en: Oxygen vacancies are a common source of excess electrons in complex oxides. In Mott insulators, these additional electrons can induce a metal-insulator transition (MIT), fundamentally altering the electronic properties of the  system. Here we study the effect of oxygen vacancies in LaTiO3, a prototypical Mott insulator close to the MIT. We show that the introduction of oxygen vacancies creates a vacancy-related band immediately below the partially filled Ti-t 2g bands. We study the effect of this additional band on the Mott MIT  using a combination of density functional theory and dynamical mean-field theory (DFT+DMFT), employing a minimal correlated subspace consisting of effective Ti-t 2g orbitals plus an additional Wannier function centered on the vacancy site. We find that the Mott insulating state in LaTiO3 is robust to the presence of the vacancy band, which remains fully occupied even in the presence of a local Coulomb repulsion, and therefore does not cause a doping of the Mott insulator

en: DFT+DMFT study of oxygen vacancies in a Mott insulator

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).

defects

display_name: defects

dftdmft

display_name: dftdmft

electronic-structure

display_name: electronic-structure

mott-insulators

display_name: mott-insulators

oxygen-vacancies

display_name: oxygen-vacancies


$oid: OBSOLETE

en: We investigate the synergy occurring in photocatalytic water splitting between the metal-organic framework MIL-125-NH2 and two co-catalysts, namely NiO and Ni2P, by calculating their band edge alignment with respect to the redox levels of liquid water. For the NiO/H2O and Ni2P/H2O interfaces, we employ an explicit atomistic description of water and perform molecular dynamics simulations considering both molecular and dissociated water adsorbed at the co-catalyst surface. For the MIL-125-NH2/NiO and MIL-125-NH2/Ni2P interfaces, we rely on the concept of charge neutrality and use a scheme combining the electron affinities and the charge neutrality levels of the interface components. We provide a description of the underlying fundamental processes that is consistent with photoluminescence and intrinsic activity experiments and that supports NiO and Ni2P as suitable co-catalysts for MIL-125-NH2 as far as the hydrogen evolution reaction is concerned.

en: Unraveling the synergy between metal-organic frameworks and co-catalysts in photocatalytic water splitting

materialscloud

approval_status: approved

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

co-catalyst

display_name: co-catalyst

density-functional-calculations

display_name: density-functional-calculations

first-principles-calculations

display_name: first-principles-calculations

hydrogen-evolution

display_name: hydrogen-evolution

metal-organic-frameworks

display_name: metal-organic-frameworks

molecular-dynamics

display_name: molecular-dynamics

photocatalysis

display_name: photocatalysis

water-splitting

display_name: water-splitting


en: Singlet Fission (SF) has demonstrated significant promise for boosting the power conversion efficiency (PCE) of solar cells. Traditionally, SF is targeted as an intermolecular process, however its dependence on crystal packing makes molecular design difficult. In contrast, intramolecular SF (iSF) enables the exploration of tunable bi-chromophoric systems following well-defined structure-property relationships. In this work, we propose a set of parameters to screen conjugated donor-acceptor copolymer candidates with potential iSF behaviour. We focus our analysis on the E(S1)>2E(T1) thermodynamic condition and on the appropriate charge transfer (CT) character of S1. We map the CT character with respect to the frontier molecular orbital (FMO) energies of the constituent monomers, providing a cost-effective protocol for an accelerated screening of promising iSF donor-acceptor pairs, while minimizing the number of computations. These parameters are applied to a chemically diverse, curated library of 81 truncated dimers of synthetically feasible donor-acceptor copolymers. From our dataset, four candidates are flagged for iSF, two of which were previously experimentally reported. This protocol is envisioned to be scaled up for the high-throughput screening of large databases of donor-acceptor dimers for the design and identification of conjugated polymers capable of iSF.

en: Designing Singlet Fission Candidates from Donor-Acceptor Copolymers

da-copolymers

display_name: da-copolymers

dft-computations

display_name: dft-computations

epfl

display_name: epfl

screening-descriptors

display_name: screening-descriptors

singlet-fission

display_name: singlet-fission


en: We investigate the solvation effect of water on the overpotentials of the oxygen evolution reaction on rutile TiO2 by applying the thermodynamic integration method on atomistic model interfaces with and without the water molecules. We compare the results at the vacuum interface with the commonly used computational hydrogen electrode method, finding overall good agreement. The effect of the solvent is found to be twofold. First, the explicit treatment of the solvent can lead to equilibrium configurations differing from the relaxed structures without solvent. Second, the overpotentials can be affected by up to 0.5 eV. The energetics are subject to electrostatic effects at the interface rather than to modifications in the hydrogen bond network. These results provide a promising perspective for treating the solvent with implicit models.

en: Effect of the Solvent on the Oxygen Evolution Reaction at the TiO2-Water Interface

explicit-solvent

display_name: explicit-solvent

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction

rutile

display_name: rutile

solvation-effect

display_name: solvation-effect

water

display_name: water


en: We describe a nonempirical procedure for achieving accurate band gaps of extended systems through the insertion of suitably defined potential probes. By enforcing Koopmans' condition on the resulting localized electronic states, we determine the optimal fraction of Fock exchange to be used in the adopted hybrid functional. As potential probes, we consider point defects, the hydrogen interstitial, and various adjustable potentials that allow us to vary the energy level of the localized state in the band gap. By monitoring the delocalized screening charge, we achieve a measure of the degree of hybridization with the band states, which can be used to improve the band-gap estimate. Application of this methodology to AlP, C, and MgO yields band gaps differing by less than 0.2 eV from experiment.

en: Adjustable potential probes for band-gap predictions of extended systems through nonempirical hybrid functionals

band-gap

display_name: band-gap

first-principles

display_name: first-principles

hybrid-functional

display_name: hybrid-functional

point-defect

display_name: point-defect

potential-probe

display_name: potential-probe


en: In this work, we investigate the angle-dependent magnetoresistance (AMR) of the layered nodal-line Dirac semimetal ZrSiS for the in-plane and out-of-plane current directions. Combining the Fermi surfaces calculated from first principles with the Boltzmann's semiclassical transport theory, we reproduce all the prominent features of the unusual behavior of the in-plane and out-of-plane AMR.We can conclude that the dominant contribution the cusplike AMR lies in open orbits of the hole pocket and, in general, AMR is strongly influenced by charge compensation effect and the off-diagonal conductivity tensor elements, which give rise to peculiar butterfly-shaped AMR.

en: Highly anisotropic interlayer magnetoresitance in ZrSiS nodal-line Dirac semimetal

boltzmann-transport-theory

display_name: boltzmann-transport-theory

fermi-surface

display_name: fermi-surface

magnetoresistance

display_name: magnetoresistance

marvel

display_name: marvel

wanniertools

display_name: wanniertools


en: Two-dimensional materials are emerging as a promising platform for ultrathin channels in field-effect transistors. To this aim, novel high-mobility semiconductors need to be found or engineered. Although extrinsic mechanisms can in general be minimized by improving fabrication processes, the suppression of intrinsic scattering (driven, for example, by electron–phonon interactions) requires modification of the electronic or vibrational properties of the material. Because intervalley scattering critically affects mobilities, a powerful approach to enhance transport performance relies on engineering the valley structure. We show here the power of this strategy using uniaxial strain to lift degeneracies and suppress scattering into entire valleys, dramatically improving performance. This is shown in detail for arsenene, where a 2% strain stops scattering into four of the six valleys and leads to a 600% increase in mobility. The mechanism is general and can be applied to many other materials, including in particular the isostructural antimonene and blue phosphorene. In this entry we provide the AiiDA database with the calculation of the electron-phonon matrix elements for arsenene, both in the equilibrium and in the strained case, together with scripts to retrieve and plot them.

en: Valley-Engineering Mobilities in Two-Dimensional Materials

electronphonon-scattering

display_name: electronphonon-scattering

intervalley

display_name: intervalley

transport

display_name: transport


en: Using a density-functional framework, we investigate the vibrational spectra of vitreous SiO2 to determine to what extent these spectra provide information about the medium-range structure of the oxide network. We carry out a comparative study involving three model structures, which all feature a nondefective network of corner-sharing tetrahedra but differ through their Si-O-Si bond-angle distributions and ring statistics. We first address the results of typical diffraction probes. Fair agreement with experiment is achieved for the total neutron and total x-ray structure factors of all models, indicating limited sensitivity of these structure factors to the medium-range structure. The same consideration also applies to the Si-O and O-O partial structure factors. At variance, the Si-Si partial structure factor is found to be highly sensitive to the Si-O-Si bond-angle distribution. We then address typical vibrational spectra, such as the inelastic neutron spectrum, the infrared spectra, and the Raman spectra. Our study indicates that the considered experimental data are globally consistent with a medium-range structure characterized by an average Si-O-Si bond angle of 148° and with small-ring concentrations as derived from the intensities of the experimental Raman defect lines. To describe the infrared and Raman couplings, our work also introduces parametric models which reproduce well the spectra calculated from first principles.

en: Medium-range structure of vitreous SiO2

cscs

display_name: cscs

dft

display_name: dft

epfl

display_name: epfl

infrared

display_name: infrared

raman

display_name: raman

snsf

display_name: snsf


en: The electronic charge density plays a central role in determining the behavior of matter at the atomic scale, but its computational evaluation requires demanding electronic-structure calculations. We introduce an atom-centered, symmetry-adapted framework to machine-learn the valence charge density based on a small number of reference calculations. The model is highly transferable, meaning it can be trained on electronic-structure data of small molecules and used to predict the charge density of larger compounds with low, linear-scaling cost. Applications are shown for various hydrocarbon molecules of increasing complexity and flexibility, and demonstrate the accuracy of the model when predicting the density on octane and octatetraene after training exclusively on butane and butadiene. This transferable, data-driven model can be used to interpret experiments, accelerate electronic structure calculations, and compute electrostatic interactions in molecules and condensed-phase systems.

en: Transferable Machine-Learning Model of the Electron Density

materialscloud

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

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

electron-density

display_name: electron-density

machine-learning

display_name: machine-learning


en: Distinct to type-I Weyl semimetals (WSMs) that host quasiparticles described by the Weyl equation, the energy dispersion of quasiparticles in type-II WSMs violates Lorentz invariance and the Weyl cones in the momentum space are tilted. Since it was proposed that type-II Weyl fermions could emerge from WTe2, MoTe2, WP2 and MoP2 families of materials, a large number of experiments have been dedicated to unveiling the possible manifestation of type-II WSMs, e.g., surface-state Fermi arcs. However, the interpretations of the experimental results are very controversial. Here, using angle-resolved photoemission spectroscopy supported by the first-principles calculations, we probe the tilted Weyl cone bands in the bulk electronic structure of WP2 directly, which are at the origin of Fermi arcs at the surfaces and transport properties related to the chiral anomaly in type-II WSMs. Our results ascertain that, due to the spin-orbit coupling, the Weyl nodes originate from the splitting of fourfold degenerate band-crossing points with Chern numbers C=2 or  C=-2, which is unique among all the discovered WSMs. Our finding also provides a guiding line to observe the chiral anomaly that could manifest in novel transport properties. Input files for theoretical calculations and raw data from ARPES measurements are included in this record.

en: Observation of Weyl Nodes in Robust Type-II Weyl Semimetal WP2

materialscloud

en: Materials Cloud

arpes

display_name: arpes

type-ii-weyl-semimetal

display_name: type-ii-weyl-semimetal

vasp

display_name: vasp


en:
From the Ising model and the Lennard-Jones fluid to water and the iron-carbon system, phase diagrams are an indispensable tool to understand phase equilibria. Despite the effort of the simulation community, the calculation of a large portion of a phase diagram using computer simulation is still today a significant challenge. Here, we propose a method to calculate phase diagrams involving liquid and solid phases by the reversible transformation of the liquid and the solid. To this end, we introduce an order parameter that breaks the rotational symmetry and we leverage our recently introduced method to sample the multithermal-multibaric ensemble. In this way, in a single molecular dynamics simulation, we are able to compute the liquid-solid coexistence line for entire regions of the temperature and pressure phase diagram. We apply our approach to the bcc-liquid phase diagram of sodium and the fcc-bcc-liquid phase diagram of aluminum.

This repository contains the input files to reproduce the results of the paper mentioned above. The results are divided into two zip files Al.zip and Na.zip for simulations of aluminum and sodium, respectively. Inside each folder there is an example for the calculation of free energies at constant temperature and pressure, and the input files and results of the multithermal-multibaric simulations.

en: Calculation of phase diagrams in the multithermal-multibaric ensemble

envi

en: Environment

aluminum

display_name: aluminum

enhanced-sampling

display_name: enhanced-sampling

free-energy

display_name: free-energy

multithermal-multibaric

display_name: multithermal-multibaric

phase-diagrams

display_name: phase-diagrams

sodium

display_name: sodium

ves

display_name: ves


en:
In a recent work, we demonstrated the bottom-up on-surface synthesis of poly(para-dibenzo[bc,kl]-coronenylene) , a zigzag edge-encased analog of poly(para-phenylene), and its lateral fusion into zigzag edge-extended graphene nanoribbons.
The record contains data to reproduce the calculations that were performed at Empa to support the findings discussed in the manuscript.

en: Graphene Nanoribbons Derived From Zigzag Edge-Encased Poly(para-2,9-dibenzo[bc,kl]coronenylene) Polymer Chains

ab-initio

display_name: ab-initio

garphene-nanoribbons

display_name: garphene-nanoribbons

on-surface-synthesis

display_name: on-surface-synthesis


en: We have analyzed structural motifs in the Deem database of hypothetical zeolites to investigate whether the structural diversity found in this database can be well-represented by classical descriptors, such as distances, angles, and ring sizes, or whether a more general representation of the atomic structure, furnished by the smooth overlap of atomic position (SOAP) method, is required to capture accurately structure–property relations. We assessed the quality of each descriptor by machine-learning the molar energy and volume for each hypothetical framework in the dataset. We have found that a SOAP representation with a cutoff length of 6 Å, which goes beyond near-neighbor tetrahedra, best describes the structural diversity in the Deem database by capturing relevant interatomic correlations. Kernel principal component analysis shows that SOAP maintains its superior performance even when reducing its dimensionality to those of the classical descriptors and that the first three kernel principal components capture the main variability in the dataset, allowing a 3D point cloud visualization of local environments in the Deem database. This "cloud atlas" of local environments was found to show good correlations with the contribution of a given motif to the density and stability of its parent framework. Local volume and energy maps constructed from the SOAP/machine learning analyses provide new images of zeolites that reveal smooth variations of local volumes and energies across a given framework and correlations between the contributions to volume and energy associated with each atom-centered environment.

en: A New Kind of Atlas of Zeolite Building Blocks

erc

display_name: erc

marvel

display_name: marvel

sccer

display_name: sccer

zeolites

display_name: zeolites


en: This record mainly upload all the raw experimental data related to the manuscript. Fig1 include the transport data, magnetotransport data, AHE data, ESR data, uSR data. These data related to the conclusion that spin fluctuation established in the paramagnetic phase. Fig2 contains in-plane and out-of-plane 3D photoemission spectra, and high resolution cuts on top and side surface. Fig3 contains band electronic structure along high symmetry lines with spin along all kinds of typical directions. Fig4 contains 3-D in-plane and out-of-plane Fermi surfaces on the 101 cleavage, high resolution cuts, STM/STS data. The proved the observation of Weyl points.

en: Spin fluctuation induced Weyl semimetal state in the paramagnetic phase of EuCd2As2

arpes

display_name: arpes

magnetic-material

display_name: magnetic-material

magnetic-weyl-semimetal

display_name: magnetic-weyl-semimetal

spin-fluctuation

display_name: spin-fluctuation

topological-material

display_name: topological-material


en: Coronoids, polycyclic aromatic hydrocarbons with geometrically defined cavities, are promising model structures of porous graphene. This record contains data to support our recent research where we report the on-surface synthesis of C168 and C140 coronoids, referred to as [6]- and [5]coronoid, respectively, using 5,9-dibromo-14-phenylbenzo[m]tetraphene as the precursor. These coronoids entail large cavities (>1 nm) with inner zigzag edges, distinct from their outer armchair edges. While [6]coronoid is planar, [5]coronoid is not. Low-temperature scanning tunneling microscopy/spectroscopy and noncontact atomic force microscopy unveil structural and electronic properties in accordance with those obtained from density functional theory calculations. Detailed analysis of ring current effects identifies the rings with the highest aromaticity of these coronoids, whose pattern matches their Clar structure. The pores of the obtained coronoids offer intriguing possibilities of further functionalization toward advanced host–guest applications.

en: Large-cavity coronoids with different inner and outer edge structures

aromaticity

display_name: aromaticity

coronoid

display_name: coronoid

cscs

display_name: cscs

snsf

display_name: snsf


en: This entry provides the snapshots of liquid water generated with ab initio molecular dynamics using rVV10 density functional at room temperature. Nuclear quantum effects are taken into account through path-integral molecular dynamics simulations.

en: Ab initio electronic structure of liquid water: Molecular dynamics snapshots

liquid-water

display_name: liquid-water

nuclear-quantum-effect

display_name: nuclear-quantum-effect


en: Several enhanced sampling methods, such as umbrella sampling or metadynamics, rely on the identification of an appropriate set of collective variables. Recently two methods have been proposed to alleviate the task of determining efficient collective variables. One is based on linear discriminant analysis; the other is based on a variational approach to conformational dynamics and uses time-lagged independent component analysis. In this paper, we compare the performance of these two approaches in the study of the homogeneous crystallization of two simple metals. We focus on Na and Al and search for the most efficient collective variables that can be expressed as a linear combination of X-ray diffraction peak intensities. We find that the performances of the two methods are very similar. Wherever the different metastable states are well-separated, the method based on linear discriminant analysis, based on its harmonic version, is to be preferred because simpler to implement and less computationally demanding. The variational approach, however, has the potential to discover the existence of different metastable states.

en: Improving collective variables: The case of crystallization

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).

crystalization

display_name: crystalization

enhanced-sampling

display_name: enhanced-sampling

hlda

display_name: hlda

plumed

display_name: plumed


en: Rare-earth nickelates exhibit a metal–insulator transition accompanied by a structural distortion that breaks the symmetry between formerly equivalent Ni sites. The quantitative theoretical description of this coupled electronic–structural instability is extremely challenging. Here, we address this issue by simultaneously taking into account both structural and electronic degrees of freedom using a charge self-consistent combination of density functional theory and dynamical mean-field theory, together with screened interaction parameters obtained from the constrained random phase approximation. Our total energy calculations show that the coupling to an electronic instability toward a charge disproportionated insulating state is crucial to stabilize the structural distortion, leading to a clear first order character of the coupled transition. The decreasing octahedral rotations across the series suppress this electronic instability and simultaneously increase the screening of the effective Coulomb interaction, thus weakening the correlation effects responsible for the metal–insulator transition. Our approach allows to obtain accurate values for the structural distortion and thus facilitates a comprehensive understanding, both qualitatively and quantitatively, of the complex interplay between structural properties and electronic correlation effects across the nickelate series.

en: Energetics of the coupled electronic–structural transition in the rare-earth nickelates

materialscloud

en: Materials Cloud

dynamical-mean-field-theory

display_name: dynamical-mean-field-theory

electronic-properties-and-materials

display_name: electronic-properties-and-materials

marvel

display_name: marvel

quantum-materials

display_name: quantum-materials


en: We present all AMONs for GDB and Zinc data-bases using no more than 7 non-hydrogen atoms (AGZ7)---a calculated organic chemistry building-block dictionary based on the AMON approach [Huang and von Lilienfeld, Nature Chemistry (2020)]. AGZ7 records Cartesian coordinates of compositional and constitutional isomers, as well as properties for ∼140k small organic molecules obtained by systematically fragmenting all molecules of Zinc and the majority of GDB17 into smaller entities, saturating with hydrogens, and containing no more than 7 heavy atoms (excluding hydrogen atoms). AGZ7 cover the elements H, B, C, N, O, F, Si, P, S, Cl, Br, Sn and I and includes optimized geometries, total energy and its decomposition, Mulliken atomic charges, dipole moment vectors, quadrupole tensors, electronic spatial extent, eigenvalues of all occupied orbitals, LUMO, gap, isotropic polarizability, harmonic frequencies, reduced masses, force constants, IR intensity, normal coordinates, rotational constants, zero-point energy, internal energy, enthalpy, entropy, free energy, and heat capacity (all at ambient conditions) using B3LYP/cc-pVTZ (pseudopotentials were used for Sn and I) level of theory. We exemplify the usefulness of this data set with AMON based machine learning models of total potential energy predictions of seven of the most rigid GDB-17 molecules.

en: Dictionary of 140k GDB and ZINC derived AMONs

envi

en: Environment

building-blocks

display_name: building-blocks

erc

display_name: erc

organic-chemical-space

display_name: organic-chemical-space

quantum-machine-learning

display_name: quantum-machine-learning


rare-earth-magnetic-nitride-perovskites

en: We propose perovskite nitrides with magnetic rare-earth metals as novel materials with a range of technological applications. These materials appear to be thermodynamically stable and, in spite of possessing different crystal structures and different atomic environments, they retain the magnetic moment of the corresponding elemental rare-earth metal. We find both magnetic metals and semiconductors, with a wide range of magnetic moments and some systems posses record-high magnetic anisotropy energies. Further tuning of the electronic and magnetic properties can also be expected by doping with other rare-earths or by creating solid solutions. The synthesis of these exotic materials with unusual compositions would extend not only the accepted stability domain of perovskites but also open the way for a series of applications enabled by their rich physics.

en: Rare-earth magnetic nitride perovskites

title_for_slug: rare-earth-magnetic-nitride-perovskites

magnetic-anisotropy-energy

display_name: magnetic-anisotropy-energy

nitride-perovskites

display_name: nitride-perovskites

rare-earth-metals

display_name: rare-earth-metals


en: Interatomic potentials are often necessary to describe complex realistic systems that would be too costly to study from first-principles. Commonly, interatomic potentials are designed using functional forms driven by physical intuition and fitted to experimental or computational data. The moderate flexibility of these functional forms limits their ability to be systematically improved by increasing the fitting datasets; on the other hand, their qualitative description of the essential physical interactions ensures a modicum degree of transferability. Recently, a novel trend has emerged where potential-energy surfaces are represented by neural networks fitted on large numbers of first-principles calculations, thus maximizing flexibility but requiring extensive datasets to ensure transferability. Gaussian Approximation Potentials in particular are a novel class of potentials based on non-linear, non-parametric Gaussian-process regression. Here we generate a Gaussian Approximation model for the α-phase of iron training on energies, stresses and forces taken from first-principles molecular dynamics simulations of pristine and defected bulk systems, of surfaces and γ-surfaces with different crystallographic orientations.

en: Gaussian Approximation Potentials for iron from extended first-principles database (Data Download)

artificial-neural-networks

display_name: artificial-neural-networks

gaussian-approximation-potentials

display_name: gaussian-approximation-potentials

interatomic-potentials

display_name: interatomic-potentials

iron

display_name: iron

machine-learning

display_name: machine-learning


en: Methods based on the second order Møller–Plesset perturbation theory (MP2) and the Random Phase Approximation (RPA) have emerged as practicable and reliable approaches to improve the accuracy of density functional approximations for first principle atomistic simulations. Such approaches are in fact capable to account ab-initio for non-local dynamical electron correlation effects, which play a fundamental role, for example, in the description of non-bonded interactions. To assess the performance of MP2 and RPA for real applications, isobaric-isothermal Monte Carlo simulations have been performed to study the structural properties of bulk liquid water under ambient conditions. The choice of bulk liquid water as benchmark system is motivated by the complicated nature of the intermolecular interactions, where repulsion, polarization, hydrogen bonding and van der Waals forces play an important role and are particularly difficult to reproduce accurately in atomistic models. The results demonstrate the feasibility of such approaches which open the way for further applications.

en: Isobaric-Isothermal Monte Carlo Simulations of Bulk Liquid Water from MP2 and RPA Theory (MC Trajectories Data Download)

bulk-liquid-water

display_name: bulk-liquid-water

isobaric-isothermal-npt

display_name: isobaric-isothermal-npt

monte-carlo-simulations

display_name: monte-carlo-simulations

rpa

display_name: rpa


en:
With the growth of natural gas as an energy source, upgrading CO2-contaminated supplies has become increasingly important. Here we develop a single metric that captures how well an adsorbent performs the separation of CH4 and CO2, and we then use this metric to computationally screen tens of thousands of all-silica zeolites. We show that the most important predictors of separation performance are the CO2 heat of adsorption (Qst, CO2) and the CO2 saturation loading capacity. We find that a higher-performing material results when the absolute value of the CH4 heat of adsorption (Qst, CH4) is decreased independently of Qst, CO2, but a correlation that exists between Qst, CH4 and Qst, CO2 in all-silica zeolites leads to incongruity between the objectives of optimizing Qst, CO2 and minimizing Qst, CH4, rendering Qst, CH4 nonpredictive of separation performance. We also conduct a large-scale analysis of ideal adsorbed solution theory (IAST) by comparing results obtained using directly-generated mixture isotherms to those obtained using IAST; IAST appears adequate for the purposes of establishing performance trends and structure–property relationships in a high-throughput manner, but it must be tested for validity when analyzing individual adsorbents in detail since it can produce significant errors for materials in which there is site segregation of the adsorbate species.

Version 2 provides the structures in CIF format.

en: High-throughput computational screening of nanoporous adsorbents for CO 2 capture from natural gas

database

display_name: database

dc

display_name: dc

deliverable-capacities

display_name: deliverable-capacities

gcmc

display_name: gcmc

grand-canonical-monte-carlo

display_name: grand-canonical-monte-carlo

high-throughput

display_name: high-throughput

iza

display_name: iza

methane-storage

display_name: methane-storage

nanoporous

display_name: nanoporous

porous-polymer-networks

display_name: porous-polymer-networks

three-dimensional

display_name: three-dimensional

zeolites

display_name: zeolites


enhanced-sampling-of-transition-states

en: The free energy landscapes of several fundamental processes are characterized by high barriers separating long-lived metastable states. In order to explore these type of landscapes enhanced sampling methods are used. While many such methods are able to obtain sufficient sampling in order to draw the free energy, the transition states are often sparsely sampled. We propose an approach based on the Variationally Enhanced Sampling Method to enhance sampling in the transition region. To this effect, we introduce a dynamic target distribution which uses the derivative of the instantaneous free energy surface to locate the transition regions on the fly and modulate the probability of sampling different regions. Finally, we exemplify  the effectiveness of this approach in enriching the number of configurations in the transition state region in the cases of a chemical reaction and of a nucleation process.

en: Enhanced sampling of transition states

title_for_slug: enhanced-sampling-of-transition-states

critical-clusters

display_name: critical-clusters

enhanced-sampling

display_name: enhanced-sampling

molecular-modelling

display_name: molecular-modelling

nucleation

display_name: nucleation

transition-states

display_name: transition-states


en:
Metal–organic frameworks (MOFs), a class of porous materials, are of particular interest in gas storage and separation applications due largely to their high internal surface areas and tunable structures. MOF-5 is perhaps the archetypal MOF; in particular, many isoreticular analogues of MOF-5 have been synthesized, comprising alternative dicarboxylic acid ligands. In this contribution we introduce a new set of hypothesized MOF-5 analogues, constructed from commercially available organic molecules. We describe our automated procedure for hypothetical MOF design, comprising selection of appropriate ligands, construction of 3D structure models, and structure relaxation methods. 116 MOF-5 analogues were designed and characterized in terms of geometric properties and simulated methane uptake at conditions relevant to vehicular storage applications. A strength of the presented approach is that all of the hypothesized MOFs are designed to be synthesizable utilizing ligands purchasable online.

Version 2 includes the structures in CIF format.

en: Mail-order metal-organic frameworks (MOFs): designing isoreticular MOF-5 analogues comprising commercially available organic molecules

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).

database

display_name: database


en: Dipole polarizabilities (and other molecular properties) computed using linear response coupled cluster theory (LR-CCSD/d-aug-cc-pVDZ) and hybrid density functional theory (B3LYP/d-aug-cc-pVDZ, SCAN0/d-aug-cc-pVDZ, and B3LYP/d-aug-cc-pVTZ) for the 7,211 molecules in the QM7b database and the 52 molecules in the AlphaML showcase database.

en: Quantum Mechanical Static Dipole Polarizabilities in the QM7b and AlphaML Showcase Databases

materialscloud

en: Materials Cloud

density-functional-theory

display_name: density-functional-theory

dipole-polarizability

display_name: dipole-polarizability

linear-response-coupled-cluster

display_name: linear-response-coupled-cluster


en: Data for the case study of TuTraSt on methane diffusion in zeolites, using a standard kinetic Monte Carlo simulation based on the output of our grid analysis. We find that it is accurate, fast, and rigorous without limitations to the geometries of the diffusion tunnels or transition states.

en: TuTraST data for methane diffusion in IZA structures

envi

en: Environment

diffusion

display_name: diffusion

kinetic-monte-carlo

display_name: kinetic-monte-carlo

marvel

display_name: marvel

methane

display_name: methane

transition-state-theory

display_name: transition-state-theory

tutrast

display_name: tutrast

zeolite

display_name: zeolite


en:
Despite the enormous success and popularity of density functional theory, systematic verification and validation studies are still very limited both in number and scope. Here, we propose a universal standard protocol to verify publicly available pseudopotential libraries, based on several independent criteria including verification against all-electron equations of state and plane-wave convergence tests for phonon frequencies, band structure, cohesive energy and pressure. Adopting these criteria we obtain two optimal pseudopotential sets, namely the Standard Solid State Pseudopotential (SSSP) efficiency and precision libraries, tailored for high-throughput materials screening and high-precision materials modelling. As of today, the SSSP precision library is the most accurate open-source pseudopotential library available. This archive entry contains the database of calculations (phonons, cohesive energy, equation of state, band structure, pressure, etc.) together with the provenance of all data and calculations as stored by AiiDA.

*** Recent UPDATE April 2023 - Version 1.3.0 ***
The pseudopotentials of elements At, Fr, Ra are added from PSlibrary. The pseudopotential of actinides are added from dataset of https://www.uni-marburg.de/de/fb15/arbeitsgruppen/anorganische_chemie/ag-kraus/forschung/paw_datasets_for_the_actinoids

Please refer to the CHANGELOG.txt file within the archive to view a complete list of version changes.

en: A Standard Solid State Pseudopotentials (SSSP) library optimized for precision and efficiency

aiida

display_name: aiida

calculations

display_name: calculations

chessboards

display_name: chessboards

cohesive-energy

display_name: cohesive-energy

cscs

display_name: cscs

dft

display_name: dft

efficiency

display_name: efficiency

electronic-bands

display_name: electronic-bands

electronic-properties

display_name: electronic-properties

elemental-solids

display_name: elemental-solids

high-throughput

display_name: high-throughput

library

display_name: library

max

display_name: max

phonons

display_name: phonons

prace

display_name: prace


en: The record contains data that support the work where we report the optical imaging and absorption spectroscopy on atomically precise armchair graphene nanoribbons (GNRs) on insulating fused silica substrates. This is achieved by controlling light polarization on macroscopically aligned GNRs which greatly enhances the optical contrast of the submonolayer GNRs on the insulating substrates. We measure the linear absorption spectra of 7-armchair and 9-armchair GNRs in this study, and the experimental data agree qualitatively with ab inito calculation results. The polarization spectroscopy technique enables an unambiguous optical identification of GNRs and provides a rapid tool to characterize the transferred film over a large area.

en: Optical imaging and spectroscopy of atomically precise armchair graphene nanoribbons

absorption-spectroscopy

display_name: absorption-spectroscopy

graphene-nanoribbons

display_name: graphene-nanoribbons

optical-imaging

display_name: optical-imaging

snsf

display_name: snsf


en: The production of hydrogen fuels, via water splitting, is of practical relevance for meeting global energy needs and mitigating the environmental consequences of fossil-fuel-based transportation. Water photoelectrolysis has been proposed as a viable approach for generating hydrogen, provided that stable and inexpensive photocatalysts with conversion efficiencies over 10% can be discovered, synthesized at scale, and successfully deployed (Pinaud et al., Energy Environ. Sci., 2013, 6, 1983). While a number of first-principles studies have focused on the data-driven discovery of photocatalysts, in the absence of systematic experimental validation, the success rate of these predictions may be limited. We address this problem by developing a screening procedure with co-validation between experiment and theory to expedite the synthesis, characterization, and testing of the computationally predicted, most desirable materials. Starting with 70150 compounds in the Materials Project database, the proposed protocol yielded 71 candidate photocatalysts, 11 of which were synthesized as single-phase materials. Experiments confirmed hydrogen generation and favorable band alignment for 6 of the 11 compounds, with the most promising ones belonging to the families of alkali and alkaline-earth indates and orthoplumbates. This study shows the accuracy of a nonempirical, Hubbard-corrected density-functional theory method to predict band gaps and band offsets at a fraction of the computational cost of hybrid functionals, and outlines an effective strategy to identify photocatalysts for solar hydrogen generation.

en: Optimizing accuracy and efficacy in data-driven materials discovery for the solar production of hydrogen

density-functional-theory

display_name: density-functional-theory

dftu

display_name: dftu

high-throughput-screening

display_name: high-throughput-screening

marvel

display_name: marvel

photocatalysis

display_name: photocatalysis

water-splitting

display_name: water-splitting


en: Optical materials play a key role in enabling modern optoelectronic technologies in a wide variety of domains such as the medical or the energy sector. Among them, nonlinear optical crystals are of primary importance to achieve a broader range of electromagnetic waves in the devices. However, numerous and contradicting requirements significantly limit the discovery of new potential candidates, which, in turn, hinders the technological development. In the present work, the static nonlinear susceptibility and dielectric tensor are computed via density functional perturbation theory for a set of 579 inorganic semiconductors. The aim of this work is to provide a relevant dataset to foster the identification of promising nonlinear optical crystals in order to motivate their subsequent experimental investigation.

en: Second-harmonic generation tensors from high-throughput density-functional perturbation theory

density-functional-perturbation-theory

display_name: density-functional-perturbation-theory

first-principles

display_name: first-principles

non-linear-optics

display_name: non-linear-optics

second-harmonic-generation

display_name: second-harmonic-generation


en:
We present a computational screening of experimental structural repositories for fast Li-ion conductors, with the goal of finding new candidate materials for application as solid-state electrolytes in next-generation batteries. We start from ~1400 unique Li-containing materials, of which ~900 are insulators at the level of density-functional theory. For those, we calculate the diffusion coefficient in a highly automated fashion, using extensive molecular dynamics simulations on a potential energy surface (the recently published pinball model) fitted on first-principles forces. The ~130 most promising candidates are studied with full first-principles molecular dynamics, first at high temperature and then more extensively for the 78 most promising candidates. The results of the first-principles simulations of the candidate solid-state electrolytes found are discussed in detail.

Update April 2024:
Files are added that facilitate the Materials Cloud Archive OPTIMADE service to serve the structural data of this Archive entry via an OPTIMADE API. The molecular dynamics trajectories are served as individual structures per time-step.

en: High-throughput computational screening for solid-state Li-ion conductors

big-map

display_name: big-map

computational-high-throughput-screening

display_name: computational-high-throughput-screening

first-principles-molecular-dynamics

display_name: first-principles-molecular-dynamics

li-ion-conductors

display_name: li-ion-conductors

solid-state-electrolytes

display_name: solid-state-electrolytes


en: We perform a thorough structural search with the minima hopping method (MHM) to explore low-energy structures of methylammonium lead iodide. By combining the MHM with a forcefield, we efficiently screen vast portions of the configurational space with large simulation cells containing up to 96 atoms. Our search reveals two structures of methylammonium iodide perovskite (MAPI) that are substantially lower in energy than the well-studied experimentally observed low-temperature orthorhombic phase. The data set containing approximately ~180,000 crystal structures is provided.

en: Emergence of hidden phases of methylammonium lead-iodide (CH3NH3PbI) upon compression

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).

force-field-gulp

display_name: force-field-gulp

mapi-perovskites

display_name: mapi-perovskites

minima-hopping-method

display_name: minima-hopping-method

structure-prediction

display_name: structure-prediction

vasp-scan--functional

display_name: vasp-scan--functional


en:
This dataset provides DFT (density functional theory as implemented in VASP, Vienna Ab Initio Simulation Package) calculations for pure Magnesium. It was designed by Binglun Yin, Markus Stricker and William A. Curtin for fitting a neural network potential with Behler-Parrinello symmetry functions. Binglun Yin carried out the calculation.

It corresponds to a dataset that is commonly used to fit interatomic potentials for mechanics applications and includes structure-energy relationships for structures used to calculate:
1. Bulk properties
2. Generalized stacking fault energies
3. Decohesion and relaxed surfaces
4. Dimer
5. Corner and rod geometries
6. Vacancy formation energy

en: Pure Magnesium DFT calculations for interatomic potential fitting

materialscloud

en: Materials Cloud

decohesion

display_name: decohesion

dft

display_name: dft

elasticity

display_name: elasticity

epfl

display_name: epfl

magnesium

display_name: magnesium

metallurgy

display_name: metallurgy

snsf

display_name: snsf

stacking-faults

display_name: stacking-faults

surfaces

display_name: surfaces


en: Generalized stacking fault energy (GSFE) is a crucial material property for describing nanoscale plasticity in crystalline materials, such as dislocation dissociation, nucleation, and twinning. The dependence of the GSFE on applied stress normal to the stacking fault (SF) plane has been suggested to influence such phenomena. Here, the SF stress dependence is analyzed through (i) the generalized stacking fault potential energy (GSFE) and (ii) the generalized stacking fault enthalpy (GSFH). Our DFT calculations reveal that the GSFE is almost independent of the applied normal stress, which contradicts the long-standing wisdom and previous studies. We also reveal the inelastic inter-planar normal displacement associated with the SF. The coupling between the positive inelastic normal displacement and the applied normal stress decreases the GSFH.

en: Stress-dependence of generalized stacking fault energies: a DFT study

envi

en: Environment

erc

display_name: erc

generalized-stacking-fault-energy

display_name: generalized-stacking-fault-energy

stacking-fault-stress-dependence

display_name: stacking-fault-stress-dependence


en: The data contained in this record, raw data of images and input files to reproduce calculations, support our recent report for the on-surface synthesis and characterization of two ultralow-gap open-shell molecules, namely peri-tetracene, a benzenoid graphene fragment with zigzag edge topology, and dibenzo[a,m]dicyclohepta[bcde,nopq]rubicene, a nonbenzenoid nonalternant structural isomer of peri-tetracene with two embedded azulene units. Our results provide an understanding of the ramifications of altered bond topologies at the single-molecule scale, with the prospect of designing functionalities in carbon-based nanostructures via engineering of bond topology

en: Tailoring Bond Topologies in Open-Shell Graphene Nanostructures

atom-manipulation

display_name: atom-manipulation

density-functional-theory

display_name: density-functional-theory

scanning-tunneling-microscopy

display_name: scanning-tunneling-microscopy

scanning-tunneling-spectroscopy

display_name: scanning-tunneling-spectroscopy


en:
This data set contains atomic structures of liquid water and amorphous Li(x)Si in the XCrySDen structure format (XSF) [1]. Total energies are included as additional meta information. The extended XSF format is compatible with the Atomic Energy Network (ænet) package [2,3] for artificial neural network (ANN) potential construction and application.

Liquid water: The liquid water data set comprises energies and forces of 9,189 condensed-phase structures. The data was obtained in an iterative procedure described in detail in Ref. [4]. The final ANN potential was employed in Refs. [4,5].

Initial structures (iteration 0) were obtained from classical and path integral AIMD simulations of bulk liquid water in a cubic box containing 64 water molecules at 300 K as reported in Ref. [6]. Distorted configurations with higher forces were added by randomly displacing the Cartesian coordinates. Iteration 1 contains 500 configurations from MD simulations with the fully flexible SPC/E flex water model [7] employing a 25% increased water density (80 water molecules) and elevated temperatures (T= 500 K) to sample highly repulsive configurations. Structures in iteration 2 were obtained by classical MD simulations with preliminary ANN potentials at T= 300, 325, 350, and 370 K using cubic 64-molecule boxes and experimental densities. Iteration 3 data contains structures from preliminary ANN simulations with classical and quantum nuclei at a wide range of temperatures (T= 258, 268, 280, 290, 300, 310, 320, 330, 340, 350, 360, and 370 K) using experimental densities.

Energies and atomic forces were calculated with CP2K [8,9] using the revPBE exchange-correlation functional [10,11] with D3 dispersion correction [12] following Ref. [4]. Atomic cores were represented using the dual-space Goedecker-Teter-Hutter pseudopotentials [13], Kohn-Sham orbitals were expanded in the TZV2P basis set within the GPW method [14], and the density was represented by an auxiliary plane-wave basis with a cutoff of 400 Ry.

Amorphous LiSi: The amorphous LiSi data set comprises ~45,000 atomic bulk, surface, and cluster structures of elemental Li and Si, and Li(x)Si alloys of various compositions (0.0 ≤ x ≤ 4.75) as well as the corresponding energies and interatomic forces, which were generated using an iterative approach. An initial data set was based on the crystal structures of Li, Si, and the LiSi alloys with distorted lattice parameters and perturbed atomic positions. Additional partially delithiated alloy structures were generated using an evolutionary algorithm and further refined by including structures from ANN-potential MD simulations. Further details are given in references [15, 16].

The energies and forces of the LiSi structures were obtained from DFT calculations using the PBE [10] exchange-correlation functional and PAW pseudopotentials [17], as implemented in VASP [18,19]. We employed a plane-wave basis set with a cutoff of 520 eV and a uniform gamma-centered k-point grid with a mesh density corresponding to a number of k points of at least 1000 divided by the number of atoms. Most calculations in this data set were obtained from single-point DFT calculations. For ground state crystal structures, the atomic positions and lattice parameters were optimized until residual forces were below 20 meV/Å.

[1] A. Kokalj, J. Mol. Graphics Modell. 17, 176-179 (1999).
[2] N. Artrith, A. Urban, Comput. Mater. Sci. 114, 135-150 (2016).
[3] N. Artrith, A. Urban, G. Ceder, Phys. Rev. B 96, 014112 (2017).
[4] T. Morawietz, O. Marsalek, S. R. Pattenaude, L. M. Streacker, D. Ben-Amotz, and T. E. Markland, J. Phys. Chem. Lett. 9, 851 (2018).
[5] T. Morawietz, A. S. Urbina, P. K. Wise, X. Wu, W. Lu, D. Ben-Amotz, and T. E. Markland, J. Phys. Chem. Lett. 10, 6067 (2019).
[6] O. Marsalek and T. E. Markland, J. Phys. Chem. Lett. 8, 1545 (2017).
[7] X. B. Zhang, Q. L. Liu, and A. M. Zhu, Fluid Ph. Equilibria 262, 210 (2007).
[8] J. VandeVondele, M. Krack, F. Mohamed, M. Parrinello, T. Chassaing, and J. Hutter, Comput. Phys. Commun. 167, 103 (2005).
[9] J. Hutter, M. Iannuzzi, F. Schiffmann, and J. VandeVondele, WIRES Comput. Mol. Sci. 4, 15 (2014).
[10] J. P. Perdew, K. Burke, and M. Ernzerhof, Phys. Rev. Lett. 77, 3865 (1996).
[11] Y. Zhang and W. Yang, Phys. Rev. Lett. 80, 890 (1998).
[12] S. Grimme, J. Antony, S. Ehrlich, and H. Krieg, J. Chem. Phys. 132, 154104 (2010).
[13] S. Goedecker, M. Teter, and J. Hutter, Phys. Rev. B 54, 1703 (1996).
[14] B. G. Lippert, J. Hutter, and M. Parrinello, Mol. Phys. 92, 477 (1997).
[15] N. Artrith, A. Urban, G. Ceder, J. Chem. Phys. 148, 241711 (2018).
[16]  N. Artrith, A. Urban, Y. Wang, G. Ceder, https://arxiv.org/abs/1901.09272 (2019).
[17] P. E. Blöchl, Phys. Rev. B 50, 17953–17979 (1994).
[18] G. Kresse, J. Furthmüller, Phys. Rev. B 54, 11169-11186 (1996).
[19] G. Kresse, J. Furthmüller, Comput. Mater. Sci. 6, 15-50 (1996).

en: AENET-LAMMPS and AENET-TINKER: interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials

aenet

display_name: aenet

amorphous-lisi

display_name: amorphous-lisi

liquid-water

display_name: liquid-water

path-integral-aimd

display_name: path-integral-aimd

vasp

display_name: vasp


en: Nonempirical hybrid functionals are investigated for band-gap predictions of inorganic metal-halide perovskites belonging to the class CsBX3 , with B = Ge, Sn, Pb and X = Cl, Br, I. We consider both global and range-separated hybrid functionals and determine the parameters through two different schemes. The first scheme is based on the static screening response of the material and thus yields dielectric-dependent hybrid functionals. The second scheme defines the hybrid functionals through the enforcement of Koopmans' condition for localized defect states. We also carry out quasiparticle self-consistent GW calculations with vertex corrections to establish state-of-the-art references. For the investigated class of materials, dielectric-dependent functionals and those fulfilling Koopmans' condition yield band gaps of comparable accuracy (∼0.2 eV), but the former only require calculations for the primitive unit cell and are less subject to the specifics of the material.

en: Nonempirical hybrid functionals for band gaps of inorganic metal-halide perovskites

band-gaps

display_name: band-gaps

epfl

display_name: epfl

nonempirical-hybrid-functionals

display_name: nonempirical-hybrid-functionals

perovskites

display_name: perovskites


en: The effects of Zr doping on the stability of the CeO2(111) surface as a function of the dopant concentration and distribution, as well as on the relative stability of surface and subsurface oxygen vacancies, were studied by means of density functional theory (DFT+U) calculations. For a given Zr content, the more stable structures do not correspond to those configurations with Zr located in the topmost O-Ce-O trilayer (TL1), but in inner layers, and the stability decreases with increasing Zr concentration. For the undoped CeO2(111) surface, the preference of subsurface vacancies with next-nearest neighbor (NNN) Ce3+ configuration has earlier been predicted. For the Zr-doped surface, the formation of vacancies was studied using a surface unit cell with 2x2 periodicity, and it was found that the most stable configuration corresponds to the  Zr atom located in the surface layer (TL1) neighboring a subsurface oxygen vacancy with NNN Ce3+, being the  formation energy equal to 1.16 eV. The corresponding surface oxygen vacancy is 0.16 eV less stable. These values are by 0.73 and 0.92 eV, respectively, smaller than the corresponding ones for the pure CeO2(111) surface. Moreover, when Zr is located in TL2 the subsurface vacancy becomes by 0.10 eV less stable, compared to Zr in the TL1. The Ce3+ preference for the next-nearest neighbor cationic sites to both surface and subsurface vacancies at CeO2 (111), becomes more pronounced upon Zr doping. The results are explained in terms of Zr- and vacancy-induced lattice relaxation effects.

en: Relative Stability of Near-Surface Oxygen Vacancies at the CeO2 (111) Surface upon Zirconium Doping

dftu

display_name: dftu

oxygen-vacancy-formation

display_name: oxygen-vacancy-formation

surface-energy

display_name: surface-energy

zr-doping

display_name: zr-doping


en: The interactions between solute atoms and crystalline defects such as vacancies, dislocations, and grain boundaries are essential in determining alloy properties. Here we present a general linear correlation between two descriptors of local electronic structures and the solute-defect interaction energies in binary alloys of body-centered-cubic (bcc) refractory metals (such as W and Ta) with transition-metal substitutional solutes. One electronic descriptor is the bimodality of the d-orbital local density of states for a matrix atom at the substitutional site, and the other is related to the hybridization strength between the valance sp- and d-bands for the same matrix atom. For a particular pair of solute-matrix elements, this linear correlation is valid independent of types of defects and the locations of substitutional sites. These results provide the possibility to apply local electronic descriptors for quantitative and efficient predictions on the solute-defect interactions and defect properties in alloys.

en: Local electronic descriptors for solute-defect interactions in bcc refractory metals

bcc-refractory-alloys

display_name: bcc-refractory-alloys

electronic-structures

display_name: electronic-structures

first-principles-calculations

display_name: first-principles-calculations

solute-defect-interactions

display_name: solute-defect-interactions


en:
Many enhanced sampling techniques rely on the identification of a number of collective variables that describe all the slow modes of the system. By constructing a bias potential in this reduced space one is then able to sample efficiently and reconstruct the free energy landscape. In methods like metadynamics, the quality of these collective variables plays a key role in convergence efficiency. Unfortunately in many systems of interest it is not possible to identify an optimal collective variable, and one must deal with the non-ideal situation of a system in which some slow modes are not accelerated.
We propose a two-step approach in which, by taking into account the residual multiscale nature of the problem, one is able to significantly speed up convergence. To do so, we combine an exploratory metadynamics run with an optimization of the free energy difference between metastable states, based on the recently proposed variationally enhanced sampling method. This new method is well parallelizable and is especially suited for complex systems, because of its simplicity and clear underlying physical picture.

en: Making the best of a bad situation: a multiscale approach to free energy calculation

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).

enhanced-sampling

display_name: enhanced-sampling

free-energy

display_name: free-energy

metadynamics

display_name: metadynamics


en: Quantum spin Hall insulators (QSHIs) make up a class of two-dimensional materials with a finite electronic band gap in the bulk and gapless helical edge states. Some of the phenomena that can be hosted in these materials, from one-dimensional low-dissipation electronic transport to spin filtering, could be promising for many technological applications in the fields of electronics, spintronics, and topological quantum computing. Nevertheless, the rarity of two-dimensional materials that can exhibit nontrivial topological order at room temperature hinders development. In the publication, we report on our screening of a comprehensive database we recently identified of 1825 monolayers that can be exfoliated from experimentally known compounds to search for novel quantum spin Hall insulators. In this entry we provide the AiiDA database with the calculations of the DFT band structures (both with and without spin-orbit coupling) and the DFPT phonon dispersions for the QSHI candidates that we identify. All the data are also displayed in the dedicated DISCOVER section "2D topological insulators" https://www.materialscloud.org/discover/2dtopo/dashboard/ptable .

en: Relative abundance of Z2 topological order in exfoliable two-dimensional insulators

materialscloud

en: Materials Cloud

dfpt

display_name: dfpt

dft

display_name: dft

first-principles

display_name: first-principles

prace

display_name: prace

quantum-spin-hall

display_name: quantum-spin-hall

snsf

display_name: snsf

spin-orbit

display_name: spin-orbit

topological-insulator

display_name: topological-insulator


en: Applications of machine-learning (ML) techniques to the study of catalytic processes have begun to appear in the literature with increasing frequency. The computational speed up provided by ML allows the properties and energetics of thousands of prospective catalysts to be rapidly assessed. These results, once compiled into a database containing different properties, can be mined with the goal of establishing relationships between the intrinsic chemical properties of different catalysts and their overall catalytic performance. Previously, we applied ML models to predict the performance of 18,000 prospective catalysts for a Suzuki coupling reaction using molecular volcano plots. Here, we expand on our earlier work by examining a larger section of the C-C cross-coupling genome by using a dimensionality-reducing data-clustering algorithms (i.e., sketch-map) to, first, identify the compatibility of each catalyst with different C-C cross-coupling variants (e.g., Suzuki, Kumada, Negishi, Stille, and/or Hiyama) and, second, to uncover links between the chemical property of a catalyst and its catalytic activity. Our findings, based on the analysis of 18,000 catalysts, reveal strong correlations between a catalyst's HOMO energy and the suitability of its thermodynamic profile. These values can, subsequently, be tuned in order to maximize the thermodynamics of the catalytic cycle through the judicious choice of metal centers and the π-accepting/σ-donating nature of the flanking ligands. Overall, group 10 metals (Ni, Pd, Pt) are best coupled with the strong π-acceptor ligands and group 11 metals (Cu, Ag, Au) with weak π-acceptors, which maximize the thermodynamic drive of the catalytic cycle.

en: Mining the C-C Cross-Coupling Genome using Machine Learning

envi

en: Environment

homogeneous-catalysis

display_name: homogeneous-catalysis

machine-learning

display_name: machine-learning

sketch-map

display_name: sketch-map

transition-metal-complexes

display_name: transition-metal-complexes

volcano-plot

display_name: volcano-plot


en:
We present a method for performing multithermal-multibaric molecular dynamics simulations that sample entire regions of the temperature-pressure (TP) phase diagram. The method uses a variational principle [Valsson and Parrinello, Phys. Rev. Lett. 113, 090601 (2014)] in order to construct a bias that leads to a uniform sampling in energy and volume. The intervals of temperature and pressure are taken as inputs and the relevant energy and volume regions are determined on the fly. In this way the method guarantees adequate statistics for the chosen TP region. We show that our multithermal-multibaric simulations can be used to calculate all static physical quantities for all temperatures and pressures in the targeted region of the TP plane. We illustrate our approach by studying the density anomaly of TIP4P/Ice water.

This record includes input and output files, and Jupyter Notebooks describing the analysis of the simulations and the creation of the figures for the paper.

en: Multithermal-multibaric molecular simulations from a variational principle

density-anomaly

display_name: density-anomaly

multicanonical

display_name: multicanonical

multithermal-multibaric

display_name: multithermal-multibaric

wang-landau

display_name: wang-landau

water

display_name: water


en:
Knowledge of the oxidation state of a metal centre in a material is essential to understand its properties. Chemists have developed theories to predict the oxidation state based on counting rules, which can fail to describe the oxidation states of systems such as metal-organic frameworks.
Here we present a data-driven approach to automatically assign oxidation states, using a machine learning model trained on assignments by chemists encoded in the chemical names in the Cambridge Crystallographic Database.
Our approach only considers the immediate local environment around a metal centre, and is robust to experimental uncertainties (like incorrect protonation, unbound solvents, or changes in bondlength).
We find such excellent accuracy in our predictions that we can use our method to detect incorrect assignments.
This work nicely illustrates how powerful the collective knowledge of chemists is. Machine learning can harvest this knowledge and convert it into a useful tool for chemists.

en: Using collective knowledge to assign oxidation states

epfl

display_name: epfl

erc

display_name: erc

lsmo

display_name: lsmo

ml

display_name: ml

mof

display_name: mof

oxidation-state

display_name: oxidation-state


en: The average energy curvature as a function of the particle number is a molecule-specific quantity, which measures the deviation of a given functional from the exact conditions of density functional theory (DFT). Related to the lack of derivative discontinuity in approximate exchange-correlation potentials, the information about the curvature has been successfully used to restore the physical meaning of Kohn-Sham orbital eigenvalues and to develop non-empirical tuning and correction schemes for density functional approximations. In this work, we propose the construction of a machine-learning framework targeting the average energy curvature between the neutral and the radical cation state of thousands of small organic molecules (QM7 database). The applicability of the model is demonstrated in the context of system-specific gamma-tuning of the LC-ωPBE functional and validated against the molecular first ionization potentials at equation-of-motion (EOM) coupled-cluster references. In addition, we propose a local version of the non-linear regression model and demonstrate its transferability and predictive power by determining the optimal range-separation parameter for two large molecules relevant to the field of hole-transporting materials. Finally, we explore the underlying structure of the QM7 database with the t-SNE dimensionality-reduction algorithm and identify structural and compositional patterns that promote the deviation from the piecewise linearity condition.

en: Learning the energy curvature versus particle number in approximate density functionals

density-functional-theory

display_name: density-functional-theory

energy-curvature

display_name: energy-curvature


en: MgTa2N3 is predicted to host the topological Dirac semimetal phase. This archive includes input data necessary for reproducing first-principles calculation described in the publication.

en: MgTa2N3: A reference Dirac semimetal

dirac-semimetal

display_name: dirac-semimetal

marvel

display_name: marvel

topological-materials

display_name: topological-materials

topological-semimetal

display_name: topological-semimetal

vasp

display_name: vasp

wanniertools

display_name: wanniertools


a-unified-approach-to-enhanced-sampling

en: The sampling problem lies at the heart of atomistic simulations and over the years many different enhanced sampling methods have been suggested towards its solution. These methods are often grouped into two broad families. On the one hand methods such as umbrella sampling and metadynamics that build a bias potential based on few order parameters or collective variables. On the other hand tempering methods such as replica exchange that combine different thermodynamic ensembles in one single expanded ensemble. We adopt instead a unifying perspective, focusing on the target probability distribution sampled by the different methods. This allows us to introduce a new method that can sample any of the ensembles normally sampled via replica exchange, but does so in a collective-variables-based scheme. This method is an extension of the recently developed on-the-fly probability enhanced sampling method [Invernizzi and Parrinello, J. Phys. Chem. Lett. 11.7 (2020)] that has been previously used for metadynamics-like sampling. The method is thus very general and can be used to achieve different types of enhanced sampling. It is also reliable and simple to use, since it presents only few and robust external parameters and has a straightforward reweighting scheme. Furthermore, it can be used with any number of parallel replicas. We show the versatility of our approach with applications to multicanonical and multithermal-multibaric simulations, thermodynamic integration, umbrella sampling, and combinations thereof.

en: A unified approach to enhanced sampling

title_for_slug: a-unified-approach-to-enhanced-sampling

expanded-ensembles

display_name: expanded-ensembles

free-energy

display_name: free-energy

importance-sampling

display_name: importance-sampling

metadynamics

display_name: metadynamics

parallel-tempering

display_name: parallel-tempering

replica-exchange

display_name: replica-exchange

snsf

display_name: snsf

thermodynamic-integration

display_name: thermodynamic-integration

umbrella-sampling

display_name: umbrella-sampling


en: Porous molecular crystals are an emerging class of porous materials formed by crystallisation of molecules with weak intermolecular interactions, which distinguishes them from extended nanoporous materials like metal-organic frameworks (MOFs). To aid discovery of porous molecular crystals for desired applications, energy-structure-function (ESF) maps were developed that combine a priori prediction of both the crystal structure and its functional properties. However, it is a challenge to represent the high-dimensional structural and functional landscapes of an ESF map and to identify energetically favourable and functionally interesting polymorphs among the 1,000s-10,000s of structures typically on a single ESF map. Here, we introduce geometric landscapes, a representation for ESF maps based on geometric similarity, quantified by persistent homology. We show that this representation allows the exploration of complex ESF maps, automatically pinpointing interesting crystalline phases available to the molecule. Furthermore, we show that geometric landscapes can serve as an accountable descriptor for porous materials to predict their performance for gas adsorption applications. A machine learning model trained using this geometric similarity could reach a remarkable accuracy in predicting the materials' performance for methane storage applications.

en: Geometric landscapes for material discovery within energy-structure-function maps

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).

crystal-structure-prediction

display_name: crystal-structure-prediction

machine-learning

display_name: machine-learning

persistent-homology

display_name: persistent-homology

porous-molecular-crystals

display_name: porous-molecular-crystals


en: In this record we provide data supporting our recent work on coupled spin states in armchair nanoribbons. Exact positioning of sublattice imbalanced nanostructures in graphene nanomaterials offers a route to control interactions between induced local magnetic moments and to obtain graphene nanomaterials with magnetically nontrivial ground states. Our results reveal that such sublattice imbalanced nanostructures can be incorporated along a large band gap armchair graphene nanoribbon on the basis of asymmetric zigzag edge extensions, achieved by incorporating specifically designed precursor monomers. Scanning tunneling spectroscopy of an isolated and electronically decoupled zigzag edge extension reveals Hubbard-split states in accordance with theoretical predictions. Mean-field Hubbard-based modeling of pairs of such zigzag edge extensions reveals ferromagnetic, antiferromagnetic, or quenching of the magnetic interactions depending on the relative alignment of the asymmetric edge extensions. Moreover, a ferromagnetic spin chain is demonstrated for a periodic pattern of zigzag edge extensions along the nanoribbon axis.

en: Coupled spin states in armchair graphene nanoribbons with asymmetric zigzag edge extensions

materialscloud

en: Materials Cloud

carbon-magnetism

display_name: carbon-magnetism

cscs

display_name: cscs

graphene-nanoribbon

display_name: graphene-nanoribbon

on-surface-synthesis

display_name: on-surface-synthesis


en: Poly(triazine imide) or PTI is a promising material for molecular sieving membranes, thanks to its atom-thick ordered lattice with an extremely high density (1.6 × 10^14 pores/cm2) of triangular-shaped nanopores of ~0.34 nm diameter. Here, we investigate the application of PTI nanopores in the purification of Kr from Xe to reduce the storage volume of the mixture of 85Kr/Xe. Using van-der-Waals density-functional theory (vdW-DFT) calculations, benchmarked against the random phase approximation (RPA), we calculate the potential energy profiles for Kr and Xe across the nanopores. For each gas, starting from the RPA potential-energy profile, the force-field parameters to be used in the classical molecular dynamics framework are trained to calculate the Helmholtz free energy barrier as a function of temperature, and therefore, the corresponding entropic loss. Overall, due to the much higher activation energy from the adsorbed state in Xe (17.61 and 42.10 kJ/mole for Kr and Xe, respectively), a large Kr/Xe separation selectivity is postulated from the PTI membrane. Furthermore, the combination of atom-thick PTI lattice and high pore density leads to extremely large yet selective permeances for Kr. For example, Kr permeance of 1000 gas permeation units (GPU) accompanying a large Kr/Xe selectivity (>10000) is calculated at 25 °C, significantly better than the state-of-the-art membranes for Kr/Xe separation, making PTI-based membranes a leading candidate to process the hazardous waste of 85Kr/Xe mixture.

en: Efficient Kr/Xe separation from triangular g-C3N4 nanopores: density-functional theory calculations benchmarked with random phase approximation

envi

en: Environment

carbon-nitrides

display_name: carbon-nitrides

density-functional-theory

display_name: density-functional-theory

epfl

display_name: epfl

marvel

display_name: marvel

nanoscale-transport

display_name: nanoscale-transport

random-phase-approximation

display_name: random-phase-approximation

van-der-waals-approximations

display_name: van-der-waals-approximations


en: Multiple mechanisms for extremely large magnetoresistance (XMR) found in many topologically nontrivial/trivial semimetals have been theoretically proposed, but experimentally it is unclear which mechanism is responsible in a particular sample. In this paper, by the combination of band structure calculations, numerical simulations of magnetoresistance (MR), Hall resistivity, and de Haas-van Alphen (dHvA) oscillation measurements, we studied the MR anisotropy of SiP2 which is verified to be a topologically trivial, incomplete compensation semimetal. It was found that as magnetic field H is applied along the a-axis, the MR exhibits an unsaturated nearly linear H dependence, which was argued to arise from incomplete carriers compensation. For the H // [101] orientation, an unsaturated nearly quadratic H dependence of MR up to 5.88 × 10^4%(at 1.8 K, 31.2 T) and field-induced up-turn behavior in resistivity were observed, which was suggested due to the existence of hole open orbits extending along the kx direction. Good agreement of the experimental results with the simulations based on the calculated Fermi surface (FS) indicates that the topology of FS plays an important role in its MR.

en: Linear and quadratic magnetoresistance in the semimetal SiP2

magnetoresistance

display_name: magnetoresistance

wanniertools

display_name: wanniertools


en:
This record contains the data supporting our recent findings on electron-phonon coupling during photoinduced phase transition.
We measure mode- and band-selective electron-phonon couplings during the photoinduced insulator-to-metal phase transition in Ta2NiSe5 (TNS) by frequency-domain angle-resolved photoemission spectroscopy (FDARPES).
FDARPES gives us rich information about which band more couples which phonon mode by seeing frequency components of time-resolved angle-resolved photoemission spectra.
The experiments indicate 2 THz and 3 THz phonon modes associated with the metallic and semiconducting phases.
To get a more atomistic picture of the oscillation, we perform phonon-mode calculations relying on the density-functional theory (DFT).

The computational scheme itself is very standard that density-functional-perturbation theory (DFPT) with semilocal or local exchange-correlation functionals.
However, the required computational resources were rather huge, 25,000 core-hour, for a single DFPT with atomic position optimization having 0.1 eV/nm force accuracy for more satisfactory computational parameters.
Therefore, the data must be worth as a benchmark within DFT-level calculation for TNS.

en: Detecting electron-phonon coupling during photoinduced phase transition

dfpt

display_name: dfpt

dft

display_name: dft

electron-phonon-coupling

display_name: electron-phonon-coupling

experimental

display_name: experimental

fdarpes

display_name: fdarpes


en: Donor–acceptor (D–A) copolymers have shown great potential for intramolecular singlet fission (iSF). Nonetheless, very few design principles exist for optimizing these systems for iSF, with very little knowledge about how to engineer them for this purpose. In recent work, a fundamental trade-off between the main electronic ingredients required for iSF capable D–A coplanar copolymers was revealed. Still, further investigations are needed to understand these limitations and learn how to bypass them. In this work, we propose to induce torsion as an effective way to circumvent the limits of the coplanar approach. We disclose the potential of noncoplanar copolymers with inherently low triplet energies that encompass all the characteristics required for iSF beyond the limiting values associated with fully coplanar systems. Our findings shed some light on the electronic structure aspects of D–A copolymers for iSF and offer a new avenue for the rational design of novel promising candidates.

en: Pushing the limits of the donor-acceptor copolymer strategy for intramolecular singlet fission

donor-acceptor-copolymers

display_name: donor-acceptor-copolymers

electronic-structure

display_name: electronic-structure

marie-curie-fellowship

display_name: marie-curie-fellowship

singlet-fission

display_name: singlet-fission


en: Obtaining a precise theoretical description of the spectral properties of liquid water poses challenges for both molecular dynamics (MD) and electronic structure methods. The lower computational cost of the Koopmans-compliant functionals with respect to Green's function methods allows the simulations of many MD trajectories, with a description close to the state-of-art quasi-particle self-consistent GW plus vertex corrections method (QSGW + fxc). Thus, we explore water spectral properties when different MD approaches are used, ranging from classical MD to first-principles MD, and including nuclear quantum effects. We have observed that different MD approaches lead to up to 1 eV change in the average band gap; thus, we focused on the band gap dependence with the geometrical properties of a system to explain such spread. We have evaluated the changes in the band gap due to variations in the intramolecular O–H bond distance and HOH angle, as well as the intermolecular hydrogen bond O···O distance and the OHO angles. We have observed that the dominant contribution comes from the O–H bond length; the O···O distance plays a secondary role, and the other geometrical properties do not significantly influence the gap. Furthermore, we analyze the electronic density of states (DOS), where the KIPZ functional shows good agreement with the DOS obtained with state-of-art approaches employing quasi-particle self-consistent GW plus vertex corrections. The O–H bond length also significantly influences the DOS. When nuclear quantum effects are considered, broadening of the peaks driven by the broader distribution of the O–H bond lengths is observed, leading to a closer agreement with the experimental photoemission spectra.

en: Electronic structure of water from Koopmans-compliant functionals

band-gap

display_name: band-gap

molecular-dynamics

display_name: molecular-dynamics

nuclear-quantum-effects

display_name: nuclear-quantum-effects

water

display_name: water


en: To examine whether suitable conditions occur for the water splitting reaction at their interfaces with liquid water, we determine the pH-dependent surface coverage for a series of semiconductors, including GaAs, GaP, GaN, CdS, ZnO, SnO2, rutile and anatase TiO2. For this we calculate acidity constants at surface sites through ab initio molecular dynamics simulations and a grand-canonical formulation of adsorbates. The resulting pH values at the point of zero charge show excellent agreement with experiment and thereby support the validity of our approach. By combining information concerning the surface coverage with the alignment of the band edges with respect to the relevant redox levels, we scrutinize the potential of the considered semiconductors as photocatalysts and identify the corresponding optimal pH ranges for hydrogen and oxygen evolution. More specifically, our results indicate that GaN stands out among these semiconductors as the most promising candidate for the overall water splitting, with the potential of further improvement through alloying. With the surface coverage, our computational analysis brings a new descriptor that is at present beyond experimental reach.

en: Evaluation of photocatalysts for water splitting through combined analysis of surface coverage and energy-level alignment

cscs

display_name: cscs

deprotonation-free-energy

display_name: deprotonation-free-energy

epfl

display_name: epfl

marvel

display_name: marvel

md-trajectory

display_name: md-trajectory

semiconductor-water-interface

display_name: semiconductor-water-interface

thermodynamic-integration

display_name: thermodynamic-integration


en: Trajectories and spin densities for the bulk hydrated electron at the MP2 level of theory.  The data represent the first ab initio molecular dynamics study of the hydrated electron in the bulk using many-body wave function theory.

en: Dynamics of the Bulk Hydrated Electron from Many‐Body Wave‐Function 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).

hydrated-electron

display_name: hydrated-electron

many-body-methods

display_name: many-body-methods

solvated-electron

display_name: solvated-electron

spin-density

display_name: spin-density


en: Density functional theory calculations were used to obtain the generalized stacking fault energy surfaces in eight FCC metals: Ag, Au, Cu, Ir, Ni, Pd, Pt, and Rh. Among them, ferromagnetism was considered only in Ni. Results based on non-magnetic Ni are denoted as Ni-NM.

en: Generalized stacking fault energy surfaces for eight face-centered cubic transition metals

materialscloud

en: Materials Cloud

density-functional-theory

display_name: density-functional-theory

face-centered-cubic-transition-metals

display_name: face-centered-cubic-transition-metals

generalized-stacking-fault-energies

display_name: generalized-stacking-fault-energies


en: In this record we provide data to support our recent findings for the magnetic properties of rhombus-shaped nanographenes. Nanographenes with zigzag edges are predicted to manifest non-trivial π-magnetism resulting from the interplay of concurrent electronic effects, such as hybridization of localized frontier states and Coulomb repulsion between valence electrons. This provides a chemically tunable platform to explore quantum magnetism at the nanoscale and opens avenues towards organic spintronics. The magnetic stability in nanographenes is thus far greatly limited by the weak magnetic exchange coupling, which remains below the room-temperature thermal energy. In our work, we report the synthesis of large rhombus-shaped nanographenes with zigzag peripheries on gold and copper surfaces. Single-molecule scanning probe measurements show an emergent magnetic spin singlet ground state with increasing nanographene size. The magnetic exchange coupling in the largest nanographene (C70H22, containing five benzenoid rings along each edge), determined by inelastic electron tunnelling spectroscopy, exceeds 100 meV or 1,160 K, which outclasses most inorganic nanomaterials and survives on a metal electrode.

en: Large magnetic exchange coupling in rhombus-shaped nanographenes with zigzag periphery

envi

en: Environment

ab-initio

display_name: ab-initio

graphene-flagship

display_name: graphene-flagship

magnetic-carbon

display_name: magnetic-carbon

nanographenes

display_name: nanographenes

snsf

display_name: snsf


en: This entry includes the MD trajectories of several semiconductor-water interfaces generated with ab initio molecular dynamics using the rVV10 density functional at the temperature of 350 K. Eight semiconductor surfaces are considered, namely GaAs(110), GaP(110), GaN(10-10), CdS(10-10), ZnO(10-10), SnO2(110), rutile TiO2(110) and anatase TiO2(101). For GaAs, GaP and anatase TiO2, the trajectories for the interfaces with both the molecular and the dissociative adsorption mode of water are provided. In addition, the relaxed atomic structures of the semiconductor surfaces used to calculate the ionization potential (IP) reported in [Chem. Mater. 2018, 30, 94−111] are added.

en: MD trajectories of semiconductor-water interfaces and relaxed atomic structures of semiconductor surfaces


en: The ab initio determination of the character and properties of electronic excited states (ES) is the cornerstone of modern theoretical photochemistry. Yet, traditional ES methods become readily impractical when applied to fairly large molecules, or when used on thousands of different systems. In contrast, Machine Learning (ML) techniques have demonstrated their accuracy at retrieving ES properties of large molecular databases at a reduced computational cost. Especially for excited states applications, non-linear algorithms tend to be specialized and to target only individual properties. Learning fundamental quantum chemical objects potentially represents a more efficient, yet complex, alternative as a large number of molecular properties could be then extracted through post-processing. Herein, we report the general framework able to learn three fundamental objects of an ES: the hole and particle densities, as well as the transition density. We demonstrate the advantages of targeting those outputs of an ES computation, and address the additional complexity of learning 3-dimensional scalar fields. We apply our predictions to obtain a list of derived properties, including the state character and the topological descriptors of the exciton, for the two bands (nπ* and ππ*) of a collection of 3432 azoheteroarene photoswitches. In doing so, we prove that the proposed approach offers a way to access ES information of different kinds, bypassing the need of traditional electronic structure computations, while retaining ab initio accuracy.

en: Learning the exciton properties of azo-dyes

azo-dyes

display_name: azo-dyes

epfl

display_name: epfl

erc

display_name: erc

hole-and-particle-densities

display_name: hole-and-particle-densities

machine-learning

display_name: machine-learning

photoswitches

display_name: photoswitches


en: Group 15 elements in zero oxidation state (P, As, Sb and Bi), also called pnictogens, are rarely used in catalysis due to the difficulties associated in preparing well–structured and stable materials. Here, we report on the synthesis of highly exfoliated, few layer 2D phosphorene and antimonene in zero oxidation state, suspended in an ionic liquid, with the native atoms ready to interact with external reagents while avoiding aerobic or aqueous decomposition pathways, and on their use as efficient catalysts for the alkylation of nucleophiles with esters. The few layer pnictogen material circumvents the extremely harsh reaction conditions associated to previous superacid–catalyzed alkylations, by enabling an alternative mechanism on surface, protected from the water and air by the ionic liquid. These 2D catalysts allow the alkylation of a variety of acid–sensitive organic molecules and giving synthetic relevancy to the use of simple esters as alkylating agents.

en: Few layer 2D pnictogens catalyze the alkylation of soft nucleophiles with esters.

alkylation

display_name: alkylation

antimonene

display_name: antimonene

black-phosphorous

display_name: black-phosphorous

esters

display_name: esters

ionic-liquids

display_name: ionic-liquids


en: We investigate twisted double bilayer graphene (TDBG), a four-layer system composed of two AB-stacked graphene bilayers rotated with respect to each other by a small angle. Our ab-initio band structure calculations reveal a considerable energy gap at the charge point neutrality that we assign to the intrinsic symmetric polarization (ISP). We then introduce the ISP effect into the tight-binding parameterization and perform calculations on TDBG models that include lattice relaxation effects down to very small twist angles. We identify a narrow region around the magic angle θ*= 1.3° characterized by a manifold of remarkably flat bands gapped out from other states even without external electric fields. To understand the fundamental origin of the magic angle in TDBG, we construct a continuum model that points to a hidden mathematical link to the twisted bilayer graphene (TBG) model, thus indicating that the band flattening is a fundamental feature of TDBG, and is not a result of external fields.

en: Moiré Flat Bands in Twisted Double Bilayer Graphene

continuum-model

display_name: continuum-model

flat-bands

display_name: flat-bands

magic-angle

display_name: magic-angle

moire-superlattice

display_name: moire-superlattice

tight-binding-model

display_name: tight-binding-model

twisted-double-bilayer-graphene

display_name: twisted-double-bilayer-graphene

wanniertools

display_name: wanniertools


en: We present a database of energy and NMR chemical shifts DFT calculations of 4150 crystal organic solids. The structures contain only H/C/N/O/S atoms and were subject to all-atoms geometry optimisation. Calculations were carried out using Quantum Espresso and GIPAW.

en: Chemical Shifts in Molecular Solids by Machine Learning Datasets

chemical-shifts

display_name: chemical-shifts

energy-calculations

display_name: energy-calculations

gipaw

display_name: gipaw

nmr

display_name: nmr

organic-solids

display_name: organic-solids

quantum-espresso

display_name: quantum-espresso

snsf

display_name: snsf