en: Solvent reverse osmosis is a pressure-driven, liquid-phase process for separating solvent mixtures, offering a potential low-energy alternative to thermal operations. Graphene oxide (GO) laminates provide tunable nanochannels to probe confined solvent transport, yet solvent–solvent separations remain underexplored due to solvation-induced structural instabilities and the small molecular sizes. Here we construct solvent-stable, supported GO nanochannel membranes that preserves integrity under pressurized solvents, and tune interlayer confinement and surface polarity via controlled chemical reduction. Across 51 solvent systems and 5 distinct nanochannels, we demonstrate that separation is governed by coupled nanoconfinement and solvent affinity, where selective interfacial association can surpass simple size-exclusion expectations. Maximum permselectivity arises from balancing channel size with retained polarity, indicating that channel shrinking alone does not optimize performance. These findings identify channel surface chemistry as a key design factor for polarity-rich solvent systems and provide a framework for rationally tailoring nanochannels for complex solvent separations.

en: Solvent reverse osmosis beyond size exclusion in 2D nanochannel membranes

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).

graphene

display_name: graphene

reverse-osmosis

display_name: reverse-osmosis

solventsolvent-separation

display_name: solventsolvent-separation


$oid: OBSOLETE

en: Correlated electron molecular orbital (CEMO) materials host emergent electronic states built from molecular orbitals localized over clusters of transition metal ions yet have historically been discovered sporadically and generally been treated as isolated case studies. Here we establish CEMO materials as a systematically discoverable class and introduce a graph-based framework to identify, classify, and organize transition-metal cluster motifs in inorganic solids. Starting from crystal structures in the Materials Project, we construct transition metal connectivity graphs, extract cluster motifs using a bond-cutting algorithm, and determine cluster point groups, effective cluster sublattice dimensionality, and translational symmetry. Applying this approach in a high-throughput screen of 34,548 compounds yields 5,306 cluster-containing materials, including 2,627 stable or metastable compounds with isolated clusters and 984 materials featuring mixed-metal clusters. The resulting dataset reveals symmetry and element dependent trends in cluster formation. By integrating cluster classification with flat band lattice topology and battery-relevant information, we provide further relevant information to multiple scientific communities. The accompanying open dataset, Cluster Finder software, and interactive web platform enable systematic exploration of cluster driven electronic phenomena and establish a general pathway for discovering correlated quantum materials and functional materials with cluster-based or extended metal-metal bonding in inorganic solids.

en: Discovery of correlated electron molecular orbital materials using graph representations

materialscloud

approval_status: approved

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

battery-materials

display_name: battery-materials

correlated-electron-materials

display_name: correlated-electron-materials

flat-band

display_name: flat-band

materials-informatics

display_name: materials-informatics


en: This electrochemical, X-ray diffraction, and operando X-ray abosorption spectroscopy data characterize and decouple the effects of bulk vs. electrolyte Fe incorporation in crystalline, defective crystalline, and amorphous catalysts derived from the same CoSn(OH)₆ precursor. Electrolyte Fe incorporation equalizes the OER activity across all three structural variants, demonstrating that surface Fe chemistry can dominate initial structural effects on the reconstructed catalyst interface.

en: Characteristic data of activation of crystalline and amorphous OER catalysts with bulk and surface Fe incorporation.

catalyst

display_name: catalyst

cobalt

display_name: cobalt

diffraction

display_name: diffraction

electrocatalysis

display_name: electrocatalysis

iron

display_name: iron

local-structure

display_name: local-structure

operando

display_name: operando

oxygen-evolution

display_name: oxygen-evolution

surface-reconstruction

display_name: surface-reconstruction

x-ray-apbsorption-spectroscopy

display_name: x-ray-apbsorption-spectroscopy


en: AMaRaNTA stands for Automating Magnetic paRAmeters iN a Tensorial ApproachThe aim of the AMaRaNTA package is to implement and automatize the widely used energy-mapping method, namely the 4-state method using density functional theory by removing the laborious, multi-step manual procedures. Based on the AiiDA framework, it extracts full spin-orbit coupling included exchange and anisotropy parameters for 2D magnets. In its current implementation (full workchain publicly available at https://github.com/AMaRaNTA-code), AMaRaNTA returns the nearest-neighbour exchange tensor, complemented by scalar parameters for second- and third-nearest-neighbour exchange interactions as well as single-ion anisotropy.Present repository is to make available the results obtained using this workchain run on a set of 29 materials, a representative subset of the Materials Cloud 2D Structure database.

en: AMaRaNTA: automated first-principles exchange parameters in 2D magnets [Results]

aiida

display_name: aiida

dft

display_name: dft

magnetic-exchanges

display_name: magnetic-exchanges


en: The precise atomic-scale structure around Eu2+ activators in the β-Si6-zAlzOzN8-z:Eu2+ commercial green phosphor remains elusive. We use the first-principles ΔSCF excited-state method, embedding of the interatomic force constants for supercells up to 3501 atoms, and Huang-Rhys theory to clarify this issue. Monte Carlo exploration is used to identify representative low-energy structural models spanning different levels of Al/O concentration z.  For the lowest-energy structure at low z, our computed photoluminescence spectrum reproduces the experimental vibronic peaks at 6K with excellent agreement in peak positions and intensities, validating the Eu-N9 coordination model with Al, O, and Eu confined to the same crystallographic plane. Analysis of the low-energy structures reveals that the electron-phonon coupling is weak (S ≈ 2.15) with a robust characteristic phonon signature across different Al/O arrangements, explaining the surprising persistence of resolved phonon replicas with increasing z. We explain the experimentally observed red-shift of emission with increasing z through systematic trends in zero-phonon line energies, modest increases in Huang-Rhys factors, and larger configurational diversity at higher compositions.

en: Micro-environment of the Eu interstitial in the β-Si6-zAlzOzN8-z:Eu2+ green phosphor

electron-phonon

display_name: electron-phonon

phosphors

display_name: phosphors

vibronic

display_name: vibronic


en: This repository accompanies the manuscript “ChemFit: A framework for automated high-dimensional model parameter optimization” and contains the source code, input structures, and simulation outputs required to reproduce the examples presented in the study. ChemFit is a Python framework for the definition and massively concurrent evaluation of simulation-based objective functions for use with gradient-free and black-box optimization algorithms.The archive includes snapshots of the source code used in the study together with the corresponding simulation metadata, logfiles, reference structures, and initial configuration files necessary to reproduce the reported optimization workflows and results.

en: Data availability for "ChemFit: a framework for automated high-dimensional model parameter optimization"

biocondensates

display_name: biocondensates

chemfit

display_name: chemfit

coarse-grained-model

display_name: coarse-grained-model

computational-physics

display_name: computational-physics

lennard-jones

display_name: lennard-jones

parameter-optimization

display_name: parameter-optimization

water-model

display_name: water-model


en:
Deorbitalization of a conventional meta-generalized-gradient exchange-correlation approximation replaces its dependence upon the Kohn-Sham kinetic energy density with a dependence on the density gradient and Laplacian. In principle, that simplification should provide improved computational performance relative to the original meta-GGA form because of the shift from an orbital-dependent generalized Kohn-Sham potential to a true KS local potential. Often that prospective gain is lost because of problematic roughness in the density caused by the density Laplacian and consequent roughness in the exchange-correlation potential from the resulting higher-order spatial derivatives of the density in it. We address the problem by constructing a deorbitalizer based on the RPP deorbitalizer [Phys. Rev. Mater. 6, 083803 (2022)] with comparative smoothness of the potential along with retention of constraint satisfaction as design goals. Applied to the r2SCAN exchange-correlation functional [J. Phys. Chem. Lett. 11, 8208 (2020)], we find substantial timing improvements for solid-state calculations over both r2SCAN and its earlier deorbitalization for high precision calculations of structural properties, while improving upon the accuracy of RPP deorbitalization for both solids and molecules.

Data archive includes data needed to reproduce the figures and tables of this work, and input scripts for test set calculations performed using VASP and NWChem electronic structure codes.

en: Performance improvement of deorbitalized exchange-correlation functionals

computation

display_name: computation

density-functional-theory

display_name: density-functional-theory

deorbitalization

display_name: deorbitalization

metagga

display_name: metagga


en:
Advanced fission and fusion technologies require materials that withstand extreme conditions, relying on irradiation-tolerant structures to suppress defect evolution and mechanical degradation. This study introduces a novel approach to immobilize irradiation-induced defects by tuning local lattice distortion in concentrated solid solution alloys. Results demonstrate that higher distortion levels significantly reduce microstructural changes. The single-phase Ni80Mo20 alloy, with the highest recorded distortion (4.82% atomic size mismatch), exhibits frozen defect motion and negligible irradiation effects. This suggests engineering lattice distortion as a straightforward method for designing irradiation-resistant metallic alloys.

This dataset provides the initial and final atomic structures from molecular dynamics simulations of single vacancy and single interstitial diffusion in Ni, NiFe, NiCoV, and Ni80Mo20 alloys, each exhibiting distinct levels of lattice distortion.

en: Achieving enhanced irradiation-resistant metallic alloys by immobilizing induced defects

irradiation-damage

display_name: irradiation-damage

lattice-distortion

display_name: lattice-distortion

metallic-alloys

display_name: metallic-alloys

molecular-dynamics

display_name: molecular-dynamics


en: We establish a quantitative relation between the altermagnetic spin-splitting and different higher order multipoles of the charge and magnetization density around the magnetic atoms. Magnetic multipoles such as octupoles or triakontadipoles have been suggested as potential ferroic order parameters for d- and g-wave altermagnetism, respectively, based mainly on qualitative symmetry arguments. We use first-principles-based electronic structure calculations to establish a clear quantitative relation between the strength of the altermagnetic spin splitting and the magnitude of certain local multipoles. We vary the magnitude of these multipoles either by applying an appropriate constraint on the charge density or by varying a corresponding structural distortion mode, using two simple perovskite materials, SrCrO₃ and LaVO₃, as model systems. Our analysis indicates that in general the altermagnetic spin splitting is not exclusively determined by the lowest order nonzero magnetic multipole, but results from a superposition of contributions from different multipoles with comparable strength, suggesting the need for a multi-component order parameter to describe altermagnetism. We also discuss different measures to quantify the overall spin-splitting of a material, without relying on features that might be specific to only individual bands.

en: Multipoles as quantitative order parameters for altermagnetic spin splitting

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).

altermagnetism

display_name: altermagnetism

dft

display_name: dft

multipoles

display_name: multipoles


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"

materialscloud

en: Materials Cloud

condensed-matter-physics

display_name: condensed-matter-physics

hexagonal-ge

display_name: hexagonal-ge

model-bse

display_name: model-bse

optoelectronic-materials

display_name: optoelectronic-materials

polytypes

display_name: polytypes


en: Maximally-localized Wannier functions are quantum wavefunctions resembling atomic orbitals that are used to describe electrons in condensed matter. Since their introduction in 1997, these functions have become ubiquitous in ab initio materials simulations, including applications in linear-scaling methods, strongly correlated electron systems, quantum transport, electron-phonon interactions, and topological materials. Despite their widespread adoption in a vast software ecosystem, Wannier functions have not yet attained their fullest potential in the presence of entangled bands, as their optimization remains challenging and labor-intensive. Here, we introduce a universal meta-optimization method that leverages workflow abstraction and machine-learning techniques like differential evolution and Bayesian optimization to generate globally optimized Wannier functions without human intervention. We demonstrate this approach through three applications: (i) autonomous interpolation of entangled band structures with millielectronvolt accuracy starting from coarse Brillouin zone grids, (ii) thousand-fold acceleration of fully ab initio Boltzmann transport calculations via the use of minimal coarse Brillouin zone grids, and (iii) ultrafast high-throughput calculations of high-precision Wannier functions for large materials libraries. This work brings calculations that previously required supercomputers within the reach of personal computers.The data generated in this work and reported in the manuscript is attached in the Data.tar.gz with full description in the Readme.md.

en: Meta-optimization of maximally-localized Wannier functions

envi

en: Environment

machine-learning

display_name: machine-learning

meta-optimization

display_name: meta-optimization

wannier-functions

display_name: wannier-functions


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

dislocation

display_name: dislocation

magnesium

display_name: magnesium

neural-network-potential

display_name: neural-network-potential


en: This dataset provides the computational data supporting the manuscript entitled "Molybdenum Oxidation State Governs Brønsted–Lewis Acid Synergy in Dimethyl Oxalate Transesterification" by Li et al., providing all computational data underlying the reported adsorption behaviors on MoO3(010) and MoO2(011) surfaces. It includes optimized bulk and surface structures, representative CP2K input/output files, and post-processed electron density data, enabling reproducibility of the adsorption energies and electron density difference maps for dimethyl oxalate (DMO) and ethanol. The calculations reveal that DMO adsorbs more strongly on MoO3(010) (−0.9 eV) than on MoO2(011) (−0.7 eV), reflecting more facile Lewis acid activation by the Mo6+-dominated surface. Electron density accumulates between the surface Mo and the carbonyl O atom, while the carbonyl C loses density, indicating polarization of the molecule upon adsorption. Ethanol adsorption shows even more pronounced differences: on MoO3(010), it interacts either with a surface Lewis acidic Mo atom or a Brønsted acidic Mo–OH site, both configurations exhibiting similar adsorption energies of approximately −1.1 eV. Adsorption on the Lewis acid site induces electron accumulation at the Mo–O bond and activation of the hydroxyl H, whereas adsorption on the Brønsted acid site increases the overall electronegativity of ethanol, enhancing its nucleophilicity. In contrast, ethanol binds weakly (−0.1 eV) to MoO2(011) via a hydrogen bond with a surface O atom, with negligible interaction with Mo sites. All data are organized in a comprehensive folder structure covering bulk crystal models, isolated molecules, surface optimizations, and adsorption energy evaluations, with hydroxylated and non-hydroxylated MoO3 surfaces included. Computational settings employ CP2K (GPW) with PBEsol for structural optimizations and BEEF-vdW for energy calculations, along with specified basis sets, k-point sampling, and slab models. The dataset enables verification of all figures and tables in the manuscript and facilitates detailed analysis of Brønsted–Lewis acid synergy in MoOx-catalyzed transesterification.

en: Data for the adsorption properties of dimethyl oxalate and ethanol on the MoO3(010) and MoO2(011) surfaces

adsorption

display_name: adsorption

dimethyl-oxalate

display_name: dimethyl-oxalate

electron-density-difference

display_name: electron-density-difference

moox

display_name: moox


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

dft

display_name: dft

nanowires

display_name: nanowires

silicon

display_name: silicon

surface-adsorption

display_name: surface-adsorption


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

generative-ai

display_name: generative-ai

mlip

display_name: mlip


en: Hydrogen carriers that enable efficient transport and on-demand release of molecular hydrogen (H2) are crucial for practical hydrogen-based energy systems. Hydrogen boride (HB) nanosheets, composed of boron and hydrogen in a 1:1 stoichiometric ratio, have promising potential as safe and lightweight hydrogen carriers owing to their high gravimetric hydrogen density (8.5 wt %). In particular, heating of HB nanosheets results in H2 release over a broad temperature range from 423 to 1473 K. However, the mechanism of the multimodal H₂ desorption remains unclear. In this work, we elucidate that the interlayer H···H distances (dH···H) determine the multimodal desorption of H2 especially in the lower-temperature range (< 623 K). HB nanosheets subjected to various temperatures under different H2 pressures were prepared to investigate the thermal stability of their bonding configurations. Infrared spectroscopy and temperature-programmed desorption measurements revealed the occurrence of hydrogen depletion while the bonding configuration remains unchanged. The first to third nearest dH···H, along with the numbers of the corresponding H···H pairs, were systematically calculated for four possible interlayer stacking types. The calculated H···H pairs distribution closely matched the experimental profile for the thermally induced H2 desorption, suggesting that the multimodal desorption of H2 in the lower-temperature range is governed by the distribution of hydrogen distances. This study elucidates the unique mechanisms of H2 formation in HB nanosheets and provides valuable insights into the design of two-dimensional hydrogen containing materials.

en: Interlayer hydrogen-hydrogen spacing regulates the formation of molecular hydrogen in hydrogen boride nanosheets

hydrogen-boride-nanosheet

display_name: hydrogen-boride-nanosheet

hydrogen-release

display_name: hydrogen-release

interlayer-space

display_name: interlayer-space

two-dimensional-materials

display_name: two-dimensional-materials


en: Van der Waals (vdW) interactions play a crucial role in the formation of various systems. Two-dimensional (2D) metals have attracted considerable attention due to their unique properties, particularly in applications where vdW interactions play a significant role.  In particular, a graphene template facilitates the self-assembly of 2D metal nanosheets. A study of vdW interactions between copperene and graphene has been carried out. The behavior of the binding energy of copperene/graphene is studied within the framework of DFT-D3.Collection of data, input and output files. Calculations were performed using Quantum ESPRESSO.

en: Long-range van der Waals forces between graphene and copperene: first principles 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).

input-files

display_name: input-files

output-files

display_name: output-files

quantum-espresso

display_name: quantum-espresso


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

materialscloud

en: Materials Cloud

electronic-structure

display_name: electronic-structure

equation-discovery

display_name: equation-discovery

gw

display_name: gw

machine-learning

display_name: machine-learning

many-body-perturbation-theory

display_name: many-body-perturbation-theory

symbolic-regression

display_name: symbolic-regression


en: In the field of orthopedic biomechanics, synthetic bone surrogates are essential for evaluating surgical techniques, implant stability, and fracture risks due to their reproducible geometry and consistent mechanical behavior compared to human cadaveric tissue. Fourth-generation (4th Gen) Sawbones(R) composite models, which utilize a short fiber-filled epoxy (SFFE) cortical shell and a polyurethane foam (PUF) cancellous core, represent the industry standard. However, a significant gap remains in the literature regarding the full anisotropic characterization of these individual constituents, which is critical for high-fidelity numerical simulations. This study presents a comprehensive experimental dataset covering uniaxial tension (ASTM D638), compression (ASTM D695), and fracture toughness (ASTM E399) tests, alongside specialized shear characterization using a modified Single Cube Apparatus (SCA). The dataset includes 93 successful mechanical tests: 56 for the SFFE cortical surrogate and 37 for the PUF cancellous surrogate. The results validate the transverse isotropy of the SFFE and the isotropic behavior of the PUF. These findings provide the required mechanical properties for accurate finite element studies, facilitating the rigorous validation of synthetic bone surrogates as representations of biological human tissue.

en: Dataset for characterization of fracture and elastic properties of commercially available polyurethane foam and short fiber filled epoxy for bone models

envi

en: Environment

anisotropy

display_name: anisotropy

bone-substitutes

display_name: bone-substitutes

characterization

display_name: characterization

experimental

display_name: experimental

fracture-toughness

display_name: fracture-toughness

sawbones

display_name: sawbones


en: Rutile Sn1−x Gex O2 alloys are promising materials for high-power electronic applications due to their dopability and tunable ultra-wide band gaps. We use first-principles density functional theory and statistical mechanics to investigate the crystallographic, electronic, and thermodynamic properties of rutile Sn1−x Gex O2 alloys. We predict that the lattice parameters follow Vegard’s law, while band gaps calculated with the hybrid HSE06 functional exhibit strong bowing, consistent with experiment. We also predict that the disordered phase has a large positive mixing enthalpy and a slight tendency for Ge–Sn clustering, indicated by weakly negative short-range order parameters. This large positive mixing enthalpy produces a miscibility gap with a critical temperature above 2300 K, implying that the high Ge and Sn solubilities observed in thin-film synthesis cannot be explained by the incoherent phase diagram alone. We demonstrate that coherency strain substantially alters phase stability. Calculations of the coherent spinodal show significant suppression of the miscibility gap, reducing the critical temperature to ≈ 900 K. These coherent phase boundaries could account for the experimentally observed high solubilities at typical growth temperatures. Our results indicate that coherency strain stabilizes these metastable alloys and enables bandgap engineering in this ultrawide-bandgap material system.

en: Thermodynamic and electronic properties of rutile Sn1−x Gex O2 alloys from first principles

band-gap

display_name: band-gap

classical-statistical-mechanics

display_name: classical-statistical-mechanics

thermodynamics-of-mixing

display_name: thermodynamics-of-mixing


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

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

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).

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: 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

materialscloud

en: Materials Cloud

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

envi

en: Environment

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

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

dftu

display_name: dftu

multipoles

display_name: multipoles

spin-orbit-coupling

display_name: spin-orbit-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: 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

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

atomistic-simulations

display_name: atomistic-simulations

cscs

display_name: cscs

equivariance

display_name: equivariance

erc

display_name: erc

interatomic-potentials

display_name: interatomic-potentials

machine-learning

display_name: machine-learning

snsf

display_name: snsf


en: Magnetic materials are crucial for manipulating electron spin and magnetic fields, enabling applications in data storage, spintronics, charge transport, and energy conversion, while also providing insight into fundamental quantum phenomena. In numerous applications, the interaction between electrons and lattice vibrations, known as electron-phonon coupling, can be of significant importance. In that regard, we extend the EPW package to be able to interpolate the electron-phonon matrix elements combining perturbation theory and maximally localized Wannier functions. This advance allows to use dense momentum grids at a reasonable computational cost when computing electron-phonon-related quantities and physical properties. We validate our implementation considering ferromagnetic iron and nickel where we explore the absence of phonon-driven superconductivity, finding that superconductivity is intrinsically suppressed. Furthermore, we evaluate the carrier resistivity at finite temperatures for both systems, considering the role of magnetism in carrier transport. Our findings indicate that in the case of Fe, the primary contributor to resistivity is electron-phonon scattering. In contrast, for Ni, electron-phonon scattering constitutes less than one-third of the resistivity, underscoring a fundamental difference in the transport properties of the two systems.

en: Electron-phonon coupling in magnetic materials using the local spin density approximation

materialscloud

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

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

electron-phonon-coupling

display_name: electron-phonon-coupling

magnetism

display_name: magnetism

wannier-functions

display_name: wannier-functions


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

materialscloud

en: Materials Cloud

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

envi

en: Environment

carrier-mobility

display_name: carrier-mobility

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: 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: This dataset supports a spin-polarized density functional theory study examining whether O₂-activation-favorable active-site environments at Fe- and Co-substituted sulfur vacancies in monolayer MoS₂ can produce a comparable response for H-free N₂ adsorption. Side-on and end-on N₂ adsorption branches were evaluated together with projected density of states and crystal orbital Hamilton population analyses to distinguish coordination geometry, thermodynamic preference, and intramolecular bond weakening. A perpendicular electric field and K/Na co-adsorption were further considered as physical and chemical perturbations. The calculations show that shared side-on coordination does not produce O₂-like N₂ activation, while the thermodynamically preferred end-on branch retains a shorter and stronger N–N bond. Alkali co-adsorption substantially enhances N–N bond weakening but does not eliminate the O₂–N₂ activation gap or reverse the end-on thermodynamic preference. The archive provides the optimized structures, representative computational input and output data, and numerical source data supporting the reported analyses.

en: Alkali co-adsorption enhances bond-resolved N2 activation at O2-active MoS2 single-atom sites

alkali-promotion

display_name: alkali-promotion

cohp

display_name: cohp

density-functional-theory

display_name: density-functional-theory

nitrogen-activation

display_name: nitrogen-activation

single-atom-catalysis

display_name: single-atom-catalysis


en: The FAIR principles have transformed how computational materials data and workflows are shared, yet existing repositories can only serve pre-computed entries — their coverage is perpetually incomplete and cannot adapt to new questions on demand. We built OptiMat Alloys, a large-language-model-powered conversational agent for multi-principal element alloy exploration, on three pillars: a living database that stores every calculation with provenance, low-barrier accessibility through a web interface that requires zero programming expertise, and built-in uncertainty quantification via cross-potential and cross-configuration validation. By coupling foundational machine-learning interatomic potentials that span nearly the entire periodic table with natural-language interaction, the agent enables targeted, on-demand computation guided by the user's own domain knowledge — extending FAIR from pre-computed repositories to on-demand knowledge generation, and making computational alloy screening accessible to any materials scientist.

en: OptiMat Alloys: a FAIR, living database of multi-principal element alloys enabled by a conversational agent

fair-principle

display_name: fair-principle

llm-agent

display_name: llm-agent

materials-informatics

display_name: materials-informatics

multi-principal-element-alloys

display_name: multi-principal-element-alloys

universal-machine-learning-potentials

display_name: universal-machine-learning-potentials


en: Magnesium oxide is a paradigmatic ionic oxide, yet its experimentally observed chemical activity remains difficult to reconcile with its wide band gap and non-reducible character. Here, we combine polarization-dependent infrared reflection–absorption spectroscopy with hybrid density functional theory to investigate CO adsorption on welldefined MgO singlecrystal surfaces, explicitly disentangling the roles of surface orientation, point defects, and lowcoordination step-edge sites. Using CO as a site-sensitive vibrational probe, we demonstrate that the reactivity of MgO(100) originates from fourfold-coordinated Mg4c2+ step-edge sites rather than terrace Schottky defects. Distinct vibrational signatures, adsorption geometries, and binding energies are observed in quantitative agreement between experiment and theory. Onthe more open MgO(110) surface, where Mg4c2+ sites are intrinsic to the terrace, a coordinationcontrolled trend emerges: Mg4c2+ sites bind CO more strongly and exhibit higher-frequency bands than Mg5c2+ sites. Analysis of transition dipole moments further reveals a substantial enhancement of infrared intensities at low-coordinated sites, underscoring the need to account for site-specific dipole strengths in spectral interpretation. These results establish extended lowcoordination motifs as the dominant defect sites on MgO surfaces, with direct implications for metal anchoring and the design of defect-engineered oxide catalysts

en: CO adsorption on MgO revisited: Polarization-resolved identification of step edges as the dominant defects on MgO(100) and MgO(110)

active-sites

display_name: active-sites

dft

display_name: dft

irras

display_name: irras

magnesium-oxide

display_name: magnesium-oxide

schottky-defects

display_name: schottky-defects

step-edges

display_name: step-edges


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 mechanistic pathways underlying catalytic CO2 hydrogenation

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).

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: This record presents a first-principles oxygen K-edge X-ray absorption spectroscopy (XAS) dataset for the Ni-rich layered cathode material NMC811 (Li₁₋ₓNi₀.₈Mn₀.₁Co₀.₁O₂) across three states of charge. Nine NMC811 supercells were considered: three low-energy cation/vacancy configurations at each of three lithiation levels (pristine x = 0.0, half-delithiated x = 0.5, and fully delithiated x = 1.0), each supercell containing 60 transition-metal sites (48 Ni, 6 Mn, 6 Co) and 120 oxygen sites. For every symmetrically inequivalent oxygen site, the O K-edge spectrum was computed with the excited-electron-and-core-hole (XCH) method as implemented in VASP 6.4.2, using the R2SCAN meta-GGA functional and GW-type PAW data sets; spectra were aligned via their total-energy-difference excitation onsets. The dataset contains 923 site-resolved O K-edge spectra together with the ground-state reference structures and the VASP input files (INCAR, POSCAR, KPOINTS) for both the ground-state and the core-hole calculations. The site-resolved spectra encode the spectral signatures of the individual local oxygen environments and can be combined into per-structure spectra or exploited further, for instance for machine-learning-assisted spectral interpretation. Helper scripts and a structure index are included. POTCAR files are not distributed (VASP license); experimental reference spectra used in the associated manuscript are not included and should be obtained from the original publication.

en: Oxygen K-edge X-ray absorption spectroscopy database for NMC811 layered cathode materials

materialscloud

en: Materials Cloud

first-principles-spectra-database

display_name: first-principles-spectra-database

x-ray-absorption-spectroscopy

display_name: x-ray-absorption-spectroscopy


en:
Machine learning interatomic potentials (MLIPs) are routinely used to model diverse atomistic phenomena, yet parameterizing them to accurately capture solid-state phase transformations remains difficult. We present error metrics and data-generation schemes designed to streamline the parameterization of MLIPs for modeling precipitation in multi-component alloys. We developed an algorithm that enumerates symmetrically distinct transformation pathways connecting chemical decorations on different parent crystal structures. Additionally, we introduce the weighted Kendall-τ coefficient and its semi-grand canonical generalization as metrics for quantifying MLIP accuracy in predicting low-temperature thermodynamics. We apply these approaches to parameterize an MLIP for a dilute Mg-Nd alloy. The resulting potential reproduces the complex early-stage precipitation behavior observed experimentally. Large-scale atomistic simulations reveal competition between order-disorder and structural transformations. Furthermore, these results suggest a continuous transition between high-symmetry hcp and bcc crystal structures during aging heat treatments.

The datasets contain the crystal structure data used to fit the Mg-Nd interatomic potential. The data include bulk Mg and Nd structures in different coordination environments and at varying densities; several point and structural defects of bulk Mg; symmetry-distinct chemical orderings of Mg and Nd on different parent lattices; and several symmetry-unique hcp-to-bcc transformation pathways of different Mg-Nd chemical decorations. A comprehensive overview of each dataset is provided in the paper's supplemental material.

en: Machine learning interatomic potentials for solid-state precipitation

envi

en: Environment

marvel

display_name: marvel

metallurgy

display_name: metallurgy


en: The determination of magnetic exchange interactions is essential for the quantitative description and predictive modeling of magnetic materials. In this work, we present a neighbor-shell-based screening method for selecting magnetic configurations suitable for extracting Heisenberg exchange parameters beyond nearest-neighbor from density functional theory calculations. Using only the structure, the proposed approach identifies the linear independence of neighbor-shell contributions before any first-principles calculations instead of relying on trial-and-error generation of magnetic configurations. We apply the proposed approach to two representative classes of magnetic configurations: random spin states and spin spirals, and validate its predictions against direct DFT fitting for Fe and MnF$_2$. We show that only parameters obtained when all relevant neighbor-shell contributions are linearly independent remain transferable to other magnetic configurations. This dataset contains the first-principles calculation data and fitting results supporting the study of magnetic-configuration selection for the extraction of Heisenberg exchange interactions.

en: Magnetic-configuration design for reliable Heisenberg exchange parameters

heisenberg-exchange-interactions

display_name: heisenberg-exchange-interactions

neighbor-shell-analysis

display_name: neighbor-shell-analysis

random-spin-states

display_name: random-spin-states

spin-spirals

display_name: spin-spirals


en: Electrical conductivity is a key property for the design of new metallic conductors, but its accurate first-principles prediction is computationally expensive, which has limited its exploration in high-throughput materials screening. In the associated work, we combine machine-learning surrogate models with density-functional theory (DFT) and density-functional perturbation theory (DFPT) to screen approximately 2.8 million inorganic compounds from the Alexandria database for metallic electrical conductivity, using the electron–phonon coupling constant λ from Eliashberg theory as a proxy for scattering strength. From this pool, only the compounds predicted to exceed a high-conductivity threshold were carried forward for explicit electron–phonon coupling calculations and Boltzmann transport theory using the EPW code, solving the iterative linearized Boltzmann transport equation (BTE), narrowing the candidates down to 52 top-performing compounds. This dataset provides the DFT, DFPT, and EPW input and output files for these 52 candidates, together with 24 elemental benchmark metals and 3 spin-orbit-coupling reruns computed for validation.

en: High-throughput study of electrical conductivity in ordered metals

eletrical-conductivity

display_name: eletrical-conductivity

epw

display_name: epw

high-throughput

display_name: high-throughput

quantum-espresso

display_name: quantum-espresso


en: The targeted design of multi-RE-principal-component RE2Si2O7 disilicates ((nRExi)2Si2O7) for environmental barrier coatings (EBCs) applications requires customizing the multi-RE compositions to achieve maximal optimization on the target properties, with a prerequisite to control their high-temperature stabilized phase as β or γ polymorphs. This is challenged by their rich polymorphic phases varied with the elemental properties on the RE cationic sites. In this study, a random forest (RF) model is developed with high accuracy to classify the four types of high-temperature stabilized phase composition − single-β, single-γ, single-δ/mixed δ+γ, and phase separation – identifying the average RE3+ cationic radius ( ) and the deviation of RE3+ cationic radius ( ) as the most influential factors. The well-trained model is validated on the prediction of (Gdx1Hox2Ybx3Lux4)2Si2O7 and (Ndx1Hox2Ybx3Lux4)2Si2O7 systems, supported by experimental characterization of six representative compositions. High-throughput DFT calculations reveal that the formation of their high-temperature stabilized phases correlates with the the low energy costs to accommodate configurational randomness into the multicomponent system, characterized by rapid convergence of the configurational entropy of mixing with increased excitation energy. The design criteria for single-phase β-(nRExi)2Si2O7 and γ-(nRExi)2Si2O7 disilicates are established. This work provides an investigation paradigm enabling the targeted design of (nRExi)2Si2O7 EBCs materials.

en: Machine learning empowered compositional design of multiple rare-earth principal component disilicates for environmental barrier coatings applications

environmental-barrier-coatings

display_name: environmental-barrier-coatings

high-entropy-rare-earth-disilicates

display_name: high-entropy-rare-earth-disilicates

phase-composition

display_name: phase-composition


en: Polaron formation localizes charge carriers and drives a crossover from band-like to hopping transport in materials. Hopping dynamics can be obtained from DFT supercell calculations of transition states, but these suffer from polaron self-interaction, spurious electrostatics, and poor scaling with polaron size. We introduce a supercell-free framework for ab initio polaron hopping transport based on variational polaron equations and the string method. The approach optimizes transition states between self-trapped polaron states directly in reciprocal space and provides the polaron configurations along the path, enabling evaluation of adiabatic hopping rates and mobilities. We apply the method to LiF and rutile TiO2, revealing multi-step and anisotropic hopping mechanisms. In rutile TiO2, the computed electron-polaron mobility agrees with experiment, whereas band-like Boltzmann transport substantially overestimates the mobility. Our results establish a scalable route to first-principles polaron-hopping dynamics in materials in which charge motion is governed by self-trapping.This archive contains the data necessary to reproduce the results of the "Optimal transition states for polaron hopping transport without supercells" article by Vasilii Vasilchenko, Matteo Giantomassi, Samuel Poncé, and Xavier Gonze.

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles-calculations

display_name: first-principles-calculations

hopping-transport

display_name: hopping-transport

minimum-energy-paths

display_name: minimum-energy-paths

polarons

display_name: polarons


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

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

en: GapExtractor v2.0

band-gap-energy

display_name: band-gap-energy

diffuse-reflectance-spectroscopy

display_name: diffuse-reflectance-spectroscopy

polyphasic-samples

display_name: polyphasic-samples

tauc-method

display_name: tauc-method


en:
Magnet/superconductor hybrid systems have been put forward as a platform for realizing topological superconductivity. We investigated the heterostructure of ferromagnetic monolayer CrCl₃ and superconducting NbSe₂. Using low-temperature scanning tunneling microscopy, we observe topologically trivial Yu-Shiba-Rusinov (YSR) states localized at the edge of CrCl₃ islands. DFT simulations reveal that the Cr atoms at the edge have an enhanced d-orbital DOS close to the Fermi energy. This leads to an exchange coupling between these atoms and the substrate that rationalizes the edge-localization of the YSR states.

This dataset contains the first-principles calculations performed on a nanoribbon of CrCl₃ on NbSe₂ and the associated notebooks used to generate figures from this data.

en: One dimensional edge localized YSR states in CrCl₃ on NbSe₂

aiida

display_name: aiida

aiida-siesta

display_name: aiida-siesta

density-functional-theory

display_name: density-functional-theory

heterostructure

display_name: heterostructure

horizon-europe

display_name: horizon-europe

magnetism

display_name: magnetism

prace

display_name: prace

siesta

display_name: siesta

superconductivity

display_name: superconductivity

van-der-waals

display_name: van-der-waals

yu-shiba-rusinov

display_name: yu-shiba-rusinov


en: Recent experiments show that the CoCrFeNiPd high-entropy alloy (HEA) is significantly stronger than CoCrFeNi and with nanoscale composition fluctuations beyond those expected for random alloys. These fluctuations were suggested to be responsible for strengthening. Here, a recent parameter-free theory for initial yield strength in fcc random alloys is shown to predict the strength of CoCrFeNiPd in good agreement with experiments. The strengthening is due mainly to the large misfit volume of Pd in CoCrFeNi, indicating that the effects of the non-random composition fluctuations are secondary. Analyses of strength variations and strengthening-associated length scales help rationalize why dislocation motion is insensitive to such fluctuations. These findings point to the value of theory for understanding the mechanical behavior of HEAs.

en: Origin of high strength in the CoCrFeNiPd high-entropy alloy

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).

epfl

display_name: epfl

high-entropy-alloys

display_name: high-entropy-alloys

palladium

display_name: palladium

solute-strengthening-theory

display_name: solute-strengthening-theory

yield-strength

display_name: yield-strength


en: Transitioning from small-scale to big-data studies has the potential to reveal new layers of intricacy that better facilitate and rationalize catalytic behavior. Given the computational resources available today, data-driven approaches can propel the next leap forward in catalyst design. Using a data-driven inspired workflow consisting of data generation, statistical analysis, and dimensionality reduction algorithms we explore trends surrounding the thermodynamics of a model hydroformylation reaction catalyzed by group 9 metals bearing phosphine ligands. Specifically, we introduce a type of "augmented" volcano plot, the energetic profile similarity (EPSim) map, as a means to easily visualize the similarity of each catalyst's complete catalytic cycle energy profile to that of a hypothetical ideal reference profile without relying upon linear scaling relationships. In addition to quickly identifying catalysts that most closely match the ideal thermodynamic catalytic cycle energy profile, these maps also enable a more refined comparison of species lying closely in standard volcano plots. For the reaction studied here, they inherently uncover the presence of multiple sets of scaling relationships differentiated by metal type, where iridium catalysts follow distinct relationships than cobalt/rhodium catalysts and have profiles that more closely match the ideal thermodynamic profile. Reconstituted molecular volcano plots confirm the findings of the EPSim maps by showing that hydroformylation thermodynamics are governed by two distinct volcano shapes, one for iridium catalysts and a second for cobalt/rhodium species.

en: Data-powered augmented volcano plots for homogeneous catalysis

materialscloud

en: Materials Cloud

augmented-volcano-plots

display_name: augmented-volcano-plots

big-data

display_name: big-data

epsim-maps

display_name: epsim-maps

homogeneous-catalysis

display_name: homogeneous-catalysis

hydroformilation

display_name: hydroformilation


en: The development of machine-learning models for atomic-scale simulations has benefitted tremendously from the large databases of materials and molecular properties computed in the past two decades using electronic-structure calculations. More recently, these databases have made it possible to train “universal” models that aim at making accurate predictions for arbitrary atomic geometries and compositions. The construction of many of these databases was however in itself aimed at materials discovery, and therefore targeted primarily to sample stable, or at least plausible, structures and to make the most accurate predictions for each compound – e.g. adjusting the calculation details to the material at hand. Here we introduce a dataset designed specifically to train models that can provide reasonable predictions for arbitrary structures, and that therefore follows a different philosophy. Starting from relatively small sets of stable structures, the dataset is built to contain “massive atomic diversity” (MAD) by aggressively distorting these configurations, with near-complete disregard for the stability of the resulting configurations. The electronic structure details, on the other hand, are chosen to maximize consistency rather than to obtain the most accurate prediction fora given structure, or to minimize computational effort. The MAD dataset we present here, despite containing fewer than 100k structures, has already been shown to enable training universal interatomic potentials that are competitive with models trained on traditional datasets with two to three orders of magnitude more structures. We describe in detail the philosophy and details of the construction of the MAD dataset. We also introduce a low-dimensional structural latent space that allows us to compare it with other popular datasets, and that can also be used as a general-purpose materials cartography tool.

en: Massive Atomic Diversity: a compact universal dataset for atomistic machine learning

envi

en: Environment

atomistic-simulations

display_name: atomistic-simulations

dataset

display_name: dataset

density-functional-theory

display_name: density-functional-theory

foundation-models

display_name: foundation-models

interatomic-potentials

display_name: interatomic-potentials

machine-learning

display_name: machine-learning


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

ab-initio

display_name: ab-initio

enantioselectivity

display_name: enantioselectivity

scanning-probe-microscopy

display_name: scanning-probe-microscopy

snsf

display_name: snsf


en: We combine the linearized Boltzmann Transport Equation (LBTE) and quantum transport by means of the Non-equilibrium Green's Functions (NEGF) to simulate monolayer MoS2 and WS2 ultra-scaled transistors with carrier mobilities extracted from experiments. Electron-phonon, charged impurity, and surface optical phonon scattering are taken into account with all necessary parameters derived from ab initio calculations or measurements, except for the impurity concentration. The LBTE method is used to scale the scattering self-energies of NEGF, which only include local interactions. This ensures an accurate reproduction of the measured mobilities by NEGF. We then perform device simulations and demonstrate that the considered transistors operate far from their performance limit (from 50% for MoS2 to 60% for WS2). Higher quality materials and substrate engineering will be needed to improve the situation.

en: Ab initio mobility of single-layer MoS2 and WS2: comparison to experiments and impact on the device characteristics

lbte

display_name: lbte

quantum-transport

display_name: quantum-transport


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

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: Cu2O has appealing properties as an electrode for photo-electrochemical water splitting, yet its practical performance is severely limited by inefficient charge extraction at the interface. Using hybrid DFT calculations, we investigate carrier capture processes by oxygen vacancies (VO) in the experimentally observed (√3×√3)R30° reconstruction of the dominant (111) surface. Our results show that these VO are doubly ionized and that associated defects states strongly suppress electron transport. In particular, the excited electronic state of a singly charged VO plays a crucial role in the non-radiative electron capture process with a capture coefficient of about 10^-9 cm3/s and a lifetime of 0.04 ps, explaining the experimentally observed ultrafast carrier relaxation. These results highlight that engineering the surface VO chemistry will be a crucial step in optimizing Cu2O for photoelectrode applications.

en: Importance of surface oxygen vacancies for ultrafast hot carrier relaxation and transport in Cu2O

carrier-capture

display_name: carrier-capture

cscs

display_name: cscs

hybrid-dft

display_name: hybrid-dft

oxygen-vacancies

display_name: oxygen-vacancies

surface-reconstruction

display_name: surface-reconstruction


en: Density functional theory calculations have been a pillar of computational materials science in the past two decades, and the recent development of AI and data-driven methods has reiterated their importance. ABACUS (Atomic-orbital Based Ab-initio Computation at USTC) is an open-source density functional theory code that supports both plane-wave and numerical atomic orbital basis sets. It is designed as a versatile platform integrating various electronic structure methods and is particularly well-suited for deep-learning-assisted materials simulations. The accuracy of a density functional theory code is crucial for its wide deployment. In this work, we use the verification protocol established by E. Bosoni et al. [Nat. Rev. Phys. 6, 45-58 (2024)] based on the AiiDA common workflows framework to validate the precision of ABACUS by comparing its results with previously generated all-electron benchmarks. Our results show that, using consistent pseudopotentials and comparable calculation settings, ABACUS yields highly consistent results compared to other established codes such as QE, Abinit, and CASTEP. This entry contains all the data, calculations, and scripts required to reproduce the results.

en: Precision verification results data for ABACUS@PW|PseudoDojo-v0.4

automation

display_name: automation

dft

display_name: dft

equation-of-state

display_name: equation-of-state

pseudopotentials

display_name: pseudopotentials

verification

display_name: verification