture (RCC), an intensified approach to carbon dioxide capture and utilization for cost and energy input reductions. Yet, there is a fundamental lack of understanding of mechanisms around CO2 adsorption and subsequent conversion on these materials, hindering further development. Herein, we investigated several supported alkaline metal oxides for their CO2 adsorption characteristics to find that Na/Al2O3 had the highest CO2 adsorption capacity, accompanied by a variety of CO2 adsorption geometries as identified by in situ DRIFTS and computational modeling. The addition of catalytic metals (Ru, Pd) increased the adsorption capacity of Na/Al2O3 without altering binding modes. In the subsequent reactive desorption step, acetate and formate intermediates were observed. Notably, this mechanistic investigation identified that the formation of acetate species was unique to RCC on a DFM, as these species were not observed in co-fed hydrogenation over the DFM or RCC over a Na-free catalyst.

Herein, we share the structural CONTCARs obtained using periodic PBE calculations in VASP for hydrated (110) and (100) surfaces of gamma-alumina, Na2O-modified surface models for Na/Al2O3, subsequent structures of various surface carbonate (CO3*) and bicarbonate (HCO3*) configurations formed on each of these surfaces upon CO2 adsorption and adsorbed acetate and formate radicals on the surfaces.

en: Identifying mechanistic differences between co-fed CO2 hydrogenation and reactive CO2 capture using Ru and Pd dual function materials

alumina

display_name: alumina

density-functional-theory

display_name: density-functional-theory

dfm

display_name: dfm

dft

display_name: dft

dual-function-material

display_name: dual-function-material

rcc

display_name: rcc

reactive-carbon-capture

display_name: reactive-carbon-capture


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

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

ab-initio

display_name: ab-initio

diamond

display_name: diamond

total-energy

display_name: total-energy


en: We present a systematic density-functional theory study of the effects of strain on the structural and electronic properties in vanadium dioxide (VO2), with particular emphasis on its effect on the relative stability of the metallic rutile and the insulating monoclinic M1 phases. We consider various strain conditions that can be related to epitaxial strain present in VO2 films grown on different lattice planes. Our calculations confirm the dominant role of c axis strain, i.e., along the direction of the V--V dimerization in the M1 phase. Our analysis suggests that this effect stems primarily from the weakening of the lattice stiffness, with the hopping amplitude along the c axis playing a minor role. We also confirm that, in strain scenarios that deform the basal plane, the c axis strain still has a dominant effect on the phase stability.

en: Effects of strain on the stability of the metallic rutile and insulating M1 phases of vanadium dioxide

dftuv

display_name: dftuv

strain

display_name: strain


en:
This record contains the complete optimization trajectories, potential model, and analysis data for the Nudged Elastic Band (NEB) calculation of the ethylene + N$_2$O cycloaddition, a Grignard rearrangement, and a bicyclobutane  reaction. This dataset supports the accompanying manuscript "Two-dimensional RMSD projections for reaction path visualization and validation" and enables the full reproduction of the reported 2D RMSD landscapes and 1D energy profiles.

Transition state or minimum energy path finding methods constitute a routine component of the computational chemistry toolkit. Standard analysis involvestrajectories conventionally plotted in terms of the relative energy to the initial state against a cumulative displacement variable, or the image number.These dimensional reductions obscure structural rearrangements in high dimensions and are often history dependent. This precludes the ability tocompare optimization histories of different methods beyond the number of calculations, time taken, and final saddle geometry. We present a method mappingtrajectories onto a two-dimensional projection defined by a permutation corrected root mean square deviation from the reactant and product configurations. Energyis represented as an interpolated color-mapped surface constructed from all optimization steps using a gradient-enhanced Gaussian Process with the inversemultiquadric kernel, whose posterior variance contours delineate data-supported regions from extrapolated ones. A rotated coordinate frame decomposes the RMSDplane into reaction progress and orthogonal distance. We show the utility of the framework on a cycloaddition reaction, where a machine-learned potentialsaddle and density functional theory reference lie on comparable energy contours despite geometric displacements, along with the ratification of thevisualization for more complex reactions, a Grignard rearrangement, and a conrotatory bicyclobutane ring opening.

The provided data encompasses the entire computational workflow: from the pre-trained PET-OMAT machine-learned potential and initial endpoint geometries to the intermediate NEB optimization steps and the final converged reaction path.

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).

nudged-elastic-band

display_name: nudged-elastic-band

saddle-search-methods

display_name: saddle-search-methods

transition-states

display_name: transition-states

visualization

display_name: visualization


en: This study employed density functional theory in combination with the Materials Project database to systematically screen the unexplored phase space of Ag–In–I double salts. High-throughput screening of the Ag–In–I ternary phase space reveals several stable and metastable compounds belonging to two distinct structural families: tetrahedrally and octahedrally coordinated phases, with characteristic band gaps of approximately 3.0 eV and 2.0 eV, respectively. Although these materials are unlikely to function as efficient photovoltaic absorbers, their tunable band gaps and favorable stability make them promising candidates for charge transport and other optoelectronic applications. This dataset contains the input and output files for all DFT and hybrid functional calculations, the theoretically predicted potential of each material as absorber layer under different lighting conditions, as computed by spectroscopic limited maximum efficiency metric, as well as the structures of the uniquely identified polymorphs. All data are organized such that files are grouped according to the figures in the main text, along with selected files from the supplementary material.

materialscloud

en: Materials Cloud

en: Computational screening and discovery of Silver–Indium Halide double salts.

computational-materials-desing

display_name: computational-materials-desing

high-throughput

display_name: high-throughput

indoor-photovoltaics

display_name: indoor-photovoltaics

perovskite-inspired-materials

display_name: perovskite-inspired-materials

solar-cells

display_name: solar-cells


en: Nanostructured hybrid films composed of tungsten oxide (WOx) nanoclusters and vertically aligned carbon nanotubes (CNTs) were synthesized through a combination of chemical vapor deposition and supersonic cluster beam deposition. The use of a cluster source enabled the direct fabrication of oxygen-deficient, nonstoichiometric WOx nanoclusters, which decorated the CNT sidewalls with a characteristic “beaded necklace-style” morphology. Electrical resistance measurements under ethanol exposure in ultrahigh vacuum revealed a distinct behavior consistent with n-type conduction, unlike the intrinsic p-type behavior of pristine CNTs and of WOx films. This inversion is linked to the appearance of an interfacial charge transfer from the oxygen vacancies in the defective WOx nanoclusters to the CNTs, which injects electrons into the CNT network and shifting its Fermi level, thereby inverting the conduction type. Notably, this n-type conduction response remained stable even after prolonged air exposure. These results propose a viable approach to achieving air-stable n-type doping in CNT-based nanostructures.

en: Stable n-type conduction in WOx-CNT hybrid films

carbon-nanotubes

display_name: carbon-nanotubes

experimental

display_name: experimental

gas-sensing

display_name: gas-sensing

hybrid-nanostructures

display_name: hybrid-nanostructures

n-type-conductor

display_name: n-type-conductor

tungsten-oxide-nanoclusters

display_name: tungsten-oxide-nanoclusters


en: The 𝛽-pyrochlore is a charge ice – each tetrahedron of the pyrochlore lattice is occupied by two Ni2+ and two Cr3+ according to an ice rule, resulting in fully packed same-species loops with power-law length distribution. Using inelastic neutron scattering and numerical simulations, we show that the spin dynamics can be well described by a model with three antiferromagnetic interaction strengths, one for Ni-Ni, Cr-Ni, and Cr-Cr interactions. Because the strengths of the two intra-loop interactions are quite different, the fluctuations of each type of loop can be identified in the spectrum. therefore forms an algebraic loop liquid in which power-law cation correlations control the spin dynamics. Our parameters predict a transition to spin nematic order with associated arrest of dynamics at 𝑇𝑛≈0.55 K, while we observe a freezing transition at 𝑇𝑓=2.3K. Measurements of magnetisation, ac and non-linear susceptibility show that this is not a canonical spin glass transition so we associate it with the development of slow dynamics due to inter-loop nematic correlations.

en: Supporting data for the paper "Algebraic loop liquid in the pyrochlore CsNiCrF6"

envi

en: Environment

correlated-disorder

display_name: correlated-disorder

frustrated-magnetism

display_name: frustrated-magnetism

inelastic-neutron-scattering

display_name: inelastic-neutron-scattering


en: Topological effects arising from the Berry curvature lead to intriguing transport signatures in quantum materials. Two such phenomena are the chiral anomaly and nonlinear Hall effect (NLHE). A unified description of these transport regimes requires a quantitative treatment of both band topology and electron scattering. Here, we show accurate predictions of the magnetoresistance in topological semimetals and NLHE in noncentrosymmetric materials by solving the Boltzmann transport equation (BTE) with electron-phonon (e-ph) scattering and Berry curvature computed from first principles. We apply our method to study magnetotransport in a prototypical Weyl semimetal, TaAs, and the NLHE in strained monolayer WSe2, bilayer WTe2 and bulk BaMnSb2. In TaAs, we find a chiral contribution to the magnetoconductance which is positive and increases with magnetic field, consistent with experiments. We show that e-ph interactions can significantly modify the Berry curvature dipole and its dependence on temperature and Fermi level, highlighting the interplay of band topology and electronic interactions in nonlinear transport. The computed nonlinear Hall response in BaMnSb2 is in agreement with experiments. By adding the Berry curvature to first-principles transport calculations, our work advances the quantitative analysis of a wide range of linear and nonlinear transport phenomena in quantum materials.

en: Magnetotransport in topological materials and nonlinear Hall effect via first-principles electronic interactions and band topology

berry-curvature

display_name: berry-curvature

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

magnetotransport

display_name: magnetotransport

nonlinear-hall-effect

display_name: nonlinear-hall-effect

topological-materials

display_name: topological-materials


en: We demonstrate that non-diffusive, fluid-like heat transport, such as heat backflowing from cooler to warmer regions, can be induced, controlled, and amplified in extreme thermal conductors such as graphite and hexagonal boron nitride. We employ the viscous heat equations, i.e., the thermal counterpart of the Navier-Stokes equations in the laminar regime, to show with first-principles quantitative accuracy that a finite thermal viscosity yields steady-state heat vortices, and governs the magnitude of transient temperature waves. Finally, we devise strategies that exploit devices' boundaries and resonance to amplify and control heat hydrodynamics, paving the way for novel experiments and applications in next-generation electronic and phononic technologies.

en: Viscous heat backflow and temperature resonances in extreme thermal conductors

graphite

display_name: graphite

heat-hydrodynamics

display_name: heat-hydrodynamics

temperature-resonances

display_name: temperature-resonances

thermal-viscosity

display_name: thermal-viscosity


en: Photocatalytic water splitting has attracted considerable attention for renewable energy production. Since the first reported photocatalytic water splitting by titanium dioxide, this material remains one of the most promising photocatalysts, due to its suitable band gap and band-edge positions. However, predicting both of these properties is a challenging task for existing computational methods. Here we show how Koopmans spectral functionals can accurately predict the band structure and level alignment of rutile, anatase, and brookite TiO₂ using a computationally efficient workflow that only requires (a) a DFT calculation of the photocatalyst/vacuum interface and (b) a Koopmans spectral functional calculation of the bulk photocatalyst. The success of this approach for TiO₂ suggests that this strategy could be deployed for assessing the suitability of novel photocatalyst candidates. This record contains for each polymorph: (a) a calculation of a slab; (b) KI and pKIPZ band structure calculation of the bulk system.

en: Predicting the suitability of photocatalysts for water splitting using Koopmans spectral functionals: the case of TiO₂ polymorphs

electronic-structure

display_name: electronic-structure

koopmans-spectral--functionals

display_name: koopmans-spectral--functionals

orbital-density-dependent-functionals

display_name: orbital-density-dependent-functionals

photocatalysis

display_name: photocatalysis

water-splitting

display_name: water-splitting


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

en: Electronic structure and dynamical correlations in antiferromagnetic BiFeO3

density-functional-theory

display_name: density-functional-theory

dynamical-correlations

display_name: dynamical-correlations


en:
We use the self-consistent harmonic approximation (SSCHA) with machine learning interatomic potentials to calculate the effect of 18O substitution on the properties of quantum paraelectric SrTiO3 (STO). We find that calculations including both quantum and anharmonic effects are able to reproduce the experimentally observed isotope effect, in which the replacement of 16O by 18O induces the ferroelectric state, and we demonstrate that the ferroelectric phase transition in ST18O can be reproduced in a purely displacive description. We calculate the ferroelectric soft mode frequency as a function of volume, lattice parameters, and temperature for ST16O and ST18O and find that the phase space in which ST16O shows quantumparaelectric behavior, while ST18O becomes ferroelectric, is narrow. Our Letter shows that machine learning interatomic potentials enable temperature-dependent simulations that include quantum and anharmonic phonon effects and that quantitative prediction of the phase diagram in the case of STO is limited by the accuracy of the underlying electronic structure method.

This dataset contains the inputs/and outputs from the SSCHA calculations.

materialscloud

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

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

en: Machine-learning-enabled ab initio study of quantum phase transitions in SrTiO3

ferroelectric

display_name: ferroelectric

isotope-effect

display_name: isotope-effect

quantum-paraelectric

display_name: quantum-paraelectric