en:
In this paper, we propose an extension to the approach of [Xi, C; et al. J. Chem. Theory Comput. 2022, 18, 6878] to calculate ion solvation free energies from first-principles (FP) molecular dynamics (MD) simulations of a hybrid solvation model. The approach is first re-expressed within the quasi-chemical theory of solvation. Then, to allow for longer simulation times than the original first-principles molecular dynamics approach and thus improve the convergence of statistical averages at a fraction of the original computational cost, a machine-learned (ML) energy function is trained on FP energies and forces and used in the MD simulations. The ML workflow and MD simulation times (≈200 ps) are adjusted to converge the predicted solvation energies within a chemical accuracy of 0.04 eV. The extension is successfully benchmarked on the same set of alkaline and alkaline-earth ions.
The record includes all molecular-dynamics trajectories, energies and forces used to obtain the solvation energies of alkaline and alkaline-earth ions in water, as reported in Table 2 of referenced paper.

en: Solvation free energies from machine learning molecular dynamics

envi

en: Environment

solvation

display_name: solvation


$oid: OBSOLETE

en: Twinning is an important deformation mode in plastically deformed hexagonal close-packed materials. The extremely high twin growth rates at the nanoscale make atomistic simulations an attractive method for investigating the role of individual twin/matrix interfaces such as twin boundaries and basal-prismatic interfaces in twin growth kinetics. Unfortunately, there is no single framework that allows researchers to differentiate such interfaces automatically, neither in experimental in-situ transmission electron microscopy analysis images nor in atomistic simulations. Moreover, the presence of alloying elements introduces substantial noise to local atomic environments, making it nearly impossible to identify which atoms belong to which interface. Here, with the help of advanced machine learning methods, we provide a proof-of-concept way of using the local stress field distribution as an indicator for the presence of interfaces and for determining their types. We apply such an analysis to the growth of twin embryos in Mg-10 at.% Al alloys under constant stress and constant strain conditions, corresponding to two extremes of high and low strain rates, respectively. We discover that the kinetics of such growth is driven by high-energy basal-prismatic interfaces, in line with our experimental observations for pure Mg. The data provided here allows for reproducing findings from our work.

en: Machine learning of twin/matrix interfaces from local stress field

materialscloud

approval_status: approved

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

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

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

fr: Environnement

it: Ambiente

machine-learning

display_name: machine-learning

magnesium-twinning

display_name: magnesium-twinning

molecular-dynamics

display_name: molecular-dynamics


en: Antiferromagnets are normally thought of as materials with compensated magnetic sublattices. This adds to their technological advantages but complicates readout of the antiferromagnetic state. We demonstrate theoretically the existence of a Dzyaloshinskii-Moriya interaction (DMI) which is determined by the magnetic symmetry classes of Cr₂O₃ surfaces with an in-plane magnetic easy axis. The DMI explains a previously predicted out-of-plane magnetization at the nominally compensated surfaces of chromia, leading to a surface-localized canted ferrimagnetism. This is in agreement with magnetotransport measurements and with density functional theory predictions which further allow us to quantify the strength of DMI. The temperature dependence of the transversal resistance for these planes shows distinct behavior in comparison with that of the Cr₂O₃  plane, which we attribute to the influence of DMI. Our work provides a framework to analyze surface-driven phenomena in antiferromagnets, and motivates the use of nominally compensated chromia surfaces for antiferomagnetic spintronics and magnonics. Here, we provide the data for density functional theory and Monte Carlo calculations. Micromagnetic and experimental data for this joint experiment-theory collaboration are archived separately according to EU regulations.

en: Surface-symmetry-driven Dzyaloshinskii-Moriya interaction and canted ferrimagnetism in collinear magnetoelectric antiferromagnet Cr₂O₃

density-functional-theory

display_name: density-functional-theory

dzyaloshinskii-moriya-interactions

display_name: dzyaloshinskii-moriya-interactions

magnetoelectrics

display_name: magnetoelectrics

magnetotransport

display_name: magnetotransport

micromagnetics

display_name: micromagnetics

surface-magnetization

display_name: surface-magnetization


en: We present a first-principles study of the low-temperature rhombohedral phase of BaTiO₃ using Hubbard-corrected density-functional theory. By employing density-functional perturbation theory, we compute the onsite Hubbard U for Ti(3d) states and the intersite Hubbard V between Ti(3d) and O(2p) states. We show that applying the onsite Hubbard U correction alone to Ti(3d) states proves detrimental, as it suppresses the Ti(3d)-O(2p) hybridization and drives the system towards a cubic phase. Conversely, when both onsite U and intersite V are considered, the localized character of the Ti(3d) states is maintained, while also preserving the Ti(3d)-O(2p) hybridization, restoring the rhombohedral phase of BaTiO₃. The generalized PBEsol+U+V functional yields good agreement with experimental results for the band gap and dielectric constant, while the optimized geometry is slightly less accurate compared to PBEsol. Zone-center phonon frequencies and Raman spectra are found to be significantly influenced by the underlying geometry. PBEsol and PBEsol+U+V provide satisfactory agreement with the experimental Raman spectrum when the PBEsol geometry is used, while PBEsol+U Raman spectrum diverges strongly from experimental data highlighting the adverse impact of the U correction alone in BaTiO₃. Our findings underscore the promise of the extended Hubbard PBEsol+U+V functional with first-principles U and V for the investigation of other ferroelectric perovskites with mixed ionic-covalent interactions.

en: Understanding the role of Hubbard corrections in the rhombohedral phase of BaTiO₃

cscs

display_name: cscs

dftu

display_name: dftu

dftuv

display_name: dftuv

phonons

display_name: phonons

raman-spectra

display_name: raman-spectra


en: The mechanisms underlying the reaction between carbon monoxide (CO) and activated dioxygen on metal oxide substrates to produce CO₂ remain poorly understood, particularly regarding the role of oxygen vacancies and the nature of the activated O₂ adsorbate. In this study, we present experimental findings from infrared reflection-absorption spectroscopy on a model system of bulk monocrystalline CeO₂(111). Contrary to expectations, exposing the reduced surface to dioxygen (O₂) at 80 K does not yield activated oxygen species, such as superoxo or peroxo. Notably, in the presence of adsorbed CO, an unexpected low-temperature oxidation reaction occurs, consuming CO while oxidizing the CeO₂ substrate. Since a direct reaction between impinging O₂ and adsorbed CO is unlikely at these low temperatures, a novel mechanism is proposed. Extensive spin-polarized density functional theory (DFT) calculations reveal that oxygen vacancies play a critical role in this low-temperature CO oxidation. Initially located in the subsurface region (Vss), these vacancies migrate to the surface (Vs) via a concerted interaction with coadsorbed CO and O₂, leading to O₂ activation and the formation of superoxo or peroxo species. Detailed analysis identifies key reaction intermediates and quantifies their adsorption energies and activation barriers. Our findings suggest that the peroxo-mediated pathway, with its lower activation barrier, is more favorable for CO oxidation at low temperatures compared to the carbonate pathway. This study provides valuable insights into the dynamic role of subsurface oxygen vacancies in the activation of gaseous O₂ and CO oxidation mechanisms on CeO₂.

en: Synergistic effects in low-temperature CO oxidation on cerium oxide surfaces

ceria

display_name: ceria

co-oxidation

display_name: co-oxidation

dft

display_name: dft

peroxo-and-superoxo

display_name: peroxo-and-superoxo


en: An extensive dataset consisting of adsorption energies of pernicious impurities present in biomass upgrading processes on common catalysts and support materials has been generated. This work aims to inform catalyst and process development for the conversion of biomass-derived feedstocks to fuels and chemicals. A high-throughput workflow was developed to execute density functional theory calculations for a diverse set of atomic (Al, B, Ca, Cl, Fe, K, Mg, Mn, N, Na, P, S, Si, Zn) and molecular (COS, H₂S, HCl, HCN, K₂O, KCl, NH₃) species on 35 unique surfaces for transition-metal (Ag, Au, Co, Cu, Fe, Ir, Ni, Pd, Pt, Re, Rh, Ru) and metal-oxide (Al₂O₃, MgO, anatase-TiO₂, rutile-TiO₂, ZnO, ZrO₂) catalysts and supports. Approximately 3,000 unique adsorption geometries were obtained. The data record includes structure and calculation output files for each unique adsorbate geometry on each surface.

en: High-throughput dataset of impurity adsorption on common catalysts in biomass upgrading applications

high-throughput

display_name: high-throughput

vasp

display_name: vasp


en: Predicting the electrical properties of organic molecular crystals (OMCs) is challenging due to their complex crystal structures and electron-phonon (e-ph) interactions. Charge transport in OMCs is conventionally categorized into two limiting regimes – band transport, characterized by weak e-ph interactions, and charge hopping due to localized polarons formed by strong e-ph interactions. However, between these two limiting cases there is a less well understood intermediate regime where polarons are present but transport does not occur via hopping. Here we show a many-body first-principles approach that can accurately predict the carrier mobility in OMCs in the intermediate regime and shed light on its microscopic origin. Our approach combines a finite-temperature cumulant method to describe strong e-ph interactions with Green-Kubo transport calculations. We apply this parameter-free framework to naphthalene crystal, demonstrating electron mobility predictions within a factor of 1.5–2 of experiment between 100–300 K. Our analysis reveals that electrons couple strongly with both inter- and intramolecular phonons in the intermediate regime, as evidenced by the formation of a broad polaron satellite peak in the electron spectral function and the failure of the Boltzmann transport equation (BTE). Our study advances quantitative modeling of charge transport in complex organic crystals. This dataset contains input and output files required to reproduce the BTE results presented in our work.

en: Intermediate polaronic charge transport in organic crystals from a many-body first-principles approach

boltzmann-equation

display_name: boltzmann-equation

carrier-mobility

display_name: carrier-mobility

charge-transport

display_name: charge-transport

electron-phonon-coupling

display_name: electron-phonon-coupling

electron-phonon-scattering-rate

display_name: electron-phonon-scattering-rate

first-principles-calculations

display_name: first-principles-calculations

molecular-crystal

display_name: molecular-crystal

naphthalene

display_name: naphthalene

organic-crystal

display_name: organic-crystal

polaron

display_name: polaron


en:
Metal-organic frameworks (MOF) and covalent organic frameworks (COFs) are promising nanocarriers for targeted drug delivery. For their uptake and release, non-covalent interactions between the framework and the drugs play a fundamental role. However, an in-depth understanding of how different functional groups affect these interactions is still lacking. Using a multilevel approach combining molecular docking and density functional theory, we show in the publication associated with this data how computational modeling can be exploited to gain information on the interplay between functionalization and drug delivery features. We find that functional groups significantly impact the strength of the host-guest interactions. Moreover, the interaction strength qualitatively correlates with drug release data from experimental literature, providing a link between framework structure and release properties. The results of our study facilitate the customization of non-covalent interactions between MOFs/COFs and drugs and give direction on how to rationally design nanocarriers for sustaining the drug release in the body over longer times.
The data deposited on the Materials Cloud Archive contains the inputs and outputs of the molecular docking and DFT simulations.

en: Engineering host-guest interactions in organic framework materials for drug delivery

covalent-organic-frameworks

display_name: covalent-organic-frameworks

drug-delivery

display_name: drug-delivery

metal-organic-frameworks

display_name: metal-organic-frameworks


en: An ab initio  study of the coupled electrons and phonon transport properties of MoS2-hBN devices. A comparison of the device characteristics when hBN is treated as a perfectly insulating, non vibrating layer and one where it is included in the DFT together with MoS2.

en: Influence of the hBN dielectric layers on the quantum transport properties of MoS2 transistors.

envi

en: Environment

ab-initio

display_name: ab-initio

quantum-transport

display_name: quantum-transport

thermal-properties

display_name: thermal-properties


en: This dataset includes the full computational workflows of a proof-of-concept study of various possible mechanisms (standard and bifunctional ones) for oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) on exfoliated NiOOH as electrocatalyst, including active edge sites (M5) and hydrogen acceptor sites in the same model system. Furthermore, explicit water is included in the model to describe the equilibration of the M-OOH species to M-OOH/eq, a crucial step that enables a bifunctional route to be operative. Additionally, different single Fe-dopant positions (M1, M2, M3, M4, M5, M6 and M7) are considered and four different reaction schemes (S1, S2, S3 and S4) are studied for the OER and the reverse ORR process. The results are relevant in alkaline conditions, where the studied model systems are stable. Certain Fe-dopant positions result in active Ni-edge sites with very low overpotentials provided that water is present within the model system.

en: Oxygen evolution and reduction on Fe‑doped NiOOH

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).

electrochemical-overpotential

display_name: electrochemical-overpotential

mixed-metal-oxyhydroxides

display_name: mixed-metal-oxyhydroxides

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction

oxygen-reduction-reaction

display_name: oxygen-reduction-reaction


en: This work presents an application of the strain-fluctuation method, exploiting the fluctuations of the strain from extensive first-principles molecular dynamics simulations in the isobaric-isothermal ensemble, to the study of the elastic tensors of superionic materials. As the superionic materials for solid-state electrolyte applications usually do not have well-defined ground-state configurations, it is challenging to apply the static methods to calculate the elastic tensors of these materials. Instead, the strain-fluctuation method captures the dynamical nature of the elastic response of these materials and is a promising approach to studying their elastic properties. In this work: a protocol is presented and documented to extract the elastic the elastic moduli and their statistical errors from the molecular dynamics trajectories (open-source code available at https://github.com/materzanini); results for two benchmark superionic materials (Li₁₀GeP₂S₁₂ and Li₁₀GeP₂O₁₂) are given; for these superionic materials, a comparison to static methods is also provided, showing that static methods overestimate the moduli with respect to the correct dynamical treatment by ~25-50%.

en: Solids that are also liquids: elastic tensors of superionic materials

materialscloud

en: Materials Cloud

big-map

display_name: big-map

cscs

display_name: cscs

elastic-constants

display_name: elastic-constants

first-principles-molecular-dynamics

display_name: first-principles-molecular-dynamics

lgpo

display_name: lgpo

lgps

display_name: lgps

snsf

display_name: snsf

solid-state-electrolytes

display_name: solid-state-electrolytes

statistical-sampling

display_name: statistical-sampling

stress-strain

display_name: stress-strain

superionics

display_name: superionics


en: This dataset includes the full computational workflows of a density functional theory based thermodynamics model for the overpotential of the oxygen evolution reaction (OER) on an oxidized palladium surface. The model assumes an oxygen bridge vacancy as an active site on the 110 surface of the tetragonal PdO2 (rutile type structure). The critical OER potential has been computed with variation of the Fe modifyer type, either dopant or adsorbate, and modifyer position. Furthermore, an alternative bifunctional pathway of OER has been considered by adding an H atom to an auxiliary O-bridge site from which the proton−electron pair for second OER reaction step is released rather than from the hydroxylated active site. The computed OER overpotential on the Fe-free surface via this bifunctional route is 0.42 V. A substitution of Pd with Fe directly at this active site further reduces the calculated OER overpotential, over the same route, to 0.35 V. This 70 mV decrease in overpotential is in good agreement with the experimentally measured decrease of 60 mV in the onset potential.

en: Oxygen evolution reaction by a palladium foil in the presence of iron

iron-dopant

display_name: iron-dopant

palladium-dioxide

display_name: palladium-dioxide


en:
Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident estimates in the context of overparametrized neural networks. Several methods, ranging from temperature scaling to different Bayesian treatments of neural networks, have been proposed to mitigate overconfidence, most often supported by the numerical observation that they yield better calibrated uncertainty measures. In this work, we provide a sharp comparison between popular uncertainty measures for binary classification in a mathematically tractable model for overparametrized neural networks: the random features model. We discuss a trade-off between classification accuracy and calibration, unveiling a double descent like behavior in the calibration curve of optimally regularized estimators as a function of overparametrization. This is in contrast with the empirical Bayes method, which we show to be well calibrated in our setting despite the higher generalization error and overparametrization.
This record provides the code to reproduce the numerical experiments of the related paper "On double-descent in uncertainty quantification in overparametrized models".

en: On double-descent in uncertainty quantification in overparametrized models (code)

neural-networks

display_name: neural-networks

numerical-simulation

display_name: numerical-simulation

uncertainty-quantification

display_name: uncertainty-quantification


en: This work explores hydrogen-doped samarium nickelate from first-principles calculations. At a concentration of 1/4 hydrogen per formula unit we find a number of polar states due to the presence of the interstitial hydrogen. Physically, the polarization of the material arises from the localization of the hydrogen's valence electron on a nearby nickel-oxygen octahedron leading to a local dipole. Due to the inherent tilt pattern present in samarium nickelate, a perovskite with an a-a-c+ tilt pattern, there is an insurmountable energy barrier to switch a given polar state the structure related by inversion symmetry. Instead, we use an in-plane epitaxial constraint to tune the total energy of two structures to be equal. These two structures, unrelated by a cell-symmetry operation, have similar a similar position of the interstitial hydrogen atom, but the valence electron localizes on a different nickel-oxygen octahedron leading to different polarizations. We find that there is a surmountable energy barrier to switch between these two structures.

en: "Fraternal-twin" ferroelectricity:  competing polar states in hydrogen-doped samarium nickelate from first principles

charge-localization

display_name: charge-localization

ferroelectricty

display_name: ferroelectricty

rare-earth-nickelates

display_name: rare-earth-nickelates


en: The determination of transport coefficients through the time-honoured Green-Kubo theory of linear response and equilibrium molecular dynamics requires significantly longer simulation times than those of equilibrium properties, while being further hindered by the lack of well-established data-analysis techniques to evaluate the statistical accuracy of the results. Leveraging recent advances in the spectral analysis of the current time series associated to molecular trajectories, we introduce a new method to estimate the full (diagonal as well as off-diagonal) Onsager matrix of transport coefficients from a single statistical model. This approach, based on the knowledge of the statistical distribution of the Onsager-matrix samples in the frequency domain, unifies the evaluation of diagonal (conductivities and viscosities) and off-diagonal (e.g., thermoelectric) transport coefficients within a comprehensive framework, significantly improving the reliability of transport coefficient estimation for materials ranging from molten salts to solid-state electrolytes. We validate the accuracy of this method against existing approaches using benchmark data on molten cesium fluoride and liquid water, and conclude our presentation with the computation of various transport coefficients of the Li₃PS₄ solid-state electrolyte.

en: Transport coefficients from equilibrium molecular dynamics

molecular-dynamics

display_name: molecular-dynamics

thermoelectricity

display_name: thermoelectricity

transport

display_name: transport


en: This study combines machine learning (ML) and high-throughput calculations to uncover new ternary electrides in the A₂BC₂ family of compounds with the P4/mbm space group. Starting from a library of 214 known A₂BC₂ phases, density-functional theory calculations were used to compute the maximum value of the electron localization function, indicating that 42 are potential electrides. A model was then trained on this dataset and used to predict the electride behaviour of 14,437 hypothetical compounds generated by structural prototyping. Then, the stability and electride features of the 1254 electride candidates predicted by the model were carefully checked by high-throughput calculations.

en: Machine learning-accelerated discovery of A₂BC₂ ternary electrides with diverse anionic electron densities

density-functional-theory

display_name: density-functional-theory

electrides

display_name: electrides

machine-learning

display_name: machine-learning


en:
A comprehensive study of the electronic and structural phase transition from 1T` to Td in the bulk Weyl semimetal Mo1-xWxTe2 at different doping concentrations has been carried out using time-of-flight momentum microscopy (including circular and linear dichroism), X-ray photoelectron spectroscopy, X-ray photoelectron diffraction, X-ray diffraction (XRD), angle-resolved Raman spectroscopy, transport measurements (including longitudinal elastoresistance), density functional theory (DFT) and Kikuchi pattern calculations. High-resolution, angle-resolved photoemission spectroscopy at 20 K reveals surface electronic states, which are indicative for topological Fermi arcs. Their dispersion agrees with the position of Weyl points predicted by DFT calculations based on the precise crystal structure of our samples obtained from XRD measurements. Raman spectroscopy confirms the inversion symmetry breaking for the Td-phase, which is a necessary condition for the emergence of topological states. Transport measurements show that increasing the doping concentration from 2 to 9 % leads to an increase in the temperature of the phase transition from 1T` to Td from 230 K to 270 K. Magnetoresistance and longitudinal elastoresistance show significantly increased values in the Td-phase.
This dataset contains the raw data discussed in the manuscript with the same title.

en: Doping-Induced Electronic and Structural Phase Transition in the Bulk Weyl semimetal Mo1-xWxTe2

envi

en: Environment

alloy

display_name: alloy

cpa

display_name: cpa

dft

display_name: dft

kkr

display_name: kkr

tmdc

display_name: tmdc

weyl-semimetal

display_name: weyl-semimetal


en: Two-dimensional (2D) materials are among the most promising candidates for beyond silicon electronic and optoelectronic applications. Recently, their recognized importance, sparked a race to discover and characterize new 2D materials. Within few years the number of experimentally exfoliated or synthesized 2D materials went from a couple of dozens to few hundreds while the number theoretically predicted compounds reached a few thousands. In 2018 we first contributed to this effort with the identification of 1825 compounds that are either easily (1036) or potentially (789) exfoliable from experimentally known 3D compounds. In the present work we report on the new materials recently added to the 2D-portfolio thanks to the extension of the screening to an additional experimental database (MPDS) as well as the most up-to-date versions of the two databases (ICSD and COD) used in our previous work. This expansion led to the discovery of an additional 1252 unique monolayers bringing the total to 3077 compounds and, notably, almost doubling the number of easily exfoliable materials (2004). Moreover, we optimized the structural properties of all the materials (regardless of their binding energy or number of atoms in the unit cell) as isolated mono-layer and explored their electronic band structure. This archive entry contains the database of 2D materials in particular it contains the structural parameters for all the 3077 structures of the global Material Cloud 2D database as extracted from their bulk 3D parent, 2710 optimized 2D structures and 2345 electronic band structure together with the provenance of all data and calculations as stored by AiiDA.

en: The Materials Cloud 2D database (MC2D)

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).

electronic-structure

display_name: electronic-structure

high-throughput

display_name: high-throughput


en: Chiral ligands are important components in asymmetric homogeneous catalysis, but their synthesis and screening can be both time-consuming and resource-intensive. Data-driven approaches, in contrast to screening procedures based on intuition, have the potential to reduce the time and resources needed for reaction optimization by more rapidly identifying an ideal catalyst. These approaches, however, are often non-transferable and cannot be applied across different reactions. To overcome this drawback, we introduce a general featurization strategy for bidentate ligands that is coupled with an automated feature selection pipeline and Bayesian ridge regression to perform multivariate linear regression modeling. This approach, which is applicable to any reaction, incorporates electronic, steric, and topological features (rigidity/flexibility, branching, geometry, constitution) and is well-suited for early-stage ligand optimization. Using only small datasets, our workflow capably predicts the enantioselectivity of four metal-catalyzed asymmetric reactions. Uncertainty estimates provided by Bayesian ridge regression permit the use of Bayesian optimization to efficiently explore pools of prospective new ligands. Finally, we constructed the BDL-Cu-2023 dataset, comprised of 312 bidentate ligands extracted from the CSD, and screened it with this procedure to identify promising ligand candidates for a challenging asymmetric oxy-alkynylation reaction.

en: Reaction-agnostic featurization of bidentate ligands for Bayesian ridge regression of enantioselectivity

materialscloud

en: Materials Cloud

bidentate-ligands

display_name: bidentate-ligands

catalysis

display_name: catalysis

epfl

display_name: epfl

homogenous-catalysis

display_name: homogenous-catalysis

ligands

display_name: ligands

nccr-catalysis

display_name: nccr-catalysis


en: The C-F activation is one of the important processes in chemical synthesis. Here, we studied the hydrodefluorination of PhCF3 with SiMe2Ph-H catalyzed by Ni(0) complexes. The mechanisms involve three main steps: C-F bond cleavage of PhCF3 on the nickel complex, transmetalation of Ni-F with SiMe2Ph-H to form a nickel hydride complex, and C-H reductive elimination of PhCF2H. We performed density functional calculations on nickel complexes with thirty carbene and phosphine ligands to obtain the relative free energy profiles. Then, linear free energy scaling relationships were determined and molecular volcano plots were constructed. To accurately describe catalytic activity, we found that multiple reference states must be considered.  Thus, the concept of "reference-generalized volcano plots (RGVPs)" was introduced to assist with the selection of the appropriate reference state to determine catalytic activity. Our regression models indicate that electronic properties of ligands significantly influence energy descriptors more than steric size, although steric hindrance affects reference state stability. Optimal ligands are highly electron-donating with small-to-moderate steric sizes. This study highlights the importance of selecting appropriate ligands and metal centers to optimize catalytic activity. Examples of input files for density functional calculations in Gaussian16 to obtain relative free energies for all intermediates and transition states, the spreadsheet tables including the calculated relative free energies for generating LFESRs and volcano plots, and all optimized Cartesian coordinates are provided.

en: Computational Design of Transition Metal Catalysts for Hydrodefluorination of Trifluoromethylarenes using Hydrosilane

carbenes

display_name: carbenes

volcano-plot

display_name: volcano-plot


en: A catalyst possessing a broad substrate scope, in terms of both turnover and enantioselectivity, is sometimes called "general". Despite their great utility in asymmetric synthesis, truly general catalysts are difficult or expensive to discover via traditional high-throughput screening and are, therefore, rare. Existing computational tools accelerate the evaluation of reaction conditions from a pre-defined set of experiments to identify the most general ones, but cannot generate entirely new catalysts with enhanced substrate breadth. For these reasons, we report an inverse design strategy based on the open-source genetic algorithm NaviCatGA and on the OSCAR database of organocatalysts to simultaneously probe the catalyst and substrate scope and optimize generality as primary target. We apply this strategy to the Pictet–Spengler condensation, for which we curate a database of 820 reactions, used to train statistical models of selectivity and activity. Starting from OSCAR, we define a combinatorial space of millions of catalyst possibilities, and perform evolutionary experiments on a diverse substrate scope that is representative of the whole chemical space of tetrahydro-β-carboline products. While privileged catalysts emerge, we show how genetic optimization can address the broader question of generality in asymmetric synthesis, extracting structure–performance relationships from the challenging areas of chemical space.

en: A genetic optimization strategy with generality in asymmetric organocatalysis as primary target

enantioselectivity

display_name: enantioselectivity

generality

display_name: generality

genetic-optimization

display_name: genetic-optimization

machine-learning

display_name: machine-learning

organic-molecules

display_name: organic-molecules

organocatalysis

display_name: organocatalysis


en: The development of spintronic devices demands the existence of materials with some kind of spin splitting (SS). In this work, we have built a database of ab initio calculated SS in 2D materials. More than that, we propose a workflow for materials design integrating an inverse design approach and a Bayesian inference optimization.  We use the prediction of SS prototypes for spintronic applications as an illustrative example of the proposed workflow. The prediction process starts with the establishment of the design principles (the physical mechanism behind the target properties), that are used as filters for materials screening, and followed by density functional theory (DFT) calculations. Applying this process to the C2DB database, we identify and classify 315 2D materials according to SS type at the valence and/or conduction bands. The Bayesian optimization captures trends that are used for the rationalized design of 2D materials with the ideal conditions of band gap and SS for potential spintronics applications. This repository then contains the main source of data generated in this work, which encompasses full information regarding the materials structure and band structure calculations results, and a database of all the identified spin splittings for these compounds, available in multiple formats.

en: High throughput inverse design and Bayesian optimization of functionalities: spin splitting in two-dimensional compounds

bayesian-optimization

display_name: bayesian-optimization

spin-splitting

display_name: spin-splitting

spintronics

display_name: spintronics


en: Raman spectroscopy is a widely-used non-destructive material characterization method, which provides information about the vibrational modes of the material and therefore of its atomic structure and chemical composition. Raman spectra can be simulated using atomistic first-principles methods but these are computationally demanding and thus the existing databases of computational Raman spectra are fairly small. We developed an optimized workflow to efficiently calculate the Raman tensors, from which the Raman spectra can be straightforwardly simulated. The workflow was benchmarked and validated by comparison to experiments and previous computational methods for select technologically relevant material systems. Using the workflow, we performed high-throughput calculations for a large set of materials (5099) belonging to many different material classes, and collected the results to a database.

en: High-throughput computation of Raman spectra from first principles

density-functional-theory

display_name: density-functional-theory

high-throughput

display_name: high-throughput

raman-tensors

display_name: raman-tensors


en: Physics-inspired molecular representations are the cornerstone of similarity-based learning applied to solve chemical problems. Despite their conceptual and mathematical diversity, this class of descriptors shares a common underlying philosophy: they all rely on the molecular information that determines the form of the electronic Schrödinger equation. Existing representations take the most varied forms, from non-linear functions of atom types and positions to atom densities and potential, up to complex quantum chemical objects directly injected into the ML architecture. In this work, we present the Spectrum of Approximated Hamiltonian Matrices (SPAᴴM) as an alternative pathway to construct quantum machine learning representations through leveraging the foundation of the electronic Schrödinger equation itself: the electronic Hamiltonian. As the Hamiltonian encodes all quantum chemical information at once, SPAᴴM representations not only distinguish different molecules and conformations, but also different spin, charge, and electronic states. As a proof of concept, we focus here on efficient SPAᴴM representations built from the eigenvalues of a hierarchy of well-established and readily-evaluated "guess" Hamiltonians. These SPAᴴM representations are particularly compact and efficient for kernel evaluation and their complexity is independent of the number of different atom types in the database

en: SPAᴴM: the spectrum of approximated hamiltonian matrices representations

epfl

display_name: epfl

erc

display_name: erc

guess-hamiltonians

display_name: guess-hamiltonians

molecular-representation

display_name: molecular-representation

snsf

display_name: snsf

spahm

display_name: spahm


en: Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we describe a Bayesian active learning framework for autonomous ``on-the-fly'' training of fast and accurate reactive many-body force fields during molecular dynamics simulations. At each time step, predictive uncertainties of a sparse Gaussian process are evaluated to automatically determine whether additional ab initio training data are needed. We introduce a general method for mapping trained kernel models onto equivalent polynomial models whose prediction cost is much lower and independent of the training set size. As a demonstration, we perform direct two-phase simulations of heterogeneous H2 turnover on the Pt(111) catalyst surface at chemical accuracy. The model trains itself in three days and performs at twice the speed of a ReaxFF model, while maintaining much higher fidelity to DFT and excellent agreement with experiment.

en: Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt

envi

en: Environment

molecular-dynamics

display_name: molecular-dynamics


en:
The effects of the finite size of the simulation box in equilibrium molecular dynamics simulations are investigated for prototypical superionic conductors of different types, namely the fluorite-structure materials PbF2, CaF2, and UO2 (type II), and the alpha phase of AgI (type I). Largely validated empirical force-fields are employed to run ns-long simulations and extract general trends for several properties, at increasing size and in a wide temperature range.
This work shows that, for the considered type-II superionic conductors, the diffusivity dramatically depend on the system size and that the superionic regime is shifted to larger temperatures in smaller cells. Furthermore, only simulations of several hundred atoms are able to capture the experimentally-observed, characteristic change in the activation energy of the diffusion process, occurring at the order-disorder transition to the superionic regime. Finite-size effects on ion diffusion are instead much weaker in alpha-AgI. The thermal conductivity is found generally smaller for smaller cells, where the temperature-independent (Allen-Feldman) regime is also reached at significantly lower temperatures. The finite-size effects on the thermal motion of the non-mobile ions composing the solid matrix follow the simple law which holds for solids.
In this record, I collect the input files employed to run the simulations, the data obtained from the latter and the Jupiter Notebook used to analyze the data.

en: Investigating finite-size effects in computer simulations of superionic materials

materialscloud

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

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

marie-curie-fellowship

display_name: marie-curie-fellowship

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

transport

display_name: transport


en: We present a density-functional theory (DFT) study of the structural, electronic, and chemical bonding behavior in germanium (Ge)-doped vanadium dioxide (VO2). Our motivation is to explain the reported increase of the metal-insulator transition temperature under Ge doping and to understand how much of the fundamental physics and chemistry behind it can be captured at the conventional DFT level. We model doping using a supercell approach, with various concentrations and different spatial distributions of Ge atoms in VO2. Our results suggest that the addition of Ge atoms strongly perturbs the high-symmetry metallic rutile phase and induces structural distortions that partially resemble the dimerization of the experimental insulating structure. Our work, therefore, hints at a possible explanation of the observed increase in transition temperature under Ge doping, motivating further studies into understanding the interplay of structural and electronic transitions in VO2.

en: Influence of germanium substitution on the structural and electronic stability of the competing vanadium dioxide phases

materialscloud

en: Materials Cloud

dft

display_name: dft

doping

display_name: doping


en: This dataset includes the computational workflows of a density functional theory based study of the oxygen evolution reaction (OER) on a manganese oxide catalyst in presence and absence of iron dopant. The thermodynamic OER overpotential has been computed by using a surface slab model based on a layered birnessite bulk structure of MnO₂ considering supercells with two and four MnO₂ units and by varying the intercalation with KOH, the amount of Fe dopant and the dopant positions. In addition, the dependence of the oxidation state of the active site atoms (either Mn or Fe) on the directly bound OER intermediate species, has been investigated. The results suggest a decrease of up to 310 mV in the thermodynamic OER overpotential upon doping the considered model structures with Fe that is consistent with the experimentally measured total overpotential decrease of 190 mV.

en: Reactivity of layered manganese oxide toward water oxidation under alkaline conditions in presence and in absence of iron

electrochemical-overpotential

display_name: electrochemical-overpotential

iron-dopant

display_name: iron-dopant

manganese-oxides

display_name: manganese-oxides

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction


en: We develop a computationally efficient scheme to accurately determine finite-temperature band gaps. We here focus on materials belonging to the class ABX3 (A = Rb, Cs; B = Ge, Sn, Pb; and X = F, Cl, Br, I), which includes halide perovskites. First, an initial estimate of the band gap is provided for the ideal crystalline structure through the use of a range-separated hybrid functional, in which the parameters are determined nonempirically from the electron density and the high-frequency dielectric constant. Next, we consider two kinds of band-gap corrections to account for spin-orbit coupling and thermal vibrations including zero-point motions. In particular, the latter effect is accounted for through the special displacement method, which consists in using a single distorted configuration obtained from the vibrational frequencies and eigenmodes, thereby avoiding lengthy molecular dynamics. The sequential consideration of both corrections systematically improves the band gaps, reaching a mean absolute error of 0.17 eV with respect to experimental values. The computational efficiency of our scheme stems from the fact that only a single calculation at the hybrid-functional level is required and that it is sufficient to evaluate the corrections at the semilocal level of theory. Our scheme is particularly convenient for large-size systems and for the screening of large databases of materials. This entry provides the ideal atomic structures and the distorted atomic structures at certain temperature including zero-point motions, generated by special displacement method.

en: Accurate and efficient band-gap predictions for metal halide perovskites at finite temperature

band-gap

display_name: band-gap

nuclear-quantum-effects

display_name: nuclear-quantum-effects

perovskites

display_name: perovskites

phonon

display_name: phonon

special-displacement-method

display_name: special-displacement-method

temperature

display_name: temperature


many-body-self-interaction-and-polarons

en: We address the many-body self-interaction in relation to polarons in density functional theory. Our study provides (i) a unified theoretical framework encompassing many-body and one-body forms of self-interaction and (ii) an efficient semilocal scheme for charge localization. Our theoretical formulation establishes a quantitative connection between the many-body and one-body forms of self-interaction in terms of electron screening, thereby conferring superiority to the concept of many-body self-interaction. Our semilocal methodology involves the use of a weak localized potential and applies equally to electron and hole polarons. We find that polarons free from many-body self-interaction have formation energies that are robust with respect to the functional adopted.

en: Many-body self-interaction and polarons

title_for_slug: many-body-self-interaction-and-polarons

density-functional-theory

display_name: density-functional-theory

electron-screening

display_name: electron-screening

many-body-self-interaction

display_name: many-body-self-interaction

mgo

display_name: mgo

one-body-self-interaction

display_name: one-body-self-interaction

polaron-localization

display_name: polaron-localization

polarons

display_name: polarons

self-interaction

display_name: self-interaction


en: We developed and implemented a multi-target multi-fidelity workflow to explore the chemical space of antiperovskite materials with general formula X3BA (X = Li, Na, Mg) and PM-3m space group, searching for stable high-performance solid state electrolytes for all-solid state batteries. The workflow is based on the calculation of thermodynamic and kinetic properties, which include phase and electrochemical stability, semiconducting behaviour, and ionic diffusivity. To accelerate the calculation of the kinetic properties, we use a surrogate model able to predict the transition state structure during ionic diffusion. This reduces the calculation cost by more than one order of magnitude while keeping the mean error within 73 meV compared to the more accurate nudged elastic band method. This method allows us identify 14 materials that agree with the experimentally reported results as some of the best solid state electrolytes. Moreover, this approach is general and chemistry neutral, so can be applied to other battery chemistries and crystal prototypes. We report here the data produced in this work. These results are collected as ASE database. It contains a markdown file that explains the usage and most important data.

en: Accelerated workflow for antiperovskite-based solid state electrolytes

antiperovskites

display_name: antiperovskites

autonomous-workflow

display_name: autonomous-workflow

high-throughput-screening

display_name: high-throughput-screening

neb-calculations

display_name: neb-calculations

solid-state-electrolytes

display_name: solid-state-electrolytes

surrogate-models

display_name: surrogate-models


en: The chalcopyrite Cu(In,Ga)S2 has gained renewed interest in recent years due to its potential application in tandem solar cells. In this contribution, a combined theoretical and experimental approach is applied to investigate stable and metastable phases forming in sputtered CuInS2 (CIS) thin films. Ab initio calculations are performed to obtain formation energies, X-ray diffraction patterns, and Raman spectra of various CIS polytypes and related compounds. Multiple low-energy CIS structures with zinc-blende and wurtzite-derived lattices are identified and their XRD/Raman patterns are shown to contain many overlapping features, which could lead to misidentification unless the techniques are duly combined and analyzed. The results are verified against experimental XRD/Raman spectra measured on a series of CIS films with different compositions and treated at different temperatures, revealing the formation of several CIS polymorphs and secondary phases. The characteristic features and the mechanisms behind the formation of different phases are discussed with the focus on the thin-film photovoltaic application of CIS. The dataset contains structures and VASP output files used to derive the discussed trends.

en: Experimental and theoretical study of stable and metastable phases in sputtered CuInS2

disorder

display_name: disorder

polymorphs

display_name: polymorphs

polytypes

display_name: polytypes

simulated-powder-x-ray-diffraction

display_name: simulated-powder-x-ray-diffraction

simulated-raman-spectra

display_name: simulated-raman-spectra

wurtzite

display_name: wurtzite

zinc-blende

display_name: zinc-blende


en:
Dynamical perturbations modify the states of classical systems in surprising ways and give rise to important applications in science and technology. For example, Floquet engineering exploits the possibility of band formation in the frequency domain when a strong, periodic variation is imposed on parameters such as spring constants. We describe here Kapitza engineering, where a drive field oscillating at a frequency much higher than the characteristic frequencies for the linear response of a system changes the potential energy surface so much that maxima found at equilibrium become local minima, in precise analogy to the celebrated Kapitza pendulum where the unstable inverted configuration, with the mass above rather than below the fulcrum, actually becomes stable. Our starting point is a quantum field theory of the Ginzburg-Devonshire type, suitable for many condensed matter systems, including particularly ferroelectrics and quantum paralectrics such as the common substrate (for oxide electronics) strontium titanate (STO). We show that an off-resonance oscillatory electric field generated by a laser-driven THz source can induce ferroelectric order in the quantum-critical limit. Heating effects are estimated to be manageable using pulsed radiation; "hidden" radiation-induced order can persist to low temperatures without further pumping due to stabilization by strain. We estimate the Ginzburg-Devonshire free-energy coefficients in STO using density functional theory (DFT) and the stochastic self-consistent harmonic approximation accelerated by a machine learned force field. Although we find that STO is not an optimal choice for Kapitza stabilization, we show that scanning for further candidate materials can be performed at the computationally convenient density functional theory level. We suggest second-harmonic-generation, soft-mode-spectroscopy, and X-ray-diffraction experiments to characterize the induced order.

This Materials Cloud entry contains the DFT calculations, the force field and results from SSCHA calculations used in the article.

en: Kapitza stabilization of quantum critical order

envi

en: Environment

dft

display_name: dft

quantum-critical-order

display_name: quantum-critical-order

sscha

display_name: sscha

swissuniversities

display_name: swissuniversities


en:
Machine-learning potentials are usually trained on the ground-state, Born-Oppenheimer energy surface, which depends exclusively on the atomic positions and not on the simulation temperature. This disregards the effect of thermally excited electrons, that is important in metals, and essential to the description of warm dense matter. An accurate physical description of these effects requires that the nuclei move on a temperature-dependent electronic free energy. We propose a method to obtain machine-learning predictions of this free energy at an arbitrary electron temperature using exclusively training data from ground-state calculations, avoiding the need to train temperature-dependent potentials, and benchmark it on metallic liquid hydrogen at the conditions of the core of gas giants and brown dwarfs. This Letter demonstrates the advantages of hybrid schemes that use physical consideration to combine machine-learning predictions, providing a blueprint for the development of similar approaches that extend the reach of atomistic modeling by removing the barrier between physics and data-driven methodologies.

This record contains the raw outputs of the DFT calculations done on the training set. These files are denoted by the "training set folder #*" description. The record also contains a minimal working example showing the ML workflow for training the data and how to run the MD simulations. We include the training data in the format of XYZ files for the structures and NumPy arrays for the DFT energies, forces and DOS. We also provide a Chemiscope visualisation file of the training set.

en: Predicting hot-electron free energies from ground-state data

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).

hot-electrons

display_name: hot-electrons

liquid-hydrogen

display_name: liquid-hydrogen

machine-learning

display_name: machine-learning

marie-curie-fellowship

display_name: marie-curie-fellowship

marvel

display_name: marvel

snsf

display_name: snsf

warm-dense-matter

display_name: warm-dense-matter


en: AiiDA calculations for the publication Giant hardening response in AlMgZn(Cu) alloys. This study presents a thermomechanical processing concept which is capable of exploiting the full indus- trial application potential of recently introduced AlMgZn(Cu) alloys. The beneficial linkage of alloy design and processing allows not only to satisfy the long-standing trade-off between high mechanical strength in use and good formability during processing but also addresses the need for economically feasible processing times. After an only 3-hour short pre-aging treatment at 100 °C, the two investigated alloys, based on commercial EN AW-5182 and modified with additions of Zn and Zn + Cu respectively, show high formability due to increased work-hardening. Then, these alloys exhibit a giant hardening response of up to 184 MPa to reach a yield strength of 410 MPa after a 20-minute short final heat treatment at 185 °C, i.e. paint-baking. This rapid hardening response strongly depends on the number density, size distribution and constitution of precursors acting as preferential nucleation sites for T-phase precursor precipitation during the final high-temperature aging treatment and is significantly increased by the addition of Cu. Minor deformation (2%) after pre-aging and before final heat treatment further enhances the development of hardening precipitates additionally by activating dislocation-supported nucleation and growth. Tensile testing, quantitative and analytical electron-microscopy methods, atom probe analysis and DFT calculations were used to characterize the alloys investigated in this work over the thermomechanical processing route. The influence of pre-strain on the hardening response and the role of Cu additions in early-stage cluster nucleation are discussed in detail and supported by in-situ STEM experiments and first-principles calculations.

en: DFT data for giant hardening response in AlMgZn(Cu) alloys

materialscloud

en: Materials Cloud

aluminum

display_name: aluminum

metallurgy

display_name: metallurgy


en: Carbon is often used as a conductive additive in catalyst layers to increase conductivity and catalytic activity. However, the effect of carbon addition to perovskites on the oxygen reduction (ORR) and oxygen evolution (OER) reactions is convoluted. In this work, composites of perovskite Ba0.5Sr0.5Co0.8Fe0.2O3-δ (BSCF) and conductive additives, carbon and indium doped tin oxide are compared. It is found that the conductive additives have differing effects on the ORR and OER activities and cobalt redox behavior, with carbon having a much more significant effect. In order to elucidate further these differences between BSCF and BSCF/Carbon, operando X-ray absorption spectroscopy (XAS) is measured simultaneously with cyclic voltammetry into the ORR and OER regions and the continuous changes in the Co oxidation state are observed with high time resolution. We theorize that carbon is enhancing the Co redox activity and as a result, the ORR and OER activities are likewise improved.

en: Influence of carbon on the dynamic changes in Co oxidation state of Ba0.5Sr0.5Co0.8Fe0.2O3-δ perovskite catalyst during the oxygen reduction and evolution reactions

alkaline

display_name: alkaline

bscf

display_name: bscf

cobalt

display_name: cobalt

electrocatalysis

display_name: electrocatalysis

oer

display_name: oer

orr

display_name: orr

xas

display_name: xas


en: Strengthening by needle-shaped β′′ precipitates is critical in Al–Mg–Si alloys. Here, the strengthening is studied computationally at the peak-aged condition where precipitate shearing and Orowan looping are usually considered to have equal strengths. Pseudo-random precipitate microstructures are constructed based on experimental precipitate dimensions and volume fractions at peak aging. A Discrete Dislocation Dynamics method is then adapted to compute the Critical Resolved Shear Stress (CRSS) for Orowan looping of dislocations moving through the non-shearable precipitate field. The CRSS for Orowan looping is determined by a typical in-situ precipitate spacing that is smaller than the average spacing and by the dislocation core energy within a radius of ≈5b, a factor rarely considered. The matrix misfit stresses, volume fraction, and precipitate shape have small effects on the CRSS. With microstructure and property details introduced as faithfully as possible, the CRSS for Orowan looping using atomistically-calibrated core energies at room temperature is nonetheless ≈33% higher than experiments. This suggests that precipitate shearing controls strength, and analyses of (i) forces acting on the precipitates, (ii) misfit stresses inside the precipitates, (iii) first-principles results for the relevant precipitate fault energies, and (iv) simulations that mimic precipitate shearing indicate a shearing CRSS closer to experiments. Thus, Orowan looping only sets an upper bound for the CRSS even at peak aging, and further quantitative progress requires detailed modeling of precipitate shearing.

en: Modeling peak-aged precipitate strengthening in Al-Mg-Si alloys

discrete-dislocation-dynamics

display_name: discrete-dislocation-dynamics

orowan-mechanism

display_name: orowan-mechanism

precipitation-strengthening

display_name: precipitation-strengthening


en: Structure determination of amorphous materials remains challenging, owing to the disorder inherent to these materials. Nuclear magnetic resonance (NMR) powder crystallography is a powerful method to determine the structure of molecular solids, but disorder leads to both a high degree of overlap between measured signals, resulting in challenges for spectral assignment, and prevents the unambiguous identification of a single modelled periodic structure as representative of the whole material. Here, we determine the atomic-level ensemble structure of the amorphous form of the drug AZD4625 by combining solid-state NMR experiments with molecular dynamics (MD) simulations and machine-learned chemical shifts. By considering the combined shifts of all 1H and 13C atomic sites in the molecule, we determine the structure of the amorphous form by identifying an ensemble of local molecular environments that are in agreement with experiment. We then extract preferred conformations and intermolecular interactions in the amorphous sample, and analyze the structure in terms of the hydrogen bonding and conformational factors that stabilize the amorphous form of the drug.

en: Atomic-level structure determination of amorphous molecular solids by NMR

amorphous-solids

display_name: amorphous-solids

machine-learning

display_name: machine-learning

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

nmr

display_name: nmr

structure-determination

display_name: structure-determination


en: We present a large dataset of density functional theory calculations of thiophene (C4H4S) adsorbed on metallic and bimetallic (100) surfaces performed in Quantum Espresso.  In total 1131 different surfaces were explored resulting in 2235 total calculations.  Slabs were built from 37 different possible elements in the stoichiometry of A3B, Ll2 structures.  The 37 single element slabs, A1 structures, were included as well.  Slabs were cleaved in the (100) direction.  Two hollow adsorption sites were chosen to be explored over bimetallic surfaces and one hollow configuration for the single metal surfaces.   Bader charge analysis files are also presented.

en: High-throughput thiophene adsorption calculations on bimetallic surfaces

chemisorption

display_name: chemisorption

dft

display_name: dft

heterogenous-catalysis

display_name: heterogenous-catalysis

surfaces

display_name: surfaces

thiophene

display_name: thiophene


en: Hydroxyapatite (HA, Ca10PO4(OH)2) is a widely explored material in the experimental domain of biomaterials science, because of its resemblance with natural bone minerals. Specifically, in the bioceramic community, HA doped with multivalent cations (e.g., Mg+2, Fe+2, Sr+2, etc.) has been extensively investigated in the last few decades. Experimental research largely established the critical role of dopant content on the changes in mechanical and biocompatibility properties. The plethora of experimental measurements of mechanical response on doped HA is based on compression or indentation testing of polycrystalline materials. Such measurements, as well as computational predictions of me,  on single crystalline (doped) HA are scarce. On that premise, the present study aims to build atomistic models of Fe2+-doped HA, a model system, with varying Fe content (10, 20, 30, and 40 mol%) and to explore their uniaxial tensile response by means of molecular dynamics (MD) simulation, together with the calculation of IR spectrum. In the equilibrated unit cell structures, Ca(1) sites were found to be energetically favourable for Fe2+ substitution. The local distribution of Fe2+ ions significantly affect the atomic partial charge distribution and chemical symmetry surrounding the functional groups. These signatures are reflected in the significant decrease in the intensity of IR peaks and are found in the Fe-doped HA, together with peak splitting because of the symmetry change in the crystal structure. Another important objective of this work is to computationally predict the mechanical response of doped HA in their single crystal format. An interesting observation is that the elastic anisotropy of undoped HA was not compromised with Fe-doping. Tensile strength (TS) is systematically reduced in doped HA with Fe+2 dopant content and a decrease in TS with temperature can be attributed to the increased thermal agitation of atoms at elevated temperatures. The physics of the tensile response was rationalized in terms of the strain dependent changes in covalent/ionic framework (Ca-P distance, P-O bond strain, O-P-O angular strain, O-H bond distance). Further, the dynamic changes in covalent bond network were energetically analyzed by calculating the changes in O-H and P-O bond vibrational energy. Summarizing, the current work develops our foundational understanding of the atomistic phenomena involved in the phase stability and tensile response of Fe-doped HA single crystals.

en: In silico study on probing atomistic insight into structural stability and tensile properties of Fe-doped hydroxyapatite single crystals

hydroxyapatite

display_name: hydroxyapatite

iron-doping

display_name: iron-doping

md-simulation

display_name: md-simulation

mechanical-response

display_name: mechanical-response


en:
Alloys composed of several elements in roughly equimolar composition, often referred to as high-entropy alloys, have long been of interest for their thermodynamics and peculiar mechanical properties, and more recently for their potential application in catalysis. They are a considerable challenge to traditional atomistic modeling, and also to data-driven potentials that for the most part have memory footprint, computational effort and data requirements which scale poorly with the number of elements included. We apply a recently proposed scheme to compress chemical information in a lower-dimensional space, which reduces dramatically the cost of the model with negligible loss of accuracy, to build a potential that can describe 25 d-block transition metals. The model shows semi-quantitative accuracy for prototypical alloys and is remarkably stable when extrapolating to structures outside its training set.
In this record, we provide a dataset containing 25,000 structures utilized for fitting the aforementioned potential, with a focus on 25 d-block transition metals, excluding Tc, Cd, Re, Os and Hg.

en: Modeling high-entropy transition-metal alloys with alchemical compression: dataset HEA25

envi

en: Environment

alchemical-compression

display_name: alchemical-compression

high-entropy-alloys

display_name: high-entropy-alloys

marvel

display_name: marvel

neural-network-potential

display_name: neural-network-potential


en: We perform a large scale study of conventional superconducting materials using a machine-learning accelerated high-throughput workflow. We start by creating a comprehensive dataset of around 7000 electron-phonon calculations performed with reasonable convergence parameters. This dataset is then used to train a robust machine learning model capable of predicting the electron-phonon and superconducting properties based on structural, compositional, and electronic ground-state properties. Using this machine, we evaluate the transition temperature (Tc) of approximately 200000 metallic compounds, all of which on the convex hull of thermodynamic stability (or close to it) to maximize the probability of synthesizability. Compounds predicted to have Tc values exceeding 5 K are further validated using density-functional perturbation theory. As a result, we identify 541 compounds with Tc values surpassing 10 K, encompassing a variety of crystal structures and chemical compositions. This work is complemented with a detailed examination of several interesting materials, including nitrides, hydrides, and intermetallic compounds. Particularly noteworthy is LiMoN2, which we predict to be superconducting in the stoichiometric trigonal phase, with a Tc exceeding 38 K. LiMoN2 has been previously synthesized in this phase, further heightening its potential for practical applications.

en: Sampling the materials space for conventional superconducting compounds

materialscloud

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

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

density-functional-perturbation-theory

display_name: density-functional-perturbation-theory

electron-phonon-coupling

display_name: electron-phonon-coupling

high-throughput

display_name: high-throughput

superconductivity

display_name: superconductivity


en: We present a first-principles method to calculate electron-phonon coupling elements in atomic systems, and showcase its application to the evaluation of the phonon-limited mobility of n-type single-layer MoS₂. The method combines a density functional theory (DFT) plane-wave supercell approach with a real-space maximally localized Wannier basis. It enables the calculation of electronic structure, phonon displacements with their corresponding frequencies, and real-space electron-phonon coupling elements on the same footing, without the need for density functional perturbation theory (DFPT) or Wannier interpolation. We report a low-field, intrinsic mobility of 274 cm²/Vs at room temperature for MoS₂, and highlight its dependence on carrier density and temperature. In addition, we compare our findings to the latest available modeling data and put them in perspective with the experimentally measured values. Based on these observations, the mobilities presented in this work appear to be compatible with experimental results, when taking into account other scattering sources. Hence, the proposed approach provides a reliable framework for mobility calculations that can be extended towards large-scale device simulations.

en: Electron-phonon calculations using a wannier-based supercell approach: applications to the monolayer MoS₂ mobility

materialscloud

en: Materials Cloud

ab-initio

display_name: ab-initio

carrier-transport

display_name: carrier-transport

electron-mobility

display_name: electron-mobility

snsf

display_name: snsf


en: Hot exciton materials have the potential to improve the quantum efficiency of organic light-emitting diodes (OLEDs) by promoting high Reversed InterSystem Crossing (hRISC) between a high-lying triplet (Tn, n≥2) and a radiative singlet (Sm). In recent years, donor–acceptor-donor (D-A-D) molecular systems have shown great promise in its ability to enhance the hRISC process under certain conditions. However, strategies to find appropriate D-A-D combinations beyond trial-and-error are still elusive. This work exposes the limited applicability of the current fragment-based design rules and proposes high-throughput screening as the optimal route to find promising candidates that fulfill the energy criteria for hot exciton materials. The strategy consists of first establishing the thresholds for large triplet-triplet splitting and small singlet-triplet gap, then filtering combinations through rate comparison of competitive crossing pathways, and finally confirming hRISC with spin-orbital coupling (SOC) evaluation. Based on a curated dataset of 234 D-A-D compounds, our protocol identifies 31 promising candidates with potential for hRISC, 4 of which have been previously reported in the literature. Remarkably, while most of the promising systems show prominent hybridized local and CT (HLCT) character, several candidates do not fulfill this condition, indicating that different routes are possible to design efficient OLED materials.

en: Donor-acceptor-donor "hot exciton" triads for high reverse intersystem crossing in OLEDs

computational-screening

display_name: computational-screening

donor-acceptor-donor

display_name: donor-acceptor-donor

epfl

display_name: epfl

hot-exciton-oleds

display_name: hot-exciton-oleds

marie-curie-fellowship

display_name: marie-curie-fellowship


en: We use first-principles density functional theory to investigate how the polar distortion is affected by doping in multiferroic hexagonal yttrium manganite, h-YMnO₃. While the introduction of charge carriers tends to suppress the polar distortion in conventional ferroelectrics, the behavior in improper geometric ferroelectrics, of which h-YMnO₃ is the prototype, has not been studied to date. Using both background charge doping and atomic substitution, we find an approximately linear dependence of the polar distortion on doping concentration, with hole doping reducing and electron doping enhancing it. We show that this behavior is a direct consequence of the improper geometric nature of the ferroelectricity. In addition to its doping effect, atomic substitution can further suppress or enhance the polar distortion through changes in the local chemistry and geometry.

en: Interplay between ferroelectricity and metallicity in hexagonal YMnO₃

dftu

display_name: dftu

erc

display_name: erc

ethz

display_name: ethz


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

en: A NN-Potential for phase transformations in Ge

deepmd

display_name: deepmd

germanium

display_name: germanium

neural-network

display_name: neural-network

phase-transitions

display_name: phase-transitions


en: We investigate the interplay of spin-orbit coupling, electronic correlations, and lattice distortions in the 5d¹ double perovskite Ba₂MgReO₆. Combining density-functional theory (DFT) and dynamical mean-field theory (DMFT), we establish the Mott-insulating character of Ba₂MgReO₆ in both its cubic and tetragonal paramagnetic phases. Despite substantial spin-orbit coupling, its impact on the formation of the insulating state is minimal, consistent with theoretical expectations for d¹ systems. We further characterize the electronic properties of the cubic and tetragonal phases by analyzing spectral functions and local occupations in terms of multipole moments centered on the Re sites. Our results confirm the presence of ferroically ordered z² quadrupoles in addition to the antiferroic x²-y²-type order. We compare two equivalent but complementary descriptions in terms of either effective Re-t2g frontier orbitals or more localized atomic-like Re-d and O-p orbitals. The former maps directly on a physically intuitive picture in terms of nominal d¹ Re cations, while the latter explicitly demonstrates the role of hybridization with the ligands in the spin-orbit splitting and the formation of the charge quadrupoles around the Re sites. Finally, we compare our DFT+DMFT results with a previous DFT+U study of the tetragonal paramagnetic state. We find good qualitative agreement for the dominant charge quadrupoles, but also notable differences in the corresponding spectral functions, underscoring the need for more comparative studies between these two methods.

en: Probing the Mott-insulating behavior of Ba₂MgReO₆ with DFT+DMFT

constrained-random-phase-approximation

display_name: constrained-random-phase-approximation

density-functional-theory

display_name: density-functional-theory

double-perovskite

display_name: double-perovskite

dynamical-mean-field-theory

display_name: dynamical-mean-field-theory

mott-insulator

display_name: mott-insulator


en:
High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterogeneous catalysts, because of the large design space, and the synergistic effects between their components. In this work we use a machine-learning potential that can model simultaneously up to 25 transition metals (d-block transition metals, excluding Tc, Cd, Re, Os and Hg) to study the tendency of different elements to segregate at the surface of a HEA.
In this record, we provide a dataset HEA25S, containing 10000 bulk HEA structures (Dataset O), 2640 HEA surface slabs (Dataset A), together with 1000 bulk and 1000 surface slabs snapshots from the molecular dynamics (MD) runs (Datasets B and C), and 500 MD snapshots of the 25 elements Cantor-style alloy surface slabs.
We also provide the HEA25-4-NN and HEA25S-4-NN final models, which were used in the study. Full description of both the dataset and the models can be found the reference paper below.

en: Surface segregation in high-entropy alloys from alchemical machine learning: dataset HEA25S

alchemical-compression

display_name: alchemical-compression

atomistic-modeling

display_name: atomistic-modeling

cosmo

display_name: cosmo

high-entropy-alloys

display_name: high-entropy-alloys

machine-learning

display_name: machine-learning

marvel

display_name: marvel

surface-segregation

display_name: surface-segregation


en: A strong driving force for charge separation and transfer in semiconductors is essential for designing effective photoelectrodes for solar energy conversion. While defect engineering and polarization alignment can enhance this process, their potential interference within a photoelectrode remains unclear. Here we show that oxygen vacancies in bismuth vanadate (BiVO4) can create defect dipoles due to a disruption of symmetry. The modified photoelectrodes exhibit a strong correlation between charge separation and transfer capability and external electrical poling, which is not seen in unmodified samples. Applying poling at -150 Volt boosts charge separation and transfer efficiency to over 90 %. A photocurrent density of 6.3 mA cm-2 is achieved on the photoelectrode after loading with a nickel-iron oxide-based cocatalyst. Furthermore, using generated holes for methane partial oxidation can produce methanol with a Faradaic efficiency of approximately 6 %. These findings provide valuable insights into the photoelectrocatalytic conversion of greenhouse gases into valuable chemical products.

en: Oxygen vacancy induced defect dipoles in BiVO4 for photoelectrocatalytic partial oxidation of methane

envi

en: Environment

arc

display_name: arc

electronic-structure

display_name: electronic-structure

experimental

display_name: experimental


en: The development of unified regenerative fuel cells (URFC) necessitates an active and stable bifunctional oxygen electrocatalyst. The unique challenge of possessing high activity for both the oxygen reduction (ORR) and oxygen evolution (OER) reactions, while maintaining stability over a wide potential window impedes the design of bifunctional oxygen electrocatalysts. Herein, two design strategies are explored to optimize their performance. The first incorporates active sites for ORR and OER, Mn and Co, into a single perovskite structure, which is achieved with the perovskites Ba0.5Sr0.5Co0.8Mn0.2O3-δ (BSCM) and La0.5Ba0.25Sr0.25Co0.5Mn0.5O3-δ (LBSCM). The second combines an active ORR perovskite catalyst (La0.4Sr0.6MnO3-δ (LSM)) with an OER active perovskite catalyst (Ba0.5Sr0.5Co0.8Fe0.2O3-δ (BSCF)) in a physical mixed composite (BSCF/LSM). The success of the two strategies is investigated by measuring the catalysts' catalytic performance and response to alternating reducing and oxidizing potentials to mimic the dynamic conditions experienced during operation of URFCs. Additionally, the continuous, potentiodynamic change in Mn, Co, and Fe oxidation state during ORR and OER is elucidated with operando X-ray absorption spectroscopy (XAS) measurements, revealing key insights into the nature of the active sites. The results reveal important catalyst physiochemical properties and provide a guide for future research and design principles for bifunctional oxygen electrocatalysts.

en: Designing bifunctional perovskite catalysts for the oxygen reduction and evolution reactions

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).

oer

display_name: oer

orr

display_name: orr

perovskite

display_name: perovskite


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

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

materialscloud

en: Materials Cloud

charge-disproportionation

display_name: charge-disproportionation

constrained-random-phase-approximation

display_name: constrained-random-phase-approximation

metal-insulator-transition

display_name: metal-insulator-transition


en: Due to presence of magnetic field gradient needed for coherent spin control, dephasing of single-electron spin qubits in silicon quantum dots is often dominated by 1/f charge noise. We investigate theoretically fluctuations of ground state energy of an electron in gated quantum dot in realistic Si/SiGe structure. We assume that the charge noise is caused by motion of charges trapped at the semiconductor-oxide interface. We consider a realistic range of trapped charge densities, ρ∼10¹⁰ cm⁻², and typical lenghtscales of isotropically distributed displacements of these charges, δr≤1 nm, and identify pairs (ρ,δr) for which the amplitude and shape of the noise spectrum is in good agreement with spectra reconstructed in recent experiments on similar structures.

en: Simulation of 1/f charge noise affecting a quantum dot in a Si/SiGe structure

charge-noise

display_name: charge-noise

quantum-dots

display_name: quantum-dots

simulation

display_name: simulation


en: Contact-Electro-Catalysis is an emerging catalytic principle that takes advantage of exchanges of electrons occurring through contact electrification events at solid-liquid interfaces to initiate or drive the catalysis of redox reactions. In this publication, the authors have proven the ability of various polymer insulators to catalyze the reduction of a wide variety of metal ions in aqueous solution, in both aerobic and anaerobic conditions. This property of the dielectric polymers was employed to design a 1-step method to selectively extract gold from e-waste leachates. In anaerobic conditions, the rate of the reactions increase due to the absence of competition form oxygen for the electrons. The influence of metal ions in solution on the distance between O₂ and the polymer chain of polytetrafluoroethylene was evaluated, as well as the resulting adsorption energy. The effect of tacticity on the ability of polymers such as PP to perform the contact-electro-catalytic reduction of oxygen was also evaluated. The files in this repository contains the position of the atoms before and after relaxation in all cases that were used to conduct the calculations.

en: Reduction of precious metal ions in aqueous solutions by contact-electro-catalysis

catalysis

display_name: catalysis

contact-electrification

display_name: contact-electrification

contact-electro-catalysis

display_name: contact-electro-catalysis

vasp

display_name: vasp


en: The functionality of atomic quantum emitters is intrinsically linked to their host lattice coordination. Structural distortions that spontaneously break the lattice symmetry strongly impact their optical emission properties and spin-photon interface. In a recent manuscript, we report on the direct imaging of charge state-dependent symmetry breaking of two prototypical atomic quantum emitters in mono- and bilayer MoS₂ by scanning tunneling microscopy (STM) and non-contact atomic force microscopy (nc-AFM). By changing the built-in substrate chemical potential, different charge states of sulfur vacancies (VacS) and substitutional rhenium dopants (ReMo) can be stabilized. VacS⁻¹ as well as ReMo⁰ and ReMo⁻¹ exhibit local lattice distortions and symmetry-broken defect orbitals attributed to a Jahn-Teller effect (JTE) and pseudo-JTE, respectively. By mapping the electronic and geometric structure of single point defects, we disentangle the effects of spatial averaging, charge multistability, configurational dynamics, and external perturbations that often mask the presence of local symmetry breaking. This record contains data to support the results discussed in our manuscript.

en: Charge state-dependent symmetry breaking of atomic defects in transition metal dichalcogenides

dfg

display_name: dfg

erc

display_name: erc

jahn-teller-effect

display_name: jahn-teller-effect

snsf

display_name: snsf

stm

display_name: stm


en: Field-effect transistors (FETs) based on two-dimensional materials (2DMs) with atomically thin channels have emerged as a promising platform for beyond-silicon electronics. However, low carrier mobility in 2DM transistors driven by phonon scattering remains a critical challenge. To address this issue, we propose the controlled introduction of localized tensile strain as an effective mean to inhibit electron-phonon scattering in 2DM. Strain is achieved by conformally adhering the 2DM via van-der-Waals forces to a dielectric layer previously nanoengineered with a gray-tone topography. Our results show that monolayer MoS₂ FETs under tensile strain achieve an 8-fold increase in on-state current, reaching mobilities of 185 cm²/Vs at room temperature, in good agreement with theoretical calculations. The present work on nanotopographic grayscale surface engineering and the use of high-quality dielectric materials has the potential to find application in the nanofabrication of photonic and nanoelectronic devices. Here we report the main data and codes needed to reproduce the theoretical findings of this work.

en: Deterministic grayscale nanotopography to engineer mobilities in strained MoS₂ FETs

density-functional-theory

display_name: density-functional-theory

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

transport

display_name: transport


en: Several methods have been developed to improve the predictions of density functional theory (DFT) in the case of strongly correlated electron systems. Out of these approaches, DFT+U, which corresponds to a static treatment of the local interaction, and DFT combined with dynamical mean field theory (DFT+DMFT), which considers local fluctuations, have both proven incredibly valuable in tackling the description of materials with strong local electron-electron interactions. While it is in principle known that the Hartree-Fock (HF) limit of the DFT+DMFT approach should recover DFT+U, demonstrating this equivalence in practice is challenging, due to the very different ways in which the two approaches are generally implemented. In this work, we introduce a way to perform DFT+U calculations in QE using Wannier functions as calculated by Wannier90, which allows us to use the same Hubbard projector functions both in DFT+U and in DFT+DMFT. We benchmark these DFT+U calculations against DFT+DMFT calculations where the DMFT impurity problem is solved within the HF approximation. Considering a number of prototypical materials including NiO, MnO, LaMnO₃, and LuNiO₃, we establish the sameness of the two approaches. Finally, we showcase the versatility of our approach by going beyond the commonly used atomic orbital-like projectors by performing DFT+U calculations for VO₂ using a special set of bond-centered Wannier functions.

en: Explicit demonstration of the equivalence between DFT+U and the Hartree-Fock limit of DFT+DMFT

dftdmft

display_name: dftdmft

dftu

display_name: dftu

hartree-fock

display_name: hartree-fock

marvel

display_name: marvel


en: Graphene nanoribbons (GNRs), nanometer-wide strips of graphene, have garnered significant attention due to their tunable electronic and magnetic properties arising from quantum confinement. A promising approach to manipulate their electronic characteristics involves substituting carbon with heteroatoms, such as nitrogen, with different effects predicted depending on their position. In a recent publication, we present the extension of the edges of 7-atom-wide armchair graphene nanoribbons (7-AGNRs) with pyridine rings, achieved on a Au(111) surface via on-surface synthesis. High-resolution structural characterization confirms the targeted structure, showcasing the pre-dominant formation of carbon-nitrogen (C-N) bonds (over 90% of the units) during growth. This favored bond formation pathway is elucidated and confirmed through density functional theory (DFT) simulations. Furthermore, an analysis of the electronic properties reveals a reduction of the band gap of the GNR, accompanied by the presence of nitrogen-localized states. Our results underscore the successful formation of C-N bonds on the metal surface, providing insights for designing new GNRs that incorporate substitutional nitrogen atoms to precisely control their electronic properties. The record contains data that support the findings described in the publication.

en: Preferential graphitic-nitrogen formation in pyridine-extended graphene nanoribbons

envi

en: Environment

aiidalab

display_name: aiidalab

cscs

display_name: cscs

dft

display_name: dft

graphene-nanoribbons

display_name: graphene-nanoribbons

nitrogen-doping

display_name: nitrogen-doping


en: Accurate descriptions of intermolecular interactions are of great importance in simulations of molecular liquids. We present an electronic structure method that combines the accuracy of the Harris functional approach with the computational efficiency of approximately linear-scaling density functional theory (DFT). The proposed method allows for simulations with accuracies close to the Kohn-Sham DFT reference. Embedded in the CP2K program package, the method is designed to enable ab initio molecular dynamics simulations of molecular solutions for system sizes of several thousands of atoms. As example of production applications we applied the method to molecular dynamics simulations in the isobaric-isothermal ensemble of the binary mixtures cyclohexane-methanol and toluene-methanol at different molar fractions of methanol. This record contains all CP2K input files necessary for the MD simulations, as well as 30ps trajectory files, and benchmark data of the energy correction.

en: A variational formulation of the Harris functional as correction to approximate Kohn-Sham density functional theory

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).

cyclohexane

display_name: cyclohexane

harris-functional

display_name: harris-functional

isobaric-isothermal-ensemble

display_name: isobaric-isothermal-ensemble

linear-scaling-dft

display_name: linear-scaling-dft

methanol

display_name: methanol

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

subsystem-dft

display_name: subsystem-dft

toluene

display_name: toluene