en: This repository accompanies the manuscript “Optical selection rules in hexagonal Ge polytypes and their lifting by symmetry perturbation” and contains vasprun.xml files of all calculations. The manuscript presents DFT calculations made with VASP of the 2H, 4H and 6H polytypes of Ge and SiGe and discusses their electronic and optical properties. The first part presents transition matrix elements and the radiative lifetimes. The second part presents the dielectric functions and absorptions of the structures from model BSE calculations.

en: Data availability for "Optical selection rules in hexagonal Ge polytypes and their lifting by symmetry perturbation"

condensed-matter-physics

display_name: condensed-matter-physics

dft

display_name: dft

hexagonal-ge

display_name: hexagonal-ge

model-bse

display_name: model-bse

optoelectronic-materials

display_name: optoelectronic-materials

polytypes

display_name: polytypes


$oid: OBSOLETE

en: Interatomic potentials remain a critical bottleneck for accurate large-scale atomistic simulations. Machine-learning potentials provide a promising route to overcoming this limitation; however, the construction of property-relevant training datasets remains a major challenge. Here, we propose an active-learning strategy based on property balancing, in which the fraction of atomic environments associated with pre-identified key material properties is dynamically optimized during training. We demonstrate this strategy for pure magnesium, whose macroscopic plastic deformation is governed by several subtle dislocation transformations that place stringent demands on potential accuracy. Using the proposed strategy, we develop a Behler–Parrinello-type neural-network potential that captures these dislocation-mediated mechanisms with a level of accuracy that has been difficult to achieve in previous studies of magnesium. The successful application to magnesium highlights the versatility and effectiveness of property balancing as a general strategy for developing machine-learning potentials for metallic systems with multiple competing property requirements. This approach provides a practical pathway toward predictive mechanistic simulations of structural metals.

en: Property-balancing active learning strategy for machine learning interatomic potentials: case study on dislocation mechanics in magnesium

materialscloud

approval_status: approved

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

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

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

dislocation

display_name: dislocation

magnesium

display_name: magnesium

neural-network-potential

display_name: neural-network-potential


en: Functionalizing Silicon Nanowires (SiNWs) through covalent attachment of organic molecules offers diverse advantages, including surface passivation, introduction of new functionalities, and enhanced material performance in applications like electronic devices and biosensors. Given the wide range of available functional molecules, systematic large-scale screening is crucial. Therefore, we developed an automated computational workflow using Python scripts in conjunction with the AiiDa framework to explore structural configurations of functional molecules adsorbed onto silicon surfaces. This workflow generates multiple adhesion configurations corresponding to different binding orientations using surface and functional molecule structures as inputs. This dataset contains data related to the structural optimization of molecules with single, double, and triple carbon-carbon bonds attached to the nanowire surface in various adhesion configurations. We describe the chemisorption on SiNWs using the slab models for the Si facets since our reference are samples with diameters of SiNWs around 50 nm, while the quantum confinement effects are important for diameters below 10 nm. For each configuration, structural characterization was conducted by calculating quantities including the bond distance between the two carbons closest to the surface and their respective bond angle relative to the z-axis, the carbon-silicon bond distance and its respective bond angle relative to the z-axis, along with the molecule's rotation angle in the xy plane. The values obtained are summarized in the main folder. The version v1 of dataset contains data related to the Si(111) surface and alkanes, alkenes, and alkynes with lengths from C2 to C10. The version v2 and v3 include also the moieties from C12 to C18. The version v4 extends the dataset with the data related to the Si(110) surface and alkanes, alkenes, and alkynes with lengths from C2 to C18. The version v5 also includes the optimized structures of SiNWs oriented along ⟨112⟩, exposing Si(110) and Si(111) facets, and featuring three different sizes: small (d = 0.7 nm), medium (d = 1.7 nm), and large (d = 2.6 nm). In this version (v6) the relaxation calculations for all the considered moieties are provided. For each system the most stable configuration is identified, and the analysis of the electronic properties is conducted in the listed references.

en: Density functional theory study of silicon nanowire surfaces functionalized by grafting organic molecules

aliphatic-molecules

display_name: aliphatic-molecules

nanowires

display_name: nanowires

silicon

display_name: silicon

surface-adsorption

display_name: surface-adsorption


en: Abstract [Accepted in Phys. Rev. Lett.]: Accurate prediction of carrier mobility is critical for the discovery and design of next-generation electronic materials. Despite sustained progress, state-of-the-art ab initio methods remain limited by the approximate treatment of electron-phonon interactions at the density functional theory level. Here, we demonstrate that incorporating many-body GW corrections to both the electronic band structure and electron-phonon couplings when solving the ab initio Boltzmann transport equation yields a mean absolute relative error of just 11% for electron mobilities across benchmark semiconductors, including Si, GaAs, GaP, diamond, and SiC. The common practice of neglecting GW corrections to the electron–phonon interaction can lead to mobility errors exceeding 50%. The present findings highlight the importance of many-body self-energy effects in carrier transport simulations, and provides fundamental insights into how many-body electron--phonon interactions govern charge transport in crystalline solids.

en: Supporting data for "Many-body first principles calculations of carrier mobility with record accuracy using GW perturbation theory"

aibte

display_name: aibte

carrier-transport

display_name: carrier-transport

electronic-structure

display_name: electronic-structure

gw

display_name: gw

gwpt

display_name: gwpt

mobility

display_name: mobility

quantum-many-body

display_name: quantum-many-body

semiconductors

display_name: semiconductors


en:
This dataset supports a computational study of finite-concentration pair breaking in Mn- and Co-doped 2H-NbSe2 using the coherent potential approximation combined with Bogoliubov-de Gennes calculations within the KKR Green-function framework. The work investigates how magnetic impurities located on the van der Waals empty-sphere site modify the superconducting anomalous density of 2H-NbSe2 from the dilute impurity limit up to the critical concentration where superconductivity collapses.

The dataset contains the full AiiDA provenance needed to reproduce the calculations reported in the companion manuscript. It includes the pristine superconducting 2H-NbSe2 reference calculation, normal-state CPA KKR calculations for Mn and Co impurities, and self-consistent CPA-BdG concentration series for both dopants. These data reproduce the concentration-dependent normalized anomalous density, the impurity-dependent critical concentrations, and the comparison between Mn- and Co-induced changes in the superconducting response.

All calculations were performed with the JuKKR Green-function code through the AiiDA-KKR workflow infrastructure on the JURECA-DC supercomputer at Forschungszentrum Jülich, with additional computational resources from CLAIX at RWTH Aachen University. The record is intended to provide transparent access to the computational provenance, input and output data, and analysis workflow underlying the reported CPA-BdG results.

en: CPA-BdG calculations for Mn- and Co-doped 2H-NbSe2: finite-concentration pair breaking and anomalous density

aiida

display_name: aiida

anomalous-density

display_name: anomalous-density

bogoliubov-de-gennes-bdg

display_name: bogoliubov-de-gennes-bdg

co

display_name: co

coherent-potential-approximation-cpa

display_name: coherent-potential-approximation-cpa

kkr-green-function-method

display_name: kkr-green-function-method

magnetic-impurities

display_name: magnetic-impurities

mn

display_name: mn

pair-breaking

display_name: pair-breaking

spin-orbit-coupling

display_name: spin-orbit-coupling

superconductivity

display_name: superconductivity

transition-metal-dichalcogenide

display_name: transition-metal-dichalcogenide

yu-shiba-rusinov-states

display_name: yu-shiba-rusinov-states


en: Topological constraints such as the Bernal-Fowler ice rules govern atomic arrangements in proton-disordered crystals. Machine learning force fields (MLFFs) provide a computationally efficient route to exploring such disordered energy landscapes using on-the-fly sampling without explicit topological supervision. Whether such a data-driven approach can autonomously recover ice-rule constraints remains an open question. Here, we show that a data-efficient GAP-SOAP force field trained on 212 DFT calculations achieves a energy RMSE of 19 meV/H2O on Ice VII configurations and reproduces phonon dispersions and infrared spectra in good agreement with DFT. A representative trained model relaxes 8 × 8 × 8 supercells (3072 atoms) with randomized proton orientations into structures consistent with ice-rule constraints, recovering a body-centred cubic oxygen lattice at 30 GPa. Under compression, the model captures the qualitative features of the Ice VII → Ice X transformation and remains stable over the 30–90 GPa range for the tested configurations. While extended training does not guarantee monotonic improvement in all properties, the model enables efficient large-scale molecular dynamics, reaching approximately one frame per second on a single CPU core. These results demonstrate that local environment descriptors can encode key aspects of hydrogen-bond topology, providing a data-efficient route to structural relaxation in disordered molecular crystals.

en: Emergence of Bernal-Fowler ice rules in crystal structure prediction via data-efficient machine learning force fields: the case of Ice VII

active-learning

display_name: active-learning

crystal-structure-prediction

display_name: crystal-structure-prediction

high-pressure-ice

display_name: high-pressure-ice

ice-rules

display_name: ice-rules

ice-vii

display_name: ice-vii

machine-learning-force-fields

display_name: machine-learning-force-fields


en: Generative artificial-intelligence (AI) models, such as score-based diffusion models, have recently advanced the field of computational materials science by enabling the generation of new materials with desired properties. In addition, these models could also be leveraged to reconstruct crystal structures for which partial information is available. One relevant example is the reliable determination of atomic positions occupied by hydrogen atoms in hydrogen-containing crystalline materials. While crucial to the analysis and prediction of many materials properties, the identification of hydrogen positions via X-ray scattering experiments has been historically challenging, and often requires more expensive neutron scattering measurements. As a consequence, and despite experimental advances which enable us nowadays to accurately determine hydrogen positions based on X-ray scattering experiments, inorganic crystallographic databases still report many lattice structures where hydrogen atoms have been either omitted or inserted with heuristics or by chemical intuition. Here, we combine diffusion models from the field of materials science with techniques originally developed in computer vision for image inpainting. We present how this knowledge transfer across domains enables a much faster and more accurate completion of host structures, compared to unconditioned diffusion models or previous approaches solely based on density-functional theory (DFT). Overall, when applied to a test dataset of hydrogen-containing materials from the MC3D database, our approach exceeds a success rate of 97% in terms of finding a structural match or predicting a more stable configuration (according to DFT) than the initial reference from the experimental source database (and with a success rate exceeding 99% when excluding structures flagged as theoretical in MC3D), both when starting from structures that were already relaxed with DFT, or when starting directly from the experimentally determined host structures.

en: Score-based diffusion models for accurate crystal-structure inpainting and reconstruction of hydrogen positions

aiida

display_name: aiida

dft

display_name: dft

generative-ai

display_name: generative-ai

machine-learning

display_name: machine-learning

mlip

display_name: mlip


en: Spin Hall effect (SHE) in two-dimensional (2D) materials is promising to effectively manipulate spin angular momentum and identify topological properties. In this work, we implemented an automated Wannierization with spin-orbit coupling on 426 non-magnetic monolayers including 210 metal and 216 insulators. Intrinsic spin Hall conductivity (SHC) has been calculated to find candidates exhibiting novel properties. We discover that Y₂C₂I₂ has an unconventional SHE with canted spin due to low crystal symmetry, Ta₄Se₂ is a metallic monolayer with exceptionally high SHC, and the semi-metal Y₂Br₂ possesses efficient charge-to-spin conversion induced by anti-crossing in bands. Moreover, quantum spin Hall insulators are investigated for quantized SHC. The present work provides a high-quality Wannier Hamiltonian database of 2D materials, and paves the way for the integration of 2D materials into high-performance and low-power-consumption spintronic devices.

en: High-throughput calculations of spin Hall conductivity in non-magnetic 2D materials

spin-hall-effect

display_name: spin-hall-effect

wannier-functions

display_name: wannier-functions


en: Predicting quasiparticle energies in materials requires expensive numerical evaluations of the electron self-energy. This limits calculations to ordered systems with small unit cells. Here, using symbolic regression, we show that the GW self-energy can be accurately approximated with compact analytical functions of physically motivated Kohn-Sham descriptors. These expressions can be learned from a single GW calculation in the ordered phase and remain accurate under symmetry breaking induced by quantum and thermal fluctuations, elastic deformations, and amorphous disorder. This development enables routine GW calculations of complex materials with thousands of atoms at a computational cost comparable to semi-local density functional theory. We demonstrate the accuracy of this approach for covalent semiconductors, ionic insulators, and two-dimensional materials. These results establish symbolic regression as a viable route to predictive, interpretable, and transferable many-body electronic structure models.

en: Electron correlation in semiconductors and insulators via symbolic regression

electronic-structure

display_name: electronic-structure

equation-discovery

display_name: equation-discovery

gw

display_name: gw

many-body-perturbation-theory

display_name: many-body-perturbation-theory

symbolic-regression

display_name: symbolic-regression


en: IZ-DPscreen is a hybrid machine learning framework developed for the high-throughput screening of double perovskites. It employs a stacking regressor approach to accurately predict key material properties, including band gap, formation energy, and thermodynamic stability. By integrating multiple learning algorithms, the model enhances prediction reliability and robustness. The performance of IZ-DPscreen is validated through benchmarking against both theoretical (DFT) and available experimental data, demonstrating its capability for reliable materials screening. This approach enables the rapid identification of thermodynamically stable double perovskites with optimal band gaps, thereby accelerating the discovery of promising candidates for optoelectronic and energy applications.

en: A hybrid machine learning approach for high-throughput screening of thermodynamically stable double perovskites with optimal band gap

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).

band-gap

display_name: band-gap

double-perovskite

display_name: double-perovskite

high-throughput-screening

display_name: high-throughput-screening

thermodynamic-stability

display_name: thermodynamic-stability


en: Nanoscale twin boundaries (TBs) effectively restrict the free motion of dislocations by intersecting with other TBs to realize high strength. However, the dislocations parallel to the TBs can glide along the TBs, resulting in detwinning for low strength. Herein, we present a triple-interlocked-nanotwinned (TIT) strategy for overcoming these inherent deficiencies. An Mg-9Li alloy was used as a proof-of-concept. The treated alloy had a large volume fraction of TIT interfaces (65.7%). It not only had a high yield strength of 508.6 MPa (approximately eleven times higher than that of the TIT-free sample) but also offered unprecedented ageing resistance, with a reduction in hardness of 6% after 730 days. The martensite-like phase transformation process concurrently triggers multiple-variant twins to form TIT structures. These TIT nets can effectively prohibit dislocation motion, reduce strengthening anisotropy, and disperse localized stress during the deformation process. This strategy of phase transformation twinning open a gate to fabricate other stronger metallic or ceramic nanotwinned bulk materials.

en: Triple-interlocked-nanotwinned bulk magnesium alloys with exceptional strength and ageing resistance

materialscloud

en: Materials Cloud

envi

en: Environment

ageing-resistance

display_name: ageing-resistance

magnesium-alloys

display_name: magnesium-alloys

strength

display_name: strength

triple-interlocked-nanotwin

display_name: triple-interlocked-nanotwin


en: We introduce P3MaZe, a real-space particle–mesh electrostatic method that combines the standard short-range/long-range decomposition of Particle-Particle Particle-Mesh (P3M) electrostatics with the Mass-Zero constrained dynamics (MaZe) framework. In this formulation, the smooth long-range electrostatic potential is represented on a mesh as a zero-inertia auxiliary field, while the discretized Poisson equation is enforced as a holonomic constraint during molecular dynamics. By retaining the standard P3M decomposition, P3MaZe preserves the systematic accuracy controls associated with the real-space cutoff, the Ewald splitting, the mesh spacing, and the charge assignment procedure, while replacing the conventional multigrid Poisson solver by a constrained correction problem. The method is validated for molten NaCl and simple point-charge flexible water (SPC/Fw). Structural, translational, collective, and rotational dynamical observables are in quantitative agreement with those obtained with established electrostatic methods, including real-space P3M, and Ewald summation. The constrained formulation consistently requires fewer multigrid iterations than the corresponding real-space P3M solver while retaining the expected linear scaling with system size. These results establish P3MaZe as a promising new direction for scalable real-space electrostatics in large-scale molecular simulations.

en: P3MaZe: a Mass-Zero constrained-dynamics formulation of particle–mesh electrostatics

constrained-dynamics

display_name: constrained-dynamics

electrostatic-interactions

display_name: electrostatic-interactions

long-range-interactions

display_name: long-range-interactions

molecular-dynamics

display_name: molecular-dynamics


en: This dataset supports the theoretical investigation reported in the manuscript "Exceptionally high carrier mobility in hexagonal diamond". Based on first-principles analyses of the electronic structures, carrier mobilities, and electron-phonon coupling mechanisms in both cubic diamond and hexagonal diamond, this work demonstrates high carrier mobility in the heaxagonal phase and elucidates its physical origins. The dataset includes relevant data files for geometry optimisation, phonon dispersion, dynamical quadrupole tensors, G0W0 quasiparticle bands, and carrier mobility calculations.

carrier-mobility

display_name: carrier-mobility

electron-phonon-coupling

display_name: electron-phonon-coupling

hexagonal-diamond

display_name: hexagonal-diamond


en: The favourable thermodynamic properties of magnesium make it a promising negative electrode for rechargeable Mg-metal batteries. However, parasitic side reactions and uncontrolled deposition at the unstable electrode/electrolyte interface severely hinder reversible electrodeposition. Here we demonstrate that a high density of twin defects via cyclic-multidirectional deformation markedly increases the diffusion rate and simultaneously suppresses interfacial reactions. Comprehensive morphological characterisation combined with theoretical calculations reveals that the twinned-magnesium negative electrode exhibits significantly enhanced kinetics, leading to a reduced dimensionless electrochemical Damköhler number and indicating that magnesium deposition becomes less diffusion-limited.Furthermore, reorganisation energy analysis based on Marcus–Hush–Chidsey kinetics, in conjunction with the battery overpotential, indicates that the twin structure effectively mitigates parasitic reactions between the negative electrode and the electrolyte, thereby significantly reducing the passivation effect and enabling long-term reversible magnesium electrodeposition. Consequently, the designed twinned-magnesium electrodes in an asymmetric coin cell paradigm achieve a Coulombic efficiency of 99.53% over 2000 cycles at 30 mA cm-2 with a capacity of 2 mAh cm-2, competitive with reported magnesium metal negative electrodes. This simple strategy has been successfully extended to the zinc metal system as a proof of concept, demonstrating the universal potential of twin engineering for achieving reversible metal electrodeposition.

en: Twinned-magnesium metal negative electrodes enable reversible magnesium electrodeposition

interface-passivation

display_name: interface-passivation

magnesium-electrodeposition

display_name: magnesium-electrodeposition

magnesium-metal-negative-electrodes

display_name: magnesium-metal-negative-electrodes

twinning

display_name: twinning


en: The quality, consistency, and information content of training data is often what determines the practical value of machine-learning models for atomistic simulations. Yet, many widely used electronic-structure databases are assembled having materials screening as primary goal rather than robust force-field learning, are limited in their scope to a specific class of chemical compounds, and/or employ inconsistent DFT functionals and settings. Here we introduce MAD-1.5, a highly curated dataset designed explicitly for training broadly applicable atomistic models across the periodic table at high levels of theory. MAD-1.5 extends the MAD dataset with targeted enrichment strategies that improve the coverage of chemical space to 102 elements while keeping the total number of configurations compact. All structures are computed with a single, standardized all-electron DFT workflow using the r2SCAN meta-GGA functional and consistent convergence settings, ensuring uniformity across chemically heterogeneous systems. The dataset encompasses molecules, clusters, bulk crystals, surfaces, and low-dimensional structures, and its quality and consistency are further enhanced by outlier removal using uncertainty quantification. We demonstrate the high accuracy that can be achieved with the proposed dataset by training PET-MAD-1.5, a generally applicable r2SCAN interatomic potential that covers 102 elements in the periodic table and achieves exceptional levels of benchmark accuracy and stability in challenging simulation protocols.

en: High-quality, high-information datasets for universal atomistic machine learning

atomistic-simulations

display_name: atomistic-simulations

dataset

display_name: dataset

density-functional-theory

display_name: density-functional-theory

interatomic-potentials

display_name: interatomic-potentials

machine-learning

display_name: machine-learning

universal-models

display_name: universal-models


en: Dynamical control of the optical nonlinear properties in solids – with light itself – will be essential for the development of ultrafast photonic technologies, including high-speed terahertz-rate data communication and advanced signal processing. Previously, methods for optically modulating nonlinear optical processes, such as second harmonic generation (SHG), have relied predominantly on non-resonant light-matter interaction or photo-generation of hot electrons in nanoscale materials. However, these approaches are typically constrained by limited interaction lengths and the initial frequency conversion is relatively weak under equilibrium conditions, making them impractical for devices that require high-contrast switching of strong signals. Here, modulation of a strong SHG signal in a bulk β-BaB2O4 (BBO) nonlinear optical crystal is achieved through resonant transient lattice deformation induced by intense terahertz (THz) pulses tuned to a specific infrared-active phonon mode. Selective lattice excitation results in an unprecedented ~30% modulation of the SHG in BBO. In contrast to past efforts employing non-resonant THz interaction, this effect cannot be explained by modulation of the nonlinear susceptibility – a 𝜒(3)-effect. But rather – through polarization-resolved measurements, ab initio calculations, and analytical modelling –the SHG modulation is found to be driven by THz-induced phonon dynamics that affect the nonlinear optical phase-matching conditions through a 𝜒(2) process. This resonant all-optical control mechanism points toward new optical control schemes, based on selected lattice degrees of freedom, to dynamically tune the overall nonlinear optical response in bulk materials. Such schemes may find practical applications in ultrafast optoelectronics and integrated photonics.

en: All-optical control of second-harmonic generation in β-BaB2O4 via coherent, terahertz-driven acentric lattice displacement

all-optical-control-in-bulk-crystals

display_name: all-optical-control-in-bulk-crystals

nonlinear-optical-phase-matching

display_name: nonlinear-optical-phase-matching

nonlinear-susceptibility

display_name: nonlinear-susceptibility

resonant-lattice-dynamics

display_name: resonant-lattice-dynamics

second-harmonic-generation

display_name: second-harmonic-generation

terahertz-phonon-excitation

display_name: terahertz-phonon-excitation


en: The Shastry-Sutherland compound SrCu2(BO3)2 (SCBO) is a paradigmatic low-dimensional quantum spin system, featuring geometrically frustrated interacting spin dimers and a rich landscape of emergent quantum phenomena. While its magnetic properties have been extensively explored, the nature of its electronic excitations - particularly the Cu2+ d–d transitions - remains poorly understood. A detailed characterization of these excitations is essential to determine the crystal field splitting and the degree of Cu 3d–O 2p hybridization, both of which underpin superexchange interactions and magnetic behavior in SCBO. Here, the electronic properties of SCBO are investigated by combining spectroscopic techniques with theoretical approaches for first-principles band structure calculations.Experimentally, we employ L3-edge resonant inelastic x‑ray scattering (RIXS) to directly probe intra-d excitations, supplemented by broadband infrared reflectivity and ellipsometry measurements to resolve charge-transfer (CT) features. Theoretically, multireference quantum chemistry calculations accurately reproduce the energies (1.8–2.4 eV) and symmetries of the observed d–d excitations, while band-structure calculations based on density functional theory elucidate the CT excitation spectrum. These combined approaches define the energy scales for both d–d and CT transitions, validate the computational framework, and provide quantitative microscopic parameters essential for refining superexchange-based magnetic models in this prototypical frustrated quantum antiferromagnet.

en: Probing high-energy electronic excitations in the Shastry-Sutherland compound SrCu2(BO3)2 with resonant inelastic x-ray scattering and optical spectroscopy

d-d-excitations

display_name: d-d-excitations

dftu

display_name: dftu

resonant-inelastic-x-ray-scattering

display_name: resonant-inelastic-x-ray-scattering

shastry-sutherland-model

display_name: shastry-sutherland-model


en: We developed a machine learning interatomic potential (MLIP) for Ge-rich GeSbTe alloys of interest for applications in phase change memories embedded in microcontrollers. The MLIP was generated by fitting with a neural network method a large database of energies and forces computed within density functional theory of elemental, binary, stoichiometric and non-stoichiometric ternary alloys in the Ge-Sb-Te phase diagram. The MLIP is demonstrated to be highly transferable to large regions of the phase diagram around the compositions included in the dataset. The MLIP is then exploited to simulate the crystallization with phase separation of three Ge-rich alloys on the Ge-SbTe and Ge- GeSbTe tie-lines that correspond to the set process of the memory cell. The transformation on the ns time scale and at 600 K, comparable to the operation conditions of the memory, yields crystalline cubic GeTe slightly Sb-doped and amorphous GeSb and Ge. These metastable phases differ from the thermodynamically stable products and form due to kinetics effects on the short time span of the set operation in phase change memories.

en: On phase separation and crystallization of Ge-rich GeSbTe alloys from atomistic simulations with a machine learning interatomic potential

database

display_name: database

interatomic-potential

display_name: interatomic-potential


en: Contact variability remains a major obstacle for scaled two-dimensional transistors, but the extent to which contact geometry can control defect sensitivity remains unclear. This dataset supports a first-principles comparison of top and recessed Ti-MoS2 contacts under a common ordered sulfur-vacancy sequence. The deposited data include VASP POSCAR-format atomic structures generated from NanoDCAL device geometries, the corresponding NanoDCAL SCF input files used as structure sources, and processed metadata tables documenting vacancy coordinates, contact geometries, convergence status and Schottky-barrier-height extraction. These data support the manuscript conclusion that recessed Ti contacts strongly reduce vacancy-position-dependent carrier-injection-barrier variation in MoS2 relative to conventional top contacts.

en: Defect-tolerant carrier injection in MoS2 enabled by recessed Ti contacts

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).

recessed-contact

display_name: recessed-contact

schottky-barrier

display_name: schottky-barrier


en: We develop data-efficient machine learning interatomic potentials (MLIPs) for fast molecular dynamics simulations combining DeePMD and MACE models within an active learning and knowledge distillation framework. Using liquid water as a case study, we first independently train DeePMD and MACE models from scratch through active learning. We find that MACE requires around 3 times less training data than DeePMD, but its inference speed is 10 times lower. We also show that starting from a pretrained foundation model based on the MACE architecture further reduces the training data by a factor of 7, resulting in a fine-tuned foundation model with a 25 times data reduction compared to DeePMD. To overcome the limitation associated with the lower inference speed of MACE potentials, we next develop a knowledge distillation scheme to train a DeePMD potential from the fine-tuned foundation model through an inexpensive active learning workflow. The distilled model is generated with ~10 times less computer time than the DeePMD model trained from scratch, while showing the same fast inference speed.Comparison with ab initio calculations shows that all the models reach the same level of accuracy in reproducing structural, vibrational, and diffusive properties of liquid water. Our approach enables practical, data-efficient training of customized MLIPs with high speed and accuracy.

en: Data-efficient and fast machine learning molecular dynamics through integrated active learning and knowledge distillation

materialscloud

en: Materials Cloud

envi

en: Environment

active-learning

display_name: active-learning

deepmd

display_name: deepmd

knowledge-distillation

display_name: knowledge-distillation

mace

display_name: mace

machine-learning-interatomic-potential

display_name: machine-learning-interatomic-potential


en: We investigate the interplay between charge, magnetic, and structural degrees of freedom in the isostructural and isoelectronic 5d1 double-perovskites Ba2MgReO6 and Ba2NaOsO6. Using first-principles-based electronic structure calculations, we show that both materials exhibit a tendency toward spontaneous quadrupolar order in the cubic paramagnetic phase, which is slightly weaker in Ba2NaOsO6 than in Ba2MgReO6. Our analysis further reveals an intimate coupling between the local magnetic moments and charge quadrupoles, mediated by the strong spin-orbit interaction, that leads to the unusual canted configuration of magnetic moments observed in these systems. When structural degrees of freedom are included, the two materials exhibit pronounced differences. In Ba2MgReO6 the strong coupling to Jahn-Teller distortions stabilizes the antiferroic Qx2−y2 order, yielding excellent agreement with available experimental data. In contrast, the Jahn-Teller coupling is significantly weaker in Ba2NaOsO6 and appears insufficient to stabilize the antiferroic quadrupolar order. While this is consistent with the absence of any measurable long-range structural distortion above the magnetic transition temperature, it contrasts with experimental results indicating a strong canting of the magnetic moments. Our analysis thus successfully describes the mechanisms shaping the properties of the Re-compound while a full quantitative description of the magnetic ground state of Ba2NaOsO6 is still elusive.

en: Quadrupole formation and coupling to magnetic and structural degrees of freedom in the 5d1 double perovskites Ba2MgReO6 and Ba2NaOsO6

multipoles

display_name: multipoles

spin-orbit-coupling

display_name: spin-orbit-coupling


en: The dataset provides crystallographic information (CIF) files for 27 dynamically stable two-dimensional silicon–metal borophene monolayers (Manuscript ID: BP14856, submitted to Physical Review B), which are made available for reviewers to download and inspect. All structures were relaxed using density functional theory (DFT) with the projector augmented wave–generalized gradient approximation (PAW‑PBE) method, based on a periodic slab model with a vacuum gap of 30 Å. With these CIF files, the lattice parameters and chemical compositions can be conveniently cross‑checked against the present paper. The accompanying README file documents in detail all computational parameters, including the VASP code, the PAW‑PBE functional, a plane‑wave cutoff energy of 600 eV, and a k‑point grid spacing of 0.189 Å⁻¹.

en: Silicon-metal sublayer engineering of honeycomb borophene:   DFT-relaxed structures of 27 dynamically stable 2D X–Si–B monolayers

dft

display_name: dft

electron-phonon-coupling

display_name: electron-phonon-coupling


en: This project contains data and software used for the publication. Code is provided which was used to analyze the magnetic structure and spin excitations in the material Co3Sn2S2 using the software SpinW for Matlab. Diffuse magnetic scattering data and a small code to read it are also included.

en: Noncollinear kagome magnet driven by giant antisymmetric exchange

diffuse-magnetic-scattering

display_name: diffuse-magnetic-scattering

spinw

display_name: spinw


en: This archive contains the data supporting the manuscript of the same title. The calculations are for bulk antiferromagnetic (type-II) NiO in a four-atom rhombohedral cell, and comprise the Wannierization of the Kohn-Sham manifold, the linear-response calculation of the on-site (U) and inter-site (V) Hubbard parameters, DFT+U+V calculations, and the construction of the block-diagonalizing and hybridized projectors discussed in the manuscript.

en: DFT+U+V is equivalent to DFT+U with density-dependent hybridized projectors

electronic-structure

display_name: electronic-structure


en: Heterogeneous catalytic pathways for clean energy conversion involve thousands of elementary steps, but most models involve only a few dozen reactions. We combine extensive density functional theory calculations, machine learning for activation barrier prediction, and human intelligence-inspired elementary reaction enumeration and identification. This enables automated kinetic modeling of CO2 hydrogenation on copper, a key process to produce fuels and chemicals. We construct one of the largest datasets consisting of 152 elementary CO2 reduction reactions and experimentally determine CO2 conversion, finding that even large networks with 100+ reactions are insufficient. In contrast, our approach reveals 9389 elementary reactions, reducing human bias in the reaction pathway. We unravel 40-fold higher CO2 conversion rates, following experimental trends of methanol and CO production. We establish the crucial role of inter-species hydrogen transfer and hydrogenation by molecular H2, a surprising data-driven discovery validated post-facto. The proposed strategy to comprehensively model complex reaction mechanisms will significantly advance catalysis research.

en: Data-driven massive reaction networks reveal pathways underlying catalytic CO2 hydrogenation

automated-reaction-enumeration

display_name: automated-reaction-enumeration

machine-learning

display_name: machine-learning

microkinetic-modeling

display_name: microkinetic-modeling

themocatalysis

display_name: themocatalysis


en: Ceria-based inverse catalysts derive their functionality from Ce3+ oxygen vacancy chemistry, yet strategies to deliberately control vacancy formation and distribution at metal oxide interfaces remain limited. Here, we demonstrate that aliovalent samarium doping provides a direct and tunable route to vacancy engineering in the prototypical CeO2/Ru(0001) inverse catalyst. Using real-time, nanometer-resolved low-energy electron microscopy in combination with low-energy electron micro-diffraction, X-ray absorption spectroscopy in photoemission electron microscopy, and density functional theory, wedirectly correlate lattice structure, oxidation state, and vacancy formation under reducing conditions. Sm incorporation generates extrinsic oxygen vacancies, which are accompanied by Ce3+ formation, which induces a pronounced lattice expansion that in turn fosters the formation of additional intrinsic oxygen vacancies, substantially enhancing reducibility and stabilizing reduced ceria phases relative to undoped CeO2. The doped system exhibits accelerated reduction kinetics, ordered intrinsic vacancy formation, and modified phase-transition behavior. By establishing how aliovalent dopants govern the vacancy landscape and redox response in inverse ceria catalysts, this work provides atomic-level mechanistic insight and concrete design principles for engineering reducible oxides at metal oxide interfaces with tailored redox properties.

en: Samarium-driven vacancy engineering in CeO2/Ru(0001) inverse catalysts

ceria-inverse-catalysts

display_name: ceria-inverse-catalysts

dftu

display_name: dftu

lanthanide-doping

display_name: lanthanide-doping

leem

display_name: leem

oxygen-vacancies

display_name: oxygen-vacancies

redox-dynamics

display_name: redox-dynamics


en: We investigate the Uhlenbeck--Ford (UF) model as a reference system for free-energy calculations in two-dimensional (2D) fluids. The 2D virial coefficients are computed exactly up to tenth order and combined with molecular simulation data to construct highly accurate numerical representations of the equation of state and the excess Helmholtz free energy. We then determine the phase diagram of the model in order to establish the thermodynamic stability limits of the fluid phase and thereby identify the range of applicability of the UF model as a fluid reference system. In the course of this analysis, we identify the solid, hexatic, and fluid phases, and show that the fluid remains the only thermodynamically stable phase, independent of density, for scaling parameters up to p<=70. Finally, we demonstrate the practical applicability of the 2D UF model as a reference system through thermodynamic integration calculations of the free energy of a two-dimensional Lennard--Jones fluid.

en: The Uhlenbeck–Ford model in two dimensions: Reference system for fluid-phase free-energy calculations

materialscloud

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

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

free-energy

display_name: free-energy

lammps

display_name: lammps

molecular-dynamics

display_name: molecular-dynamics

statistical-mechanics

display_name: statistical-mechanics


en:
The requirement of generating predictions that exactly fulfill the fundamental symmetry of the corresponding physical quantities has profoundly shaped the development of machine-learning models for physical simulations.In many cases, models are built using constrained mathematical forms that ensure that symmetries are enforced exactly. However, unconstrained models that do not obey rotational symmetries are often found to have competitive performance, and to be able to \emph{learn} to a high level of accuracy an approximate equivariant behavior with a simple data augmentation strategy.In this paper, we introduce rigorous metrics to measure the symmetry content of the learned representations in such models, and assess the accuracy by which the outputs fulfill the equivariant condition.We apply these metrics to two unconstrained, transformer-based models operating on decorated point clouds (a graph neural network for atomistic simulations and a PointNet-style architecture for particle physics) to investigate how symmetry information is processed across architectural layers and is learned during training.Based on these insights, we establish a rigorous framework for diagnosing spectral failure modes in ML models. Enabled by this analysis, we demonstrate that one can achieve superior stability and accuracy by strategically injecting the minimum required inductive biases, preserving the high expressivity and scalability of unconstrained architectures while guaranteeing physical fidelity.

This record includes sofware and instructions on how to reproduce the main results of this manuscript.

en: How unconstrained machine-learning models learn physical symmetries

materialscloud

en: Materials Cloud

atomistic-simulations

display_name: atomistic-simulations

cscs

display_name: cscs

equivariance

display_name: equivariance

erc

display_name: erc

interatomic-potentials

display_name: interatomic-potentials

snsf

display_name: snsf


en:
The Density-Functional Toolkit (DFTK) is a recent plane-wave density-functional theory (DFT) code, written with a flexible architecture and in a high-level language to facilitate development of new methods and support fundamental mathematical research on DFT. DFTK has been instrumental in a good dozen of recent papers, and its core DFT routines are considered stable. So far a systematic precision benchmark against other codes and approaches had not yet been performed. In this work, we apply the verification protocol established by E. Bosoni et al. [Nat. Rev. Phys. 6, 45-58 (2024)] based on the AiiDA high-throughput engine, to validate the precision of DFTK against the existing all-electron and pseudopotential data. This record contains our results, as well as the scripts needed to reproduce them. Out of the 720 systems, DFTK achieves excellent agreement with the all-electron average (ε<0.06) on 223 systems and good-but-not-excellent agreement (0.06<=ε<0.2) on 371 systems. Comparing DFTK with ABINIT (ABINIT@PW|PseudoDojo-v0.5), the agreement is excellent on 697 systems and good-but-not-excellent on 14 systems; the agreement is bad (ε>=0.2) for Ne and I systems only, likely due to the corresponding pseudopotentials.

This agreement is made possible by a custom "v0.5.1-fix" of PseudoDojo:

The pseudopotential files are truncated at rcut=5.99 bohr.

The UPF files in PseudoDojo v0.5 for Bi, Pb, Po, Rn, Te, Tl were missing the third projector and had to be re-generated with a patched ONCVPSP.

The new pseudopotentials in PseudoDojo v0.5 do not have a recommended Ecut, so we used the recommended Ecut of PseudoDojo v0.4.1 + 20 Ha.

We acknowledge and thank Yihan Wu for the initial versions of aiida-dftk and the DFTK support in aiida-common-workflows.

en: Precision verification results data for DFTK@PW|PseudoDojo-v0.5.1-fix

envi

en: Environment

automation

display_name: automation

dft

display_name: dft

equation-of-state

display_name: equation-of-state

pseudopotentials

display_name: pseudopotentials

verification

display_name: verification


en: Chromium oxide (Cr2O3) is widely considered a p-type transparent conducting oxide despite ongoing debate regarding its intrinsic transport character. Here, we resolve this question by computing phonon-limited electron and hole mobilities using the Boltzmann transport equation. We find that electron mobility systematically exceeds hole mobility over a wide temperature range, demonstrating that Cr2O3 is intrinsically n-type. Analysis of scattering mechanisms reveals that scattering with phonons affect electrons and holes similarly, and that the mobility asymmetry originates from the electronic structure, namely the larger effective mass and multi-valley character of the valence band. These results indicate that the commonly observed p-type behavior is likely extrinsic. The intrinsic n-type character, combined with moderate hole mobility, enables ambipolar transport and revises the role of Cr2O3 in transparent electronics. Additionally, the alignment of electrical and magnetic easy axes reveals coupling between charge transport and antiferromagnetic order, opening opportunities for magnetoelectric and spintronic devices

en: Challenging the p-type paradigm: intrinsic n-type mobility in antiferromagnetic Cr2O3

carrier-mobility

display_name: carrier-mobility

magnetism

display_name: magnetism

transparent-conducting-oxides

display_name: transparent-conducting-oxides


en: We perform a first-principles high-throughput screening of the mechanical properties of phonon-mediated superconductors derived from the recent experimentally synthesized superconducting materials database PRX Energy \textbf{4}, 033012 (2025). We develop the aiida-mechanical workflow which combines first-principles calculations of elastic constants and generalized stacking fault energies, to assess the ductility of superconducting candidates. Starting from 250 materials identified with promising superconducting critical temperatures, we computed their elastic tensors to evaluate the bulk modulus, shear modulus, Pugh's ratio, and Pettifor's ratio. To further characterize their plastic deformation behavior, we calculated the stacking fault energy and surface energy for selected materials and slip directions, allowing the estimation of Rice's ratio and related ductility indicators. We find that several new materials simultaneously exhibit high Tc and ductility, including HfPd2Al, TiRuSb, and ZrNi2Ga. This dataset provides the complete computational provenance. Specifically, it contains the fully relaxed crystal structures, Quantum ESPRESSO calculations for all elastic tensor (finite-displacement and Born approximation methodologies) and stacking fault energy workflows, as well as k-point and supercell convergence tests. This work offers a quantitative mapping of mechanical performance across a wide range of superconductors and provides a valuable reference for finding new practical superconducting materials.

en: In search of novel ductile superconductors

ductility

display_name: ductility

elastic-tensor

display_name: elastic-tensor

generalized-stacking-fault-energy

display_name: generalized-stacking-fault-energy

pettifors-ratio

display_name: pettifors-ratio

pughs-ratio

display_name: pughs-ratio

rices-ratio

display_name: rices-ratio

superconductor

display_name: superconductor

surface-energy

display_name: surface-energy


en:
Semilocal density functionals such as the Perdew-Burke-Ernzerhof (PBE) functional notoriously underestimate experimental band gaps. Hybrid functionals address this “band gap problem” by admixing a fraction of Fock exchange to semilocal exchange. The optimal mixing parameter depends on the specific material and can be identified as the inverse dielectric constant (dielectric-dependent hybrid functional, DDH). Here, we show that dielectric constants obtained using the r²SCAN meta-GGA functional are significantly more accurate than dielectric constants obtained using the semilocal PBE functional. We propose the DD-r²SCANH functional, a dielectric-dependent hybrid functional based on r²SCAN. DD-r²SCANH can outperform the standard PBE-based DDH in terms of band gaps and other electronic structure properties. Particularly impressive improvements are obtained for narrow-gap semiconductors such as Ge and InAs, where PBE wrongly predicts a metallic phase, but r²SCAN opens a band gap.

Here, we provide VASP input files for DD-r²SCANH, and further we provide the crystal structures used for our band gap benchmark in POSCAR format.

en: Dielectric-dependent hybrid functional from meta-GGA

density-functional-theory

display_name: density-functional-theory

electronic-bands

display_name: electronic-bands

electronic-structure

display_name: electronic-structure


en: The implementation of ultrasoft pseudopotentials into time-dependent density-functional perturbation theory is detailed for both the Sternheimer approach and the Liouville-Lanczos (LL) method, and equations are presented in the scalar relativistic approximation for periodic solids with finite momentum transfer q. The LL method is applied to calculations of the electron energy loss (EEL) spectrum of face-centered cubic bulk Au both at vanishing and finite q. Our study reveals the richness of the physics underlying the various contributions to the density fluctuation in gold. In particular, our calculations suggest the existence in gold of two quasiseparate 5d and 6s electron gasses, each one oscillating with its own frequency at, respectively, 5.1 eV and 10.2 eV. We find that the contribution near 2.2 eV comes from 5d to 6s interband transitions modified by the intraband contribution to the real part of the dielectric function, which we call a mixed excitation.

en: Electron energy loss spectroscopy of bulk gold with ultrasoft pseudopotentials and the Liouville-Lanczos method

dft

display_name: dft

eels

display_name: eels

eu-max

display_name: eu-max

genci

display_name: genci

gold

display_name: gold

interband-transitions

display_name: interband-transitions

intraband-transitions

display_name: intraband-transitions

liouville-lanczos-method

display_name: liouville-lanczos-method

loss-function

display_name: loss-function

marvelosp

display_name: marvelosp

mixed-excitations

display_name: mixed-excitations

plasmons

display_name: plasmons

quantum-espresso

display_name: quantum-espresso

sternheimer-equations

display_name: sternheimer-equations

tddft

display_name: tddft


en: This repository contains the scripts that were used to run the calculations that present a new biasing technique, the Adaptive Topography of Landscape for Accelerated Sampling (ATLAS). The techinque is implemented in plumed-2.0 and the input file are included in the repository, as well as a few scripts to postprocess the calculations and reproduce the plots presented in the paper

en: Global free-energy landscapes as a smoothly joined collection of local maps

enhanced-sampling

display_name: enhanced-sampling

epfl

display_name: epfl

erc

display_name: erc

machine-learning

display_name: machine-learning

molecular-dynamics

display_name: molecular-dynamics

snsf

display_name: snsf


en: We develop a scheme for finite-size corrections of vertical transition energies and single-particle energy levels involving defect states with built-in ionic polarization in supercell calculations.The method accounts on an equal footing for the screening of the electrons and of the ionic polarization charge arising from the lattice distortions. We demonstrate the accuracy of our corrections for various defects in MgO and in water by comparing with the dilute limit achieved through the scaling of the system size. The general validity of our formulation is also confirmed through a sum rule that connects vertical transition energies with formation energies of structurally relaxed defects.

en: Finite-size corrections of defect energy levels involving ionic polarization

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

density-functional-calculations

display_name: density-functional-calculations

dielectric-properties

display_name: dielectric-properties

electric-polarization

display_name: electric-polarization

first-principles-calculations

display_name: first-principles-calculations

optical-absorption-spectroscopy

display_name: optical-absorption-spectroscopy

polarons

display_name: polarons

vacancies

display_name: vacancies


en: A central goal of condensed matter physics is to understand the rich and diverse electronic and optical properties that emerge as wavelike electrons move through the periodically-arranged atoms in crystalline materials. However, more than 90 years after Bloch derived the functional forms of electronic waves in crystals (now known as Bloch wavefunctions) rapid scattering processes have so far prevented their direct experimental reconstruction. In high-order sideband generation (HSG), electrons and holes generated in semiconductors by a near-infrared (NIR) laser are accelerated to high kinetic energy by a strong terahertz field, and recollide to emit NIR sidebands before they are scattered. Here we reconstruct the Bloch wavefunctions of two types of holes in gallium arsenide wavelengths much longer than the spacing between atoms by experimentally measuring sideband polarizations and introducing an elegant theory that ties those polarizations to quantum interference between different recollision pathways. These Bloch wavefunctions are compactly visualized on the surface of a sphere. Because HSG can, in principle, be observed from any direct-gap semiconductor or insulator, we expect the method introduced in this Article can be used to reconstruct low-energy Bloch wavefunctions in a large class of bulk and nanostructured crystalline materials, and thus enable important new insights into the origin and engineering of electronic and optical properties of condensed matter. This record contains the data and analysis scripts used for these experiments.

en: Reconstruction of Bloch wavefunctions of holes in a semiconductor

materialscloud

en: Materials Cloud

bloch-wavefunctions

display_name: bloch-wavefunctions

condensed-matter-physics

display_name: condensed-matter-physics

experimental

display_name: experimental

gallium-arsenide

display_name: gallium-arsenide

luttinger-hamiltonian

display_name: luttinger-hamiltonian

non-equilibrium-physics

display_name: non-equilibrium-physics

nonlinear-optics

display_name: nonlinear-optics

terahertz

display_name: terahertz


en: Rechargeable lithium-ion batteries dominate the consumer electronics and electric vehicle markets. However, concerns on Li availability have prompted the development of alternative high energy density electrochemical energy storage systems. Rechargeable batteries based on a Ca metal anode can exhibit advantages in terms of energy density, safety and cost. The development of rechargeable Ca metal batteries requires the identification of suitable high specific energy cathode materials. This work focuses on Ca-bearing minerals because they represent stable and abundant compounds. Suitable minerals should contain a transition metal able of being reversibly reduced and oxidized, which points to several major classes of silicates and carbonates: olivine (CaFeSiO4; kirschsteinite), pyroxene (CaFe/MnSi2O6; hedenbergite and johannsenite, respectively), garnet (Ca3Fe/Cr2Si3O12; andradite and uvarovite, respectively), amphibole (Ca2Fe5Si8O22(OH)2; ferroactinolite) and double carbonates (CaMn(CO3)2; kutnahorite and CaFe(CO3)2; ankerite). This work discusses their electrode characteristics based on crystal chemistry analysis and density functional theory (DFT) calculations. The results indicate that upon Ca deintercalation, compounds such as pyroxene, garnet and double carbonate minerals could display high theoretical energy densities (ranging from 780 to 1500 Wh/kg) with moderate structural modifications. As a downside, DFT calculations indicate a hampered Ca mobility in their crystal structures. The overall analysis then disregards olivine, garnet, pyroxene, amphibole and double carbonates as structural types for future Ca-cathode materials design.

en: Analysis of minerals as electrode materials for Ca-based rechargeable batteries

envi

en: Environment

batteries

display_name: batteries

electrode-materials

display_name: electrode-materials

geochemistry

display_name: geochemistry

minerals

display_name: minerals


en: We proposed an efficient high-throughput scheme for the discovery of new stable crystalline phases. Our approach was based on the transmutation of known compounds, through the substitution of atoms in the crystal structure with chemically similar ones. The concept of similarity is defined quantitatively using a measure of chemical replaceability, extracted by data mining experimental databases. In this way we build more than 250k possible crystal phases, with almost 20k that are on the convex hull of stability. This dataset contains the optimized structure and the energy of these 250k materials calculated with the PBE approximation, in a format that is convenient for data-mining or for machine-learning applications.

en: Finding new crystalline compounds using chemical similarity

density-functional-theory

display_name: density-functional-theory

high-throughput

display_name: high-throughput

machine-learning

display_name: machine-learning


en: We extend the quasiparticle self-consistent approach beyond the GW approximation by using a range-separated vertex function. The developed approach yields band gaps, dielectric constants, and band positions with an accuracy similar to highest-level electronic-structure calculations without exceeding the cost of regular quasiparticle self-consistent GW. We introduce an exchange-correlation kernel that accounts for the vertex over the full spatial range. In the long range it complies with the Ward identity, while it is approximated through the adiabatic local density functional in the short range. In this approach, the renormalization factor is balanced and the higher-order diagrams are effectively taken into account.

en: Vertex function compliant with the Ward identity for quasiparticle self-consistent calculations beyond GW

electronic-bands

display_name: electronic-bands

electronic-structure

display_name: electronic-structure

gw

display_name: gw

ionization-potential

display_name: ionization-potential

many-body-perturbation-theory

display_name: many-body-perturbation-theory

vertex

display_name: vertex


en: The magnetic and electronic nature of the γ-Al2O3/SrTiO3 spinel/perovskite interface is explored by means of x-ray absorption spectroscopy. Polarized x-ray techniques combined with atomic multiplet calculations reveal localized magnetic moments assigned to Ti3+ at the interface with equivalent size for in- and out-of-plane magnetic field directions. Although magnetic fingerprints are revealed, the Ti3+ magnetism can be explained by a paramagnetic response at low temperature under applied magnetic fields. Modeling the x-ray linear dichroism results in a Delta0 ∼ 1.9 eV splitting between the t2g and eg states for the Ti4+ 3d0 orbitals. In addition these results indicate that the lowest energy states have the out-of-plane dxz/dyz symmetry. The isotropic magnetic moment behavior and the lowest energy dxz/dyz states are in contrast to the observations for the two-dimensional electron gas at the perovskite/perovskite interface of LaAlO3/SrTiO3 that exhibits an anisotropic magnetic dxy ground state.

en: Magnetic and electronic properties at the γ-Al2O3/SrTiO3 interface

experimental

display_name: experimental

magnetism

display_name: magnetism

two-dimensional-electron-gas

display_name: two-dimensional-electron-gas

x-ray-absorption-spectroscopy

display_name: x-ray-absorption-spectroscopy


en:
Set of inputs to perform the calculations reported in the paper.
The i-pi input enables to perform molecular dynamics / metadynamics / REMD / PIMD simulations, with adequate thermostats.
The DFTB and LAMMPS input respectively enable to calculate force and energies within the DFTB and Neural Network Forcefield frameworks.
The CP2K input files enable to calculate force and energies at PBE and PBE0 level. The latter is used as the reference to train the neural network correction on top of DFTB.

Brief description of the work: We present a generally-applicable computational framework for the efficient and accurate characterization of molecular structural patterns and acid properties in explicit solvent using H₂O₂ and CH₃SO₃H in phenol as an example. In order to address the challenges posed by the complexity of the problem, we resort to a set of data-driven methods and enhanced sampling algorithms. The synergistic application of these techniques makes the first-principle estimation of the chemical properties feasible without renouncing to the use of explicit solvation, involving extensive statistical sampling. Ensembles of neural network potentials are trained on a set of configurations carefully selected out of preliminary simulations performed at a low-cost density-functional tight-binding (DFTB) level. Energy and forces of these configurations are then recomputed at the hybrid density functional theory (DFT) level and used to train the neural networks. The stability of the NN model is enhanced by using DFTB energetics as a baseline, but the efficiency of the direct NN (i.e., baseline-free) is exploited via a multiple-time step integrator. The neural network potentials are combined with enhanced sampling techniques, such as replica exchange and metadynamics, and used to characterize the relevant protonated species and dominant non-covalent interactions in the mixture, also considering nuclear quantum effects.

en: Simulating solvation and acidity in complex mixtures with first-principles accuracy: the case of CH₃SO₃H and H₂O₂ in phenol

acid

display_name: acid

acid-homogeneous-catalysis

display_name: acid-homogeneous-catalysis

artificial-intelligence

display_name: artificial-intelligence

catalysis

display_name: catalysis

marvel

display_name: marvel

reaction

display_name: reaction

solution-chemistry

display_name: solution-chemistry


en: Two-dimensional (2D) layered materials offer a materials platform with potential applications from energy to information processing devices. Although some single- and few-layer forms of materials such as graphene and transition metal dichalcogenides have been realized and thoroughly studied, the space of arbitrarily layered assemblies is still mostly unexplored. The main goal of this work is to demonstrate precise control of layered materials' electronic properties through careful choice of the constituent layers, their stacking, and relative orientation. Physics-based and AI-driven approaches for the automated planning, execution, and analysis of electronic structure calculations are applied to layered assemblies based on prototype one-dimensional (1D) materials and realistic 2D materials. We find it is possible to routinely generate moiré band structures in 1D with desired electronic characteristics such as a band gap of any value within a large range, even with few layers and materials (here, four and six, respectively). We argue that this tunability extends to 2D materials by showing the essential physical ingredients are already evident in calculations of two-layer MoS2 and multi-layer graphene moiré assemblies.

en: Computational design of moiré assemblies aided by artificial intelligence

artificial-neural-networks

display_name: artificial-neural-networks

high-throughput-calculations

display_name: high-throughput-calculations

two-dimensional-materials

display_name: two-dimensional-materials


en:
The production of enantiopure materials and molecules is of uttermost relevance in research and industry in numerous contexts, ranging from non-linear optics to asymmetric synthesis. In the context of the latter, we have investigated dehalogenation, which is an essential reaction step for a broad class of chemical reactions. Specifically, dehalogenation of prochiral 5-bromo-7-methylbenz(a)anthracene (BMA) on prototypical, chiral, intermetallic PdGa{111} surfaces under ultrahigh vacuum conditions. Enantioselective halogen elimination is demonstrated by combining temperature-programmed x-ray photoelectron spectroscopy, scanning probe microscopy, and density functional theory. On the PdGa{111} surfaces, the difference in debromination temperatures for the two BMA surface enantiomers amounts up to unprecedented 46 K. The significant dependence of the dehalogenation temperature of the BMA surface enantiomers on the atomic termination of the PdGa{111} surfaces, implies that the ensemble effect is pronounced in this reaction step. These findings evidence enantiospecific control and hence promote intrinsically chiral crystals for asymmetric on-surface synthesis.
The record contains input files to reproduce the calculations discussed in the manuscript and the raw data of the experimental images discussed.

en: Asymmetric elimination reaction on chiral metal surfaces

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).

ab-initio

display_name: ab-initio

enantioselectivity

display_name: enantioselectivity

scanning-probe-microscopy

display_name: scanning-probe-microscopy

snsf

display_name: snsf


en: Cif files of relaxed "derived" copper surfaces from Cu2O, Cu2S, Cu3N, and Cu3P using EMT and published in J Gauthier, JH Stenlid, F Abild-Pedersen, M Head-Gordon, AT Bell, ACS Energy Letters, "The role of roughening to enhance selectivity to C2+ products during CO2 electroreduction on copper" (2021). These structures were used to evaluate the effect of roughening on catalytic selectivity of copper in electrochemcial CO2 reduction into valuable products such as fuels and commodity chemicals.

en: Atomic structures of 100nm x 100nm large oxide-, nitride-, sulfide-, and phosphide-derived copper surfaces

materialscloud

en: Materials Cloud

atomic-coordinates

display_name: atomic-coordinates

copper

display_name: copper

surface-structure

display_name: surface-structure


en: The record contains data that support our recent findings in the fabrication of cumulene containing polymers. Cumulene compounds are notoriously difficult to prepare and study because their reactivity increases dramatically with the increasing number of consecutive double bonds. In this respect, the emerging field of on‐surface synthesis provides exceptional opportunities because it relies on reactions on clean metal substrates under well‐controlled ultrahigh‐vacuum conditions. In the work we report the on‐surface synthesis of a polymer linked by cumulene‐like bonds on a Au(111) surface via sequential thermally activated dehalogenative C−C coupling of a tribenzoazulene precursor equipped with two dibromomethylene groups. The structure and electronic properties of the resulting polymer with cumulene‐like pentagon–pentagon and heptagon–heptagon connections have been investigated by means of scanning probe microscopy and spectroscopy methods and X‐ray photoelectron spectroscopy, complemented by density functional theory calculations.

en: On‐surface synthesis of cumulene‐containing polymers via two‐step dehalogenative homocoupling of dibromomethylene-functionalized tribenzoazulene

envi

en: Environment

cscs

display_name: cscs

cumulene

display_name: cumulene

dfg

display_name: dfg

erc

display_name: erc

on-surface-synthesis

display_name: on-surface-synthesis

polymers

display_name: polymers


en: We study the nature of excess electrons in CsPbBr3 and identify several single and double polaronic states. We emphasize the importance of proper inclusion of the self-interaction corrections for the stability of small electron polarons in this material. We demonstrate that spin–orbit coupling (SOC) has a significant impact on the energetics of the polaronic states. In particular, we find that SOC disfavors electron localization and leads to different polaronic geometries. Additionally, by carrying out thermodynamic integration, we show that small electron polarons are thermally stabilized in CsPbBr3. The small energy differences between the localized and delocalized electronic states could possibly reconcile the apparently conflicting properties of high charge-carrier mobilities and low recombinations rates.

en: Small electron polarons in CsPbBr3

halide-perovskites

display_name: halide-perovskites

polarons

display_name: polarons


en: In this record we provide data to support our recent findings on the synthesis of porphyrin-capped graphene nanoribbons. On-surface synthesis has emerged as a powerful tool for the construction of large, planar, π-conjugated structures that are not accessible through standard solution chemistry. Among such solid-supported architectures, graphene nanoribbons (GNRs) hold a prime position for their implementation in nanoelectronics due to their manifold outstanding properties. Moreover, using appropriately designed molecular precursors, this approach allows the synthesis of functionalized GNRs, leading to nanostructured hybrids with superior physicochemical properties. Among the potential "partners" for GNRs, porphyrins (Pors) outstand due to their rich chemistry, robustness, and electronic richness, among others. However, the use of such π-conjugated macrocycles for the construction of GNR hybrids is challenging and examples are scarce. In a recent publication we report singly and doubly Por-capped GNR segments presenting a commensurate and triply-fused GNR–Por heterojunction. The study of the electronic properties of such hybrid structures by high-resolution scanning tunneling microscopy, scanning tunneling spectroscopy, and DFT calculations reveals a weak hybridization of the electronic states of the GNR segment and the Por moieties despite their high degree of conjugation.

en: On-surface synthesis of singly and doubly porphyrin-capped graphene nanoribbon segments

dft

display_name: dft

graphene-nanoribbons

display_name: graphene-nanoribbons

porphyrin

display_name: porphyrin

snf

display_name: snf


en: In this record we provide data supporting our recent results discussed in the fabrication of non-benzenoid nanographenes. Nanographenes (NGs) have gained increasing attention due to their immense potential as tailor-made organic materials for nanoelectronics and spintronics. They exhibit a rich spectrum of physicochemical properties that can be tuned by controlling the size or the edge structure or by introducing structural defects in the honeycomb lattice. In the published manuscript we report the design and on-surface synthesis of NGs containing several odd-membered polycycles induced by a thermal procedure on Au(111). Our scanning tunneling microscopy, noncontact atomic force microscopy, and scanning tunneling spectroscopy measurements, complemented by computational investigations, describe the formation of two nonbenzenoid NGs (2A,B) containing four embedded azulene units in the polycyclic framework, via on-surface oxidative ring-closure reactions. Interestingly, we observe surface-catalyzed skeletal ring rearrangement reactions in the NGs, which lead to the formation of additional heptagonal rings as well as pentalene and as-indacene units in 2A,B, respectively. 2A,B on Au(111) both exhibit narrow experimental frontier electronic gaps of 0.96 and 0.85 eV, respectively, and Fermi level pinning of their HOMOs together with considerable electron transfer to the substrate. Ab initio calculations estimate moderate open-shell biradical characters for the NGs in the gas phase.

en: On-surface synthesis of non-benzenoid nanographenes by oxidative ring-closure and ring-rearrangement reactions

ab-initio-simulations

display_name: ab-initio-simulations

dfg-nsfc

display_name: dfg-nsfc

nanographenes

display_name: nanographenes

open-shell

display_name: open-shell


en:
We present the JuCLS (Jülich core-level shifts) database which collects first principles calculations of core-level binding energies and core-level shifts (also known as chemical shifts). The calculations for this database were performed with the FLEUR program [1], a feature-full, freely available, open source FLAPW (full-potential linearized augmented planewave) code, based on density-functional theory. The FLAPW-method is a very accurate all-electron method which within density functional theory is universally applicable to all atoms of the periodic table. All calculations are run with AiiDA through workflows within the AiiDA-FLEUR package (version 0.12.3) [2]. Our database collects predicted core-level shifts, binding energies for X-ray photoelectron spectroscopy (XPS) and as a side product formation energies. Core-level shifts are calculated within the initial state approximation and binding energies are extracted from core-hole simulations. The JuCLS v1.0 contains initial state core-level shifts and formation energies on 4435 of the 5058 stable binary metals from the Materials project database (MP) and calculations on 1271 elemental crystals from the Inorganic Crystal Structure Database (ICSD). This corresponds currently to over 130 000 unique core-level shifts and over 15 000 unique main line core-level shifts. The JuCLS database allows for the construction of theoretical X-ray photoelectron spectra containing a possible mixture of these materials. Furthermore, it allows for a direct chemical characterization of materials.

[1] https://flapw.de and www.judft.de
[2] https://github.com/JuDFTteam/aiida-fleur

en: JuCLS database of core-level shifts from all-electron density functional theory simulations for chemical analysis of X-ray photoelectron spectra

ab-initio

display_name: ab-initio

aiida-fleur

display_name: aiida-fleur

binding-energies

display_name: binding-energies

chemical-shifts

display_name: chemical-shifts

core-level

display_name: core-level

core-level-shifts

display_name: core-level-shifts

density-functional-theory

display_name: density-functional-theory

esca

display_name: esca

flapw

display_name: flapw

fleur

display_name: fleur

jucls

display_name: jucls

max

display_name: max

x-ray-photoelectron-spectroscopy

display_name: x-ray-photoelectron-spectroscopy

xps

display_name: xps


en: The article presents a series of methods that boost the speed of Wannier interpolation by several orders of magnitude, as well as their implementation in the WannierBerri code. The present dataset contains input files, scripts, and the resulting data, which allow to reproduce the examples and figures published in the article. The current version of the code is also included.

en: High performance Wannier interpolation of Berry curvature and related quantities with WannierBerri code

anomalous-hall-effect

display_name: anomalous-hall-effect

berry-curvature

display_name: berry-curvature

erc

display_name: erc

snsf

display_name: snsf

wannier-functions

display_name: wannier-functions


en: In the present record we provide the data obtained in ARPES experiments and input/output files of Quantum ESPRESSO calculations used in the publication entitled as this record. The experimental data consist of the Fermi surface at kz=π/c, the experimental band dispersion along the MΓM, XΓX and XMX, spin-resolved ARPES spectra, and spin-resolved ARPES spectra of surface states. The theoretical data consist in the bulk and slab calculations to support the experimental data.

en: Hidden bulk and surface effects in the spin polarization of the nodal-line semimetal ZrSiTe

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).

arpes

display_name: arpes

epfl

display_name: epfl

hidden-polarization

display_name: hidden-polarization

marvel

display_name: marvel

spin-texture

display_name: spin-texture


exploding-and-weeping-ceramics

en: The systematic tuning of the lattice parameters to achieve improved kinematic compatibility between phases is a broadly effective strategy for improving the reversibility, and lowering the hysteresis, of solid-solid phase transformations. Here, "kinematic compatibility" refers to the fitting together of the phases. We present an apparently paradoxical example in which tuning to near perfect compatibility in (Zr/Hf)O2-(YNb)O4 results in a high degree of irreversibility, as manifested in explosive or "weeping" behavior on cooling through the tetragonal-to-monoclinic phase transformation. In the case of weeping the polycrystal slowly and steadily falls apart at the grain boundaries. These effects occur without chemical change. Finally, tuning to satisfy a condition we term the equidistance condition results in reversible behavior with the lowest hysteresis in this system. We give evidence that all these observations are explained by a more careful analysis of compatibility of the polycrystal, accounting for sample shape. These results show that an extreme diversity of behaviors, from reversible to explosive, is possible in a chemically homogeneous system by manipulating conditions of compatibility in unexpected ways. They provide critical concepts underlying the current search for a shape memory oxide ceramic.

en: Exploding and weeping ceramics

materialscloud

en: Materials Cloud

title_for_slug: exploding-and-weeping-ceramics

ceramics

display_name: ceramics

cystallographic-compatibility

display_name: cystallographic-compatibility

shape-memory

display_name: shape-memory


en:
Spintronics applications for high-density non-volatile memories require simultaneous optimization of the perpendicular magnetic anisotropy (PMA) and current-induced magnetization switching. These properties determine, respectively, the thermal stability of a ferromagnetic memory cell and a low operation power consumption, which are mutually incompatible with the spin transfer torque as the driving force for the switching. Here, we demonstrate a strategy of alloy engineering to overcome this obstacle by using electrically induced orbital currents instead of spin currents. A non-equilibrium orbital density generated in paramagnetic γ-FeMn flows into CoPt coupled to the magnetization through spin-orbit interaction, ultimately creating an orbital torque. Controlling the atomic arrangement of Pt and Co by structural phase transition, we show that the propagation length of the transferred angular momentum can be modified concurrently with the PMA strength. We find a strong correlation to the phase transition-induced changes of d orbitals with mₗ = ±1 and mₗ = ±2 character. The close link of orbital hybridization to the dynamic orbital response and magnetic properties offers new possibilities to realize optimally designed orbitronics memory and logic applications.
This dataset contains the DFT calculations for the electronic structure of CoPt in L1₁ and A1 structures that are discussed the corresponding publication.

en: DFT calculations of the electronic structure of CoPt in L1₁ and A1 structures

envi

en: Environment

density-functional-theory

display_name: density-functional-theory

orbital-torque

display_name: orbital-torque

orbitronics

display_name: orbitronics


en: The chemical versatility of carbon imparts manifold properties to organic compounds, where magnetism remains one of the most desirable but elusive. Polycyclic aromatic hydrocarbons, also referred to as nanographenes, show a critical dependence of electronic structure on the topologies of the edges and the π-electron network, which makes them model systems with which to engineer unconventional properties including magnetism. In 1972, Erich Clar envisioned a bow-tie-shaped nanographene, C38H18, where topological frustration in the π-electron network renders it impossible to assign a classical Kekulé structure without leaving unpaired electrons, driving the system into a magnetically non-trivial ground state. In this record we include data needed to support our recent work where we demonstrate the experimental realization and in-depth characterization of this emblematic nanographene, known as Clar's goblet. Scanning tunnelling microscopy and spin excitation spectroscopy of individual molecules on a gold surface reveal a robust antiferromagnetic order with an exchange-coupling strength of 23 meV, exceeding the Landauer limit of minimum energy dissipation at room temperature. Through atomic manipulation, we realize switching of magnetic ground states in molecules with quenched spins. Our results provide direct evidence of carbon magnetism in a hitherto unrealized class of nanographenes, and prove a long-predicted paradigm where topological frustration entails unconventional magnetism, with implications for room-temperature carbon-based spintronics.

en: Topological frustration induces unconventional magnetism in a nanographene

ab-initio

display_name: ab-initio

clar-goblet

display_name: clar-goblet

kondo

display_name: kondo

nanographene

display_name: nanographene


en: The vast chemical space of metal and ligand combinations in Transition Metal Complexes (TMCs) gives rise to a rich variety of electronic excited states with local and non-local character such as intra-ligand (IL), metal-centered (MC), metal-to-ligand (MLCT) or ligand-to-metal charge-transfer (LMCT) states. Those features are equally found in Metal Organic Frameworks (MOFs), defined as modular materials built from metal-nodes connected through organic-ligands. Because of the electronic and structural complexity of MOFs, the computational description of their excited states is a formidable challenge for which two different approaches have been usually followed: the solid state and the molecular perspective. The first consists in analysing the frontier electronic bands and crystal orbitals of the electronic ground state (GS) in periodic boundary conditions, while the latter points to an accurate computation of the excited states in representative clusters at the molecular level. Herein, we apply both approaches to evaluate the optical absorption properties of three experimentally reported Ti(IV) mononuclear MOFs with in silico metal substitutions with Zn(II), Cd(II), Fe(II), Ru(II) and Zr(IV) ions, thus covering d10, d6 and d0 electronic configurations of 1st and 2nd row TMCs in MOFs. Our analysis captures the main electronic features attributed to these systems while we discuss the main advantages and drawbacks of both approximations.

en: Optical absorption properties of metal-organic frameworks: solid state versus molecular perspective

mof

display_name: mof

optical-absorption

display_name: optical-absorption


en:
One proposed mechanism of implant fouling is attributed to the nonspecific adsorption of non-collagenous bone matrix proteins (NCPs) onto a newly implanted interface. With the goal of capturing the fundamental mechanistic and thermodynamic forces that govern changes in these NCP recognition domains as a function of γ-carboxyglutamic acid (Gla) post-translational modification and surface chemistry, we probe the adsorption process of the most commonly occurring NCP, osteocalcin, onto a mineral and metal oxide surface. Here, we apply two enhanced sampling methods to independently probe the effects of post-translational modification and peptide structure on adsorption. First, well-tempered metadynamics was used to capture the binding of acetyl and N-methylamide capped glutamic acid and Gla single amino acids onto crystalline hydroxyapatite and titania model surfaces at physiological pH. Following this, parallel tempering metadynamics in the well-tempered ensemble (PTMetaD-WTE) was used to study adsorption of the α-1 domain of osteocalcin onto hydroxyapatite and titania. Simulations were performed for the α-1 domain of osteocalcin in both its fully decarboxylated (dOC) and fully carboxylated (OC) form. Our simulations find that increased charge density due to carboxylation results in increased interactions at the interface, and stronger adsorption of the single amino acids to both surfaces. Interestingly, the role of Gla in promoting compact and helical structure in the α-1 domain resulted in disparate binding modes at the two surfaces, which is attributed to differences in interfacial water behavior. Overall, this work provides a benchmark for understanding the mechanisms that drive adsorption of Gla-containing mineralizing proteins onto different surface chemistries.

This record contains files necessary to reproduce enhanced sampling well-tempered metadynamics (wtMTD) and parallel tempering metadynamics in the well-tempered ensemble (PT-MetaD-WTE) simulations in PLUMED using GROMACS 2018.3.

en: Impact of glutamate carboxylation in the adsorption of the alpha-1 domain of osteocalcin to hydroxyapatite and titania

biomimetics

display_name: biomimetics

biomineralization

display_name: biomineralization

metadynamics

display_name: metadynamics

molecular-dynamics

display_name: molecular-dynamics


en: Density-functional theory is widely used to predict the physical properties of materials. However, it usually fails for strongly correlated materials. A popular solution is to use the Hubbard corrections to treat strongly correlated electronic states. Unfortunately, the exact values of the Hubbard U and J parameters are initially unknown, and they can vary from one material to another. In this semi-empirical study, we explore the U and J parameter space of a group of iron-based compounds to simultaneously improve the prediction of physical properties (volume, magnetic moment, and bandgap). We used a Bayesian calibration assisted by Markov chain Monte Carlo sampling for three different exchange-correlation functionals (LDA, PBE, and PBEsol). We found that LDA requires the largest U correction. PBE has the smallest standard deviation and its U and J parameters are the most transferable to other iron-based compounds. Lastly, PBE predicts lattice parameters reasonably well without the Hubbard correction.

en: Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling

bayesian-calibration

display_name: bayesian-calibration

dftu

display_name: dftu

markov-chain-monte-carlo

display_name: markov-chain-monte-carlo

mcmc

display_name: mcmc


en: High-strength metal alloys achieve their performance via careful control of the nucleation, growth, and kinetics of precipitation. Alloy mechanical properties are then controlled by atomic scale phenomena such as shearing of the precipitates by dislocations. Atomistic modeling to understand the operative mechanisms requires length and time scales far larger than those accessible by first-principles methods. Here, a family of Behler-Parinello neural-network potentials (NNPs) for the Al-Mg-Si system is developed to enable quantitative studies of Al-6xxx alloys. The NNP is trained on metallurgically-important quantities computed by first principles density functional theory (DFT) leading to high fidelity predictions of intermetallic compounds, elastic constants, dilute solid-solution energetics, precipitate/matrix interfaces, Al stacking fault energies, antisite defect energies, and other quantities. A preliminary examination of early-stage clustering kinetics and energetics in Al-6xxx is then made, showing the formation of low-energy Mg-Si structures and the trapping of vacancies in these clusters. The Generalized Stacking Fault Energy surfaces (GSFE) for the three prevalent β" precipitate compositions in peak-aged Al-6xxx are then computed with the NNP, and are validated by DFT computations at key points. The NNP thus shows significant transferability across structures, making it a powerful approach for chemically-accurate simulations of metallurgical phenomena in Al-Mg-Si alloys.

en: Machine learning for metallurgy: a neural network potential for Al-Mg-Si

aluminum

display_name: aluminum

machine-learning

display_name: machine-learning

metallurgy

display_name: metallurgy


en: In this record, we provide the data supporting our recent results on the synthesis of nitrogen-substituted undecacene analogs. Heteroatom substitution in acenes allows to tailor their remarkable electronic properties, expected to include spin-polarization and magnetism for larger members of the acene family. Here, we present a strategy for the on-surface synthesis of three undecacene analogs substituted with four nitrogen atoms on an Au(111) substrate, by employing specifically designed diethano-bridged precursors. A similarly designed precursor is used to synthesize the pristine undecacene molecule. In the publication where the results are discussed, the experimental features of scanning probe microscopy are compared with ab initio simulations, to demonstrate that the ground state of the synthesized tetraazaundecacene has considerable open-shell character on Au(111). Additionally, we demonstrate that electronegative nitrogen atoms induce a considerable shift in energy level alignment compared to the pristine undecacene, and that the introduction of hydro-aza groups causes local anti-aromaticity in the synthesized compounds. Our work provides access to the precise fabrication of nitrogen-substituted acene and its analogs, potential building-blocks of organic electronics and spintronics, and a rich playground to explore pi-electron correlation.

en: On-surface synthesis and characterization of nitrogen-substituted undecacenes

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).

aromaticity

display_name: aromaticity

cscs

display_name: cscs

experimental

display_name: experimental

heteroacenes

display_name: heteroacenes

on-surface-synthesis

display_name: on-surface-synthesis

open-shell

display_name: open-shell

snsf

display_name: snsf