en: Recent advances in nanomaterials have pushed the boundaries of nanoscale fabrication to the limit of single atoms, particularly in heterogeneous catalysis. Single atom catalysts, comprising minute amounts of transition metals dispersed on inert substrates, have emerged as prominent materials in this domain. However, overcoming the tendency of these single atoms to cluster beyond cryogenic temperatures and precisely arranging them on surfaces with desired local environments pose significant challenges. Employing organic templates for orchestrating and modulating the activity of single atoms holds promise. In recent work, we introduce a novel single-atom platform wherein atoms are firmly anchored to specific coordination sites distributed along carbon-based polymers, synthesised via on-surface synthesis. These platforms exhibit atomic-level structural precision and stability, even at elevated temperatures. The asymmetry in the structure and electronic states at the active sites anticipates the enhanced reactivity of these precisely defined reactive centers. Upon exposure to CO and CO2 gases at low temperatures, the platform demonstrates excellent trapping and conversion capabilities. Fine-tuning the structure and properties of the coordination sites offers unparalleled flexibility in tailoring functionalities, thus opening avenues for previously untapped potential in catalytic applications. This record contains data that support the results presented in the published work.

en: On-surface synthesis of tailored organic platforms for single metal atoms

asymmetric-active-sites

display_name: asymmetric-active-sites

cscs

display_name: cscs

dft

display_name: dft

on-surface-synthesis

display_name: on-surface-synthesis

single-atom-catalyst

display_name: single-atom-catalyst

spm

display_name: spm


$oid: OBSOLETE

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

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

materialscloud

approval_status: approved

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

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

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

dftuv

display_name: dftuv

strain

display_name: strain


en: This dataset contains a list of ComputedStructureEntry objects used to parameterize an embedded cluster expansion in the paper https://doi.org/10.1016/j.actamat.2025.121752. We used this model to predict finite-temperature vacancy concentrations in 9-component refractory alloys (Ti-Zr-Hf-V-Nb-Ta-Cr-Mo-W). The symmetrically distinct orderings of group 5 and 6 elements in small cells correspond to calculations reported in https://doi.org/10.1038/s41524-025-01543-3.

en: Modeling the equilibrium vacancy concentration in multi-principal element alloys from first-principles

embedded-cluster-expansion

display_name: embedded-cluster-expansion

equilbrium-vacancy-concentration

display_name: equilbrium-vacancy-concentration

machine-learning

display_name: machine-learning


en: In this work we provide the results for ionization potential (IP) and electron affinity (EA) of all the 100 molecules of the set, as computed at the G0W0 level within the Yambo code. In this way, we enlarge the GW100 benchmark considering the largely used Godby-Need Plasmon Pole Approximation (GNPPA) and the recently introduced multipole approximation (MPA), both used in Yambo to describe the frequency dependence of the screening potential and not yet included in previous GW100 studies. Fully converged results on HOMO and LUMO are provided here for each molecule of the GW100 set, as extrapolated from Yambo calculations with different value of the involved parameters (plane wave cutoff for the microscopic dielectric matrix and empty state summations).

en: Benchmarking the plasmon-pole and multipole approximations in the Yambo Code using the GW100 dataset

aiida

display_name: aiida

aiida-yambo

display_name: aiida-yambo

first-principles

display_name: first-principles

godby-need-plasmon-pole-approximation

display_name: godby-need-plasmon-pole-approximation

gw

display_name: gw

high-throughput

display_name: high-throughput

mpa

display_name: mpa

multipole-approximation

display_name: multipole-approximation

yambo

display_name: yambo

yambo-code

display_name: yambo-code


en: The overall development of the machine learning force field (MLFF) has advanced rapidly, with a wide range of models emerging in recent years. However, some fundamental questions remain underexplored, such as why certain systems are intrinsically more difficult to train than others. Understanding this question can help us to propose different models and prepare appropriate datasets for different situations. We constructed Metal-43, a high-quality dataset comprising elemental structures of 43 metallic elements. Through systematic analysis, we reveal regular trends of fitting accuracies of these elemental metals in the periodic table. Unlike previous approaches that generally attribute fitting challenges to a vague notion of a “complex potential energy surface (PES)”, which is almost a synonym of the fitting difficulty, we provide a physical picture which connects the Fermi surface complexity to this complexity of PES. Furthermore, we demonstrate that current MLFF models still face clear limitations in capturing the complex PES even for elemental materials. These findings can provide a theoretical foundation and directional guidance for the development of more general and accurate MLFF models in the future.

en: Origin of the machine learning forces field errors across metal elements

dataset

display_name: dataset

density-functional-theory

display_name: density-functional-theory

machine-learning-force-field

display_name: machine-learning-force-field


en: Achieving n- and p-type symmetric and excellent two-dimensional Schottky barrier field-effect transistors (SBFETs) remains a significant challenge, particularly when employing a homogeneous channel. The electrode contact properties for bilayer (BL) MoS2 and the corresponding device performances are investigated using ab initio calculations. Stable van der Waals-type heterojunctions are formed between BL MoS2 and monolayer (ML) Sc2NX2 (X = F or O) in primitive cells, resulting in Ohmic contacts at both vertical and lateral interfaces. Consequently, excellent and symmetric n- and p-type device performances are obtained for the 10-nm-gate BL MoS2 SBFETs with ML Sc2NF2 and Sc2NO2 electrodes operating at 0.5 V for high-performance applications. These results surpass the International Roadmap for Devices and Systems targets specified as ‘5am eq’ note for 12 nm gate devices operating at 0.6 V. The outstanding and symmetric performance indicates that BL MoS2 SBFETs with ML Sc2NF2 and Sc2NO2 electrodes are promising post-Si candidates for high-performance logic circuits.

en: Monolayer Sc2NF2 and Sc2NO2 electrodes for bilayer MoS2: Achieving symmetric and excellent performances

ab-initio-transport-calculation

display_name: ab-initio-transport-calculation

symmetric-performance

display_name: symmetric-performance


en: The conversion of semimetallic suspended graphene (Gr) to a large-gap semiconducting phase is realized by controlled adsorption of atomic hydrogen (deuterium) on free-standing Gr veils in nanoporous graphene. The effects of local rehybridization from sp² to sp³ chemical bonding are investigated by combining X-ray photoelectron spectroscopy and high-resolution electron energy-loss spectroscopy (HREELS) with ab-initio based modelling. The hydrogen adatoms on the C sites induce a stretching frequency, clearly identified in vibrational spectra thanks to the use of the D isotope, which is compatible with the predicted fingerprints of adsorption on both sides of Gr corresponding to the graphane configuration. HREELS of the deuterated samples shows a wide opening of the optical band gap, consistent with the modified spectral density observed in the valence band photoemission. The results are in agreement with ab-initio calculations by GW and Bethe-Salpeter equation approaches, showing a large quasiparticle gap opening and huge excitonic binding energy

en: Dielectric response and excitations of hydrogenated free-standing graphene

big-map

display_name: big-map

bse

display_name: bse

hreels

display_name: hreels

hydrogenated-graphene

display_name: hydrogenated-graphene

max

display_name: max

nanoporous-graphene

display_name: nanoporous-graphene


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

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

computational-materials-desing

display_name: computational-materials-desing

indoor-photovoltaics

display_name: indoor-photovoltaics

perovskite-inspired-materials

display_name: perovskite-inspired-materials

solar-cells

display_name: solar-cells


en: Kohn anomalies are kinks or dips in phonon dispersions which are pronounced in low-dimensional materials. We investigate the effects of non-adiabatic phonon self-energy on Kohn anomalies in one-dimensional metals by developing a model that analyzes how the adiabatic phonon frequency, electron effective mass, and electron-phonon coupling strength influence phonon mode renormalization. We introduce an electron-phonon coupling strength threshold for low-temperature system instability, providing experimentalists with a tool to predict them. Finally, we validate the predictions of our model against first-principles calculations on a 4 Å-diameter carbon nanotube. The dataset includes dft input files used to produce the ab-initio data and scripts to reproduce the plots present in the paper

en: Importance of non-adiabatic effects on Kohn anomalies in 1D metals

electron-phonon-coupling

display_name: electron-phonon-coupling

electronic-structure

display_name: electronic-structure

kohn-anomalies

display_name: kohn-anomalies

phonon-self-energy

display_name: phonon-self-energy

phonons

display_name: phonons


en: In the last few years several ``universal'' interatomic potentials have appeared, using machine-learning approaches to predict energy and forces of atomic configurations with arbitrary composition and structure, with an accuracy often comparable with that of the electronic-structure calculations they are trained on. Here we demonstrate that these generally-applicable models can also be built to predict explicitly the electronic structure of materials and molecules. We focus on the electronic density of states (DOS), and develop PET-MAD-DOS, a rotationally unconstrained transformer model built on the Point Edge Transformer (PET) architecture, and trained on the Massive Atomistic Diversity (MAD) dataset. We demonstrate our model's predictive abilities on samples from diverse external datasets, showing also that the DOS can be further manipulated to obtain accurate band gap predictions. A fast evaluation of the DOS is especially useful in combination with molecular simulations probing matter in finite-temperature thermodynamic conditions. To assess the accuracy of PET-MAD-DOS in this context, we evaluate the ensemble-averaged DOS and the electronic heat capacity of three technologically relevant systems: lithium thiophosphate (LPS), gallium arsenide (GaAs), and a high entropy alloy (HEA). By comparing with bespoke models, trained exclusively on system-specific datasets, we show that our universal model achieves semi-quantitative agreement for all these tasks. Furthermore, we demonstrate that fine-tuning can be performed using a small fraction of the bespoke data, yielding models that are comparable to, and sometimes better than, fully-trained bespoke models.

en: A universal machine learning model for the electronic density of states

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).

bandgap

display_name: bandgap

density-of-states

display_name: density-of-states

electronic-bands

display_name: electronic-bands

machine-learning

display_name: machine-learning

universal

display_name: universal


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

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

materialscloud

en: Materials Cloud

envi

en: Environment

carbon-nanotubes

display_name: carbon-nanotubes

experimental

display_name: experimental

gas-sensing

display_name: gas-sensing

hybrid-nanostructures

display_name: hybrid-nanostructures

n-type-conductor

display_name: n-type-conductor

tungsten-oxide-nanoclusters

display_name: tungsten-oxide-nanoclusters


en: Graphene nanoribbons (GNRs) with zigzag edges are promising materials for spintronic devices due to their tunable bandgaps and spin-polarized edge states. Porphyrins offer complementary benefits, including desirable optoelectronic properties and the incorporated metal centers as qubits for quantum computing. In a recent manuscript, we combine these features in a hybrid system via the on-surface synthesis of zigzag-edge GNRs embedded with porphyrins laterally fused along the ribbon backbone. Using scanning probe microscopy and spectroscopy, we show that this innovative design achieves strong electronic coupling between porphyrin and GNR, with the porphyrin metal center inducing significant alterations in the electronic band structure. For transition metal porphyrins, significant exchange coupling between distant metal centers is mediated by the GNR's π-electron system, along with enhanced spin polarization of the ZGNR backbone edge state. Such hybrid d- and π-electron ribbon system introduces spin-orbit coupling and magnetic anisotropy to carbon nanomaterials, and holds great promise for the fabrication of one-dimensional spin qubit arrays. This record contains data supporting the results discussed in the manuscript.

en: Zigzag graphene nanoribbons with periodic porphyrin edge extensions

cscs

display_name: cscs

dftu

display_name: dftu

feii-porphyrin

display_name: feii-porphyrin

graphene-nanoribbons

display_name: graphene-nanoribbons

on-surface-synthesis

display_name: on-surface-synthesis

snsf

display_name: snsf


en:
Emergent quasiparticles in solids often exhibit unique topological properties as a result of the complex interplay between charge, orbital, spin and lattice degrees of freedom. Among these quasiparticles, the polaron occupies a special place as the first known manifestation of the interaction between a fermion and a boson field. While polarons have been investigated for almost a century, whether these quasiparticles exhibit topological properties and why remain open questions. Here, we establish the universal symmetry principles governing the topology of polar textures in large polarons. Using a group-theoretic analysis, we identify four distinct classes of polar textures in time-reversal-invariant systems, and we show that they carry integer topological charges. We validate this classification by performing state-of-the-art first-principles calculations of materials representative of each class. For these materials, we compute the fingerprints of polaron topology in Huang diffuse scattering, and propose ultrafast electron and X-ray scattering experiments to detect these quasiparticles.

This archive contains the data necessary to reproduce the results of the "Symmetry-protected topological polarons" article by Kaifa Luo, Jon Lafuente-Bartolome, and Feliciano Giustino.

en: Symmetry-protected topological polarons

diffuse-scattering

display_name: diffuse-scattering

polarons

display_name: polarons

topology

display_name: topology


en: A key technical challenge inhibiting fossil-free energy generation, which is especially pronounced  in solar plants, is the inherent intermittency. Since thermochemical energy storage represents a  solution that has yet to see large-scale utilization, the goal of the study at hand has been to find  suitable materials using a hybrid high-throughput approach, encompassing both data mining and  first principles calculations. Through these efforts, thermal properties have been estimated for more  than 50 000 mono-, bi-, and trimetallic oxides, as well as alloys, retrieved from the Materials Project  database.Thereafter, close to 18 000 convex hulls were created, each representing a unique metal ratio, in order  to determine the most stable phases at different oxygen chemical potentials. In turn, this allowed  almost 300 000 potential transitions to be identified. After filtering out potentially toxic and rare  compositions, a little over 51 000 remained, which were further reduced, to about 3000, by requiring  that the phase transformation must occur at a temperature between 400 °C and 1300 °C.A thorough analysis of the results led to the discovery of several promising energy storage materials.  In addition, Chromium; manganese; calcium; and magnesium were found to be associated with high  reaction enthalpies. When the sensible heat was taken into account, however, light elements such as  lithium; boron; iron; and sodium dominated among the top-ranking candidates. This study therefore  demonstrates how high-throughput data mining can be efficiently enhanced through first-principles  calculations, enabled by machine-learning interatomic potentials, to facilitate material discovery.

en: Discovery of thermochemical energy storage materials via a hybrid data-mining approach

data-mining

display_name: data-mining

first-principles

display_name: first-principles

high-throughput

display_name: high-throughput

machine-learning-potentials

display_name: machine-learning-potentials

metal-oxides

display_name: metal-oxides

thermochemical-energy-storage

display_name: thermochemical-energy-storage

thermodynamics

display_name: thermodynamics


en:
Accurate determination of transition states is central to an understanding of reaction kinetics. Double-endpoint methods where both initial and final states are specified, such as the climbing image nudged elastic band (CI-NEB), identify the minimum energy path between the two and thereby the saddle point on the energy surface that is relevant for the given transition, thus providing an estimate of the transition state within the harmonic approximation of transition state theory. Such calculations can, however, incur high computational costs and may suffer stagnation on exceptionally flat or rough energy surfaces. Conversely, methods that only require specification of an initial set of atomic coordinates, such as the minimum mode following (MMF) method, offer efficiency but can converge on saddle points that are not relevant for transition of interest. Here, we present an adaptive hybrid algorithm that dynamically switches between the CI-NEB with the MMF method ("off-path" CI-NEB) so as to get faster convergence to the relevant saddle point. The method is benchmarked for the Baker-Chan (BC) saddle point test set using the PET-MAD machine-learned potential as well as 59 transitions of a heptamer island on Pt(111) from the OptBench benchmark set. A Bayesian analysis of the performance shows a median change in energy and force calculations of -46% [95% CrI: -55%, -37%] relative to CI-NEB for the BC set, while a 28% reduction is found for the transitions of the heptamer island. A fixed cutoff static switch mechanism for the BC leads to a median increase of 31% calculations compared to the OCI-NEB algorithm.  These results establish the OCI-NEB method as a highly effective tool for high-throughput automated chemical discovery of atomic rearrangements.

This record provides the reproduction data, transition state search logs, and statistical models for the Off-path Climbing Image Nudged Elastic Band (OCI-NEB) algorithm. It includes the complete outputs to accompany the Github repository of scripts which support the manuscript.

en: Enhanced Climbing Image Nudged Elastic Band method with Hessian Eigenmode Alignment

machine-learning

display_name: machine-learning

nudged-elastic-band

display_name: nudged-elastic-band

saddle-search

display_name: saddle-search

transition-state

display_name: transition-state


en: One essential ingredient in many machine learning (ML) based methods for atomistic modeling of materials and molecules is the use of locality. While allowing better system-size scaling, this systematically neglects long-range (LR) effects, such as electrostatics or dispersion interaction. We present an extension of the long distance equivariant (LODE) framework that can handle diverse LR interactions in a consistent way, and seamlessly integrates with preexisting methods by building new sets of atom centered features. We provide a direct physical interpretation of these using the multipole expansion, which allows for simpler and more efficient implementations. The framework is applied to simple toy systems as proof of concept, and a heterogeneous set of molecular dimers to push the method to its limits. By generalizing LODE to arbitrary asymptotic behaviors, we provide a coherent approach to treat arbitrary two- and many-body non-bonded interactions in the data-driven modeling of matter.

en: Physics-inspired equivariant descriptors of non-bonded interactions

dispersion

display_name: dispersion

electrostatics

display_name: electrostatics

erc

display_name: erc

long-range-interactions

display_name: long-range-interactions


en: Enhancing cycling reversibility in high-energy-density lithium metal batteries (LMBs) necessitate precise management of electrolyte-derived electrochemical reactions at electrodes and interphases, yet the recently developed and most commonly used localized high-concentration electrolytes (LHCEs) technology suffers from limited tunability on these reactions due to the non-solvation-participating nature of oxygen-proximal fluorinated diluents. Here we address this issue by designing and synthesizing a new oxygen-distal fluorinated diluent. Unlike traditional oxygen-proximal fluorinated analogs, the molecule skeleton was strategically edited to position fluorine atoms distal to oxygen centers that attenuates electron-withdrawing effects at these Li+-coordination sites, enabling: enhanced diluent/anion participation and reduced volatile solvents in solvation shells. This work establishes oxygen-distal fluorination as a new electrolyte molecular skeleton editing principle for stable energy-dense LMBs.

en: Skeletal editing of electrolyte molecule by oxygen-distal fluorination for 500 Wh kg–1, cycling stable Li metal battery

lithium-metal-battery

display_name: lithium-metal-battery

molecule-design

display_name: molecule-design

theoretical-calculation

display_name: theoretical-calculation


en: Leveraging our recent development, which incorporates hole and particle information into the multi-channel molecular orbital image (MolOrbImage), to generate exceptional accuracy (mean absolute error, MAE < 0.1 eV) in predicting excited-state energies of practical photofunctional materials containing several hundred atoms, we have advanced the implementation of a new approach to overcome the high computational cost of mean-field ground-state calculations that limits its application in high-throughput materials discovery. In this work, low-cost approaches for generating approximate orbitals, including the superposition of atomic densities technique and the semi-empirical tight-binding method, have been employed to construct cost-effective multi-channel MolOrbImages. By connecting with a convolutional neural network, the performance of our model is evaluated for both small organic molecules (MAE < 0.1 eV) and practical photofunctional materials (MAE < 0.14 eV). Perturbation analysis of MolOrbImages highlights the importance of frontier orbital energies, which further motivates the adoption of transfer learning techniques to reduce prediction errors in excited-state energies.

en: Cost-effective multi-channel MolOrbImage for machine-learned excited-state properties of practical photofunctional materials

materialscloud

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

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

excited-state

display_name: excited-state

photofunctional-materials

display_name: photofunctional-materials


en: Recently, we introduced a class of molecular representations for kernel-based regression methods — the spectrum of approximated Hamiltonian matrices (SPAᴴM) — that takes advantage of lightweight one-electron Hamiltonians traditionally used as an SCF initial guess. The original SPAᴴM variant is built from occupied-orbital energies (\ie, eigenvalues) and naturally contains all the information about nuclear charges, atomic positions, and symmetry requirements. Its advantages were demonstrated on datasets featuring a wide variation of charge and spin, for which traditional structure-based representations commonly fail. SPAᴴM(a,b), as introduced here, expands eigenvalue SPAᴴM into local and transferable representations. It relies upon one-electron density matrices to build fingerprints from atomic or bond density overlap contributions inspired from preceding state-of-the-art representations. The performance and efficiency of SPAᴴM(a,b) is assessed on the predictions for datasets of prototypical organic molecules (QM7) of different charges and azoheteroarene dyes in an excited state. Overall, both SPAᴴM(a) and SPAᴴM(b) outperform state-of-the-art representations on difficult prediction tasks such as the atomic properties of charged open-shell species and of π-conjugated systems.

en: SPAᴴM(a,b): encoding the density information from guess Hamiltonian in quantum machine learning representations

materialscloud

en: Materials Cloud

epfl

display_name: epfl

guess-hamiltonians

display_name: guess-hamiltonians

molecular-representation

display_name: molecular-representation

snsf

display_name: snsf

spahm

display_name: spahm


en: Structure determination of molecular solids through NMR crystallography relies on the generation of a comprehensive set of candidate crystal structures and on the comparison of chemical shifts computed for those candidates with experimental values. Exploring the polymorph landscape of molecular solids requires extensive computational power, which leads to a significant bottleneck in the generation of the set of candidate crystals by crystal structure prediction (CSP) protocols. Here, we use a database of crystal structures with associated chemical shifts to construct three-dimensional interaction maps in molecular crystals directly derived from a molecular structure and its associated set of experimentally measured chemical shifts. We show how the maps obtained can be used to identify structural constraints for accelerating CSP protocols and to evaluate the likelihood of candidate crystal structures without requiring DFT-level chemical shift computations.

en: Chemical shift-dependent interaction maps in molecular solids

envi

en: Environment

chemical-shift

display_name: chemical-shift

nmr

display_name: nmr

statistics

display_name: statistics


en: This document contains all raw data for microstructure and electromagnetic parameters from the unpublished thesis titled: Influence of Ho3+ Substitution on the Microstructure and Electromagnetic Properties of NiZnCo Ferrites. Its primary purpose is to provide empirical evidence demonstrating the reliability of the thesis's data.

en: Source files for microstructure and electromagnetic property data used in the paper "Influence of Ho³⁺ Substitution on the Microstructure and Electromagnetic Properties of NiZnCo Ferrites"

electromagnetic-performance

display_name: electromagnetic-performance

niznco-ferrites

display_name: niznco-ferrites

solid-phase-reaction-method

display_name: solid-phase-reaction-method


en: We propose an affinitive additive strategy featuring high donor number and dipole moment, exemplified by N,N-dimethylurea (DMU), to dynamically modulate Zn2+ solvation and the structure of the electric double layer under operational electric fields.  And this repository contains all computational data underlying the study, including: DFT-optimized molecular structures; initial and equilibrated configurations from MD simulations of bulk electrolyte and Zn/electrolyte interfaces; RDF and MSD analysis files; high-throughput xTB results (AIE, AEA, ion-binding energies); and machine learning scripts for descriptor screening. Data are organized by module with detailed READMEs to ensure full reproducibility.

en: Electric-field-reinforced affinitive electrolytes for highly reversible aqueous zinc metal batteries

batteries

display_name: batteries

descriptor

display_name: descriptor

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation


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

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

graphite

display_name: graphite

heat-hydrodynamics

display_name: heat-hydrodynamics

temperature-resonances

display_name: temperature-resonances

thermal-viscosity

display_name: thermal-viscosity


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

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

electronic-structure

display_name: electronic-structure

koopmans-spectral--functionals

display_name: koopmans-spectral--functionals

orbital-density-dependent-functionals

display_name: orbital-density-dependent-functionals

photocatalysis

display_name: photocatalysis

water-splitting

display_name: water-splitting


en: Long-range interactions and electric response are essential for accurate modeling of condensed-phase systems, but capturing them efficiently remains a challenge for atomistic machine learning. Traditionally, these two phenomena can be represented by static charges, that participate in Coulomb interactions between atoms, and dynamic charges such as atomic polar tensors - aka Born effective charges - describing the response to an external electric field. We critically compare different approaches to learn both types of charges, taking bulk water and water clusters as paradigmatic examples: (1) Learning them independently; (2) Coupling static and dynamic charges based on their physical relationship with a single global coupling constant to account for dielectric screening; (3) Coupled learning with a local, environment-dependent screening factor. In the coupled case, correcting for dielectric screening is essential, yet the common assumption of homogeneous, isotropic screening breaks down in heterogeneous systems such as water clusters. A learned, environment-dependent screening restores high accuracy for the dynamical charges. However, the accuracy gain over independent dynamic predictions is negligible, while the computational cost increases compared to using separate models for static and dynamical charges. This suggests that, despite the formal connection between the two charge types, modeling them independently is the more practical choice for both condensed-phase and isolated cluster systems.

en: Simultaneous learning of static and dynamic charges

atomistic-simulations

display_name: atomistic-simulations

born-effective-charges

display_name: born-effective-charges

density-functional-theory

display_name: density-functional-theory

interatomic-potentials

display_name: interatomic-potentials

long-range-models

display_name: long-range-models

machine-learning

display_name: machine-learning


en: Based on ab initio molecular dynamics simulations, we explain how graphene-based ion transistors work, using channels filled with K⁺ ions. Our results show that electrons in graphene allow confined ions to influence each other over long distances. This explains why the transistor responds sensitively to changes in ion density, which are controlled by the gate voltage. The ON/OFF switching behavior comes from the competition between π–π stacking and cation–π interactions, which changes with ion density. The increase in ion transport efficiency—also called signal amplification—is caused by the ions moving together in a coordinated way that depends on their density. Additionally, when ions outside the channel resonate with those inside, rapid dehydration occurs. This leads to very fast ion movement through the transistor. These atomic-scale insights can help guide the design of future ultra-low-energy neuromorphic chips.

en: Quantum correlation of channel-confined ions in graphene-based transistors for energy-efficient neuromorphic chips

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).

--stacking

display_name: --stacking

collective-ion-oscillation

display_name: collective-ion-oscillation

ion-transistor

display_name: ion-transistor

ionics

display_name: ionics


en: We calculated the interaction energies of hydrogen-benzene and hydrogen-hydrogen complexes using dispersion-inclusive exchange-correlation functionals in density functional theory and compared them with results from the coupled cluster method with single, double, and perturbative triple excitations and those from the adiabatic-connection fluctuation-dissipation theorem within the random phase approximation. For both complexes, the recent generations of nonlocal van der Waals density functionals show overall improvement over semilocal exchange-correlation functionals with dispersion corrections, yielding good agreement with the coupled cluster method. We discuss their accuracy and recommend suitable exchange-correlation functionals based on different evaluation metrics.

en: Assessing the accuracy of exchange-correlation functionals in density functional theory for hydrogen-benzene and hydrogen-hydrogen interactions

materialscloud

en: Materials Cloud

beef

display_name: beef

beef-vdw

display_name: beef-vdw

benzene

display_name: benzene

ccsdt

display_name: ccsdt

cof

display_name: cof

dft

display_name: dft

hydrogen

display_name: hydrogen

hydrogen-storage

display_name: hydrogen-storage

lda

display_name: lda

mof

display_name: mof

molgw

display_name: molgw

orca

display_name: orca

pbe

display_name: pbe

pbesol

display_name: pbesol

quantum-espresso

display_name: quantum-espresso

rpa

display_name: rpa

rpbe

display_name: rpbe

scan

display_name: scan

vasp

display_name: vasp

vdw-df

display_name: vdw-df

vdw-df-cx

display_name: vdw-df-cx


en:
Dataset: Interoperable DFT OCV Workflows (Final Alignment)

We present a dataset enabling cross‑engine alignment and reproducibility of open‑circuit voltage (OCV) calculations for battery cathode materials within standardized density functional theory (DFT) workflows. The dataset implements a common, JSON‑based input/output schema and a semantic JSON‑LD description to express the workflow’s scientific intent, inputs, methods, and outputs in a code‑agnostic manner, supporting FAIR reuse. Using shared OPTIMADE‑style input structures, we execute an identical OCV protocol across multiple workflow managers and DFT engines (VASP via PerQueue and SimStack, GPAW, Quantum ESPRESSO, and CASTEP). Engine‑specific archives provide standardized output JSON files for cross‑comparison, and, where available, native files to enable full reproduction.

We focus on five representative cathode chemistries (Li2Mn3NiO8, LiCoO2, LiTiS2, LiFePO4, and MgMo3S4), chosen to probe diverse structural and electronic regimes. The dataset documents strong cross‑engine agreement for average OCVs derived from pristine charged/discharged cells, and highlights where alignment is challenging, notably for vacancy‑containing supercells at low/high states of charge due to sensitivity to smearing, symmetry breaking, and optimization procedures. By releasing harmonized inputs, standardized outputs, and semantic metadata, this work provides a practical resource for benchmarking interoperable DFT workflows, facilitates transparent comparison across codes, and supports reproducible studies and future extensions (e.g., spin polarization and Hubbard corrections) in high‑throughput materials discovery.

en: OCV intercalation workflow implemented across DFT and workflow engines

envi

en: Environment

battery

display_name: battery

big-map

display_name: big-map

ocv

display_name: ocv

workflows

display_name: workflows


en: This record contains the two-dimensional substructures identified using a novel  approach based on the crystal graph construction via Voronoi partition, interaction strength estimation via bond valence theory and the iterative removal of weak links, strating from experimentally known bulk compounds. We also report the results of DFT calculations on a subset of newly identified easily exfoliable and robust two-dimensional materials.

en: Search for low-periodic substructures in crystalline solids: a novel approach

electronic-bands

display_name: electronic-bands

high-throughput

display_name: high-throughput


en: Mechanical properties of ultrathin coatings can deviate from bulk values due to growth-stage and interface effects. Elastic moduli of bulk amorphous alumina were calculated using a near-ab initio-accurate neural-network interatomic potential to provide a modeling benchmark for hydroxylated alumina coatings deposited by atomic layer deposition at 50 °C. Experimentally, elastic moduli were evaluated by both spherical nanoindentation and laser-induced surface acoustic wave spectroscopy (LiSAWS). For coatings thicker than 7 nm, both techniques indicated a weakly decreasing modulus from ~137 GPa to ~126 GPa (nanoindentation) and ~116 GPa to ~108 GPa (LiSAWS). These values are consistent with the simulated Young’s modulus.

en: Modulus and yield strength determination at ultra-thin atomic layer deposited hydroxylated alumina coatings using spherical nanoindentation and surface acoustic waves

atomic-layer-deposition-ald

display_name: atomic-layer-deposition-ald

foundation-model

display_name: foundation-model

hydroxylated-alumina

display_name: hydroxylated-alumina

molecular-dynamics

display_name: molecular-dynamics

spherical-nanoindentation

display_name: spherical-nanoindentation

ultrathin-coatings

display_name: ultrathin-coatings

yield-strength

display_name: yield-strength

youngs-modulus

display_name: youngs-modulus


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

en: Electronic structure and dynamical correlations in antiferromagnetic BiFeO3

density-functional-theory

display_name: density-functional-theory

dynamical-correlations

display_name: dynamical-correlations

electronic-structure

display_name: electronic-structure


en: Combinatorial and guided screening of materials space with density-functional theory and related approaches has provided a wealth of hypothetical inorganic materials, which are increasingly tabulated in open databases. The OPTIMADE API is a standardised format for representing crystal structures, their measured and computed properties, and the methods for querying and filtering them from remote resources. Currently, the OPTIMADE federation spans over 20 data providers, rendering over 30 million structures accessible in this way, many of which are novel and have only recently been suggested by machine learning-based approaches. In this work, we outline our approach to non-exhaustively screen this dynamic trove of structures for the next-generation of optical materials. By applying MODNet, a neural network-based model for property prediction that has been shown to perform especially well for small materials datasets, within a combined active learning and high-throughput computation framework, we isolate particular structures and chemistries that should be most fruitful for further theoretical calculations and for experimental study as high-refractive-index materials. By making explicit use of automated calculations, federated dataset curation and machine learning, and by releasing these publicly, the workflows presented here can be periodically re-assessed as new databases implement OPTIMADE, and new hypothetical materials are suggested.

en: Optical materials discovery and design with federated databases and machine learning

modnet

display_name: modnet

optical-materials

display_name: optical-materials

optimade

display_name: optimade

refractive-index

display_name: refractive-index


en: The strongly constrained and appropriately normed (SCAN) meta-GGA functional is a milestone achievement of electronic structure theory. Recently, a revised and restored form (r²SCAN) has been suggested as a replacement for SCAN in high-throughput applications. Here, we assess the accuracy and reliability of the r²SCAN meta-GGA functional for the group IV elemental solids carbon (C), silicon (Si), germanium (Ge), and tin (Sn). We show that the r²SCAN functional agrees closely with its parent functional SCAN for elastic constants, bulk moduli, and phonon dispersions, but the numerical stability of r²SCAN is superior. Both meta-GGA functionals outperform standard GGA (Perdew-Burke-Ernzerhof) in terms of accuracy and approach the level of common hybrid functionals (Heyd-Scuseria-Ernzerhof). However, we find that r²SCAN performs much worse than SCAN for the α ↔ β phase transition of both Ge and Sn, yielding larger phase energy differences and transition pressures.Here we make available the raw phonon dispersion data and VASP input files for an example phonon calculation.

en: Lattice dynamics and structural phase stability of group IV elemental solids with the r²SCAN functional

dft

display_name: dft

phonons

display_name: phonons

vasp

display_name: vasp


en: Electron-phonon interactions in a solid are crucial for understanding many interesting material properties, such as transport properties and the temperature dependence of the electronic band gap. For harmonic materials, the linear interaction process where one electron interacts with one phonon is sufficient to quantitatively describe these properties. However, this is no longer true in anharmonic materials with significant electron-phonon interaction, such as halide perovskites. Currently, the only available models for nonlinear electron-phonon interaction are model Hamiltonians, written in terms of phenomenological parameters. Here, we provide a postprocessing Python code for the treatment of the long-range part of the 1-electron-2-phonon interaction. We show in the associated manuscripts that it is sufficient to know the phonon frequencies and eigenvectors calculated at a finite electric field, which we provide alongside this software for three selected materials: LiF, KTaO3, and CsPbI3. We calculate the 1-electron-2-phonon spectral functions for these materials, and use the one for CsPbI3 to calculate the contribution of the 1-electron-2-phonon interaction to the electron mobility. We show that the nonlinear interaction contributes significantly to the temperature scaling of the mobility in CsPbI3, due to its low phonon frequencies. For LiF, a very harmonic material with high phonon frequencies, the effect is negligible as expected. In KTaO3 the effect of nonlinear interaction is also negligible, despite the fact that it is an anharmonic material: this is due to the fact that it also has high phonon frequencies.

en: Importance of nonlinear long-range electron-phonon interaction on the carrier mobility of anharmonic halide perovskites

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).

anharmonicity

display_name: anharmonicity

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

transport

display_name: transport


en: Physics-inspired machine learning (ML) models can be categorized into two classes: those relying solely on three-dimensional structure and those incorporating electronic information. In this work, we benchmark both classes for predicting quantum-chemical properties of transition metal complexes with diverse charge and spin states, using three complementary datasets. The evaluated methods include molecular representations (SLATM, FCHL, SOAP, and SPAHM family) combined with kernel ridge regression, as well as geometric deep learning models (MACE and 3DMol). We examine how the inclusion of electronic information affects predictive accuracy across datasets and target properties. Models that incorporate electronic information consistently outperform purely structure-based models for properties whose distributions are strongly governed by electronic characters, such as spin-splitting energies and frontier orbital energies. In contrast, structure-only models perform well for predicting the HOMO–LUMO gap and dipole moment magnitude, whose distributions are relatively insensitive to electronic characteristics. Geometric deep learning models with charge and spin embeddings (MACE-QS and 3DMol-QS) show the highest overall accuracy, with 3DMol offering the best computational efficiency among the tested models. These results clarify when geometric information is sufficient and when incorporating electronic information becomes essential, providing practical guidance for selecting effective physics-based ML models for transition metal complexes.

en: Benchmarking physics-inspired machine learning models for transition metal complexes with diverse charge and spin states

materialscloud

en: Materials Cloud

benchmark

display_name: benchmark

geometric-deep-learning

display_name: geometric-deep-learning

machine-learning

display_name: machine-learning

representations

display_name: representations

transition-metal-complex

display_name: transition-metal-complex


en:
This archive provides the AiiDA workflows, metadata, and computational datasets associated with the study of vibrational frequencies and Stark tuning rates at electrochemical interfaces within the framework of grand-canonical density functional theory (GC-DFT) combined with continuum electrochemical models.

In this work, the electrochemical potential is treated as a degree of freedom by adjusting the number of electrons in the Kohn-Sham equations, enabling simulations at fixed potential. Based on the relationship between free energy and grand potential, physical properties such as atomic forces, vibrational frequencies, and Stark tuning rates are consistently evaluated. While atomic forces are found to be equivalent to Hellmann-Feynman forces in the canonical ensemble, vibrational frequencies and Stark tuning rates exhibit distinct behavior under grand-canonical conditions.

The archive contains input parameters, workflows, and computed results obtained using finite-displacement methods for the evaluation of vibrational properties and Stark tuning rates. These data enable the reproduction of the results reported in the associated publication and provide a reference for future studies of electrochemical interfaces under constant potential conditions.

en: Vibrational frequencies and stark tuning rate with continuum electro-chemical models and grand canonical density functional theory

envi

en: Environment

grand-canonical-dft

display_name: grand-canonical-dft

stark-tuning-rate

display_name: stark-tuning-rate

vibrational-frequencies

display_name: vibrational-frequencies


en: This dataset presents the results of a comprehensive crystal structure search for ternary compounds in the Lu–N–H system. Thousands of candidate structures were evaluated, and their thermodynamic stability was assessed through the construction of the convex hull of formation enthalpies. To ensure reliable energetic ordering, all calculations were performed using the highly accurate r2SCAN density functional in an all-electron framework, avoiding possible pseudopotential-related inaccuracies.  A previously unreported lutetium hydride phase (HLu₂) is identified on the convex hull, indicating thermodynamic stability. However, subsequent electron–phonon coupling analysis shows that this structure is not a promising candidate for near-ambient superconductivity. Apart from this additional phase, the results are in close agreement with earlier structure prediction studies, demonstrating strong reproducibility across independent computational approaches. This consistency highlights the maturity and reliability of modern crystal structure prediction methods. Overall, the dataset provides further evidence that no thermodynamically stable structure within the Lu–N–H system is likely to exhibit conventional high-temperature superconductivity under near-ambient conditions, as would be required to explain previously reported experimental claims.

en: Ternary phase diagram of nitrogen doped lutetium hydrides

minimahopping

display_name: minimahopping

superconductor

display_name: superconductor


en: We analyze the effects on the structural and electronic properties of vanadium dioxide (VO₂) of adding an empirical inter-atomic potential within the density-functional theory+V (DFT+V) framework. We use the DFT+V machinery founded on the extended Hubbard model to apply an empirical self-energy correction between nearest-neighbor vanadium atoms in both rutile and monoclinic phases, and for a set of structures interpolating between these two cases. We observe that imposing an explicit intersite interaction V along the vanadium-vanadium chains enhances the characteristic bonding-antibonding splitting of the relevant bands in the monoclinic phase, thus favoring electronic dimerization and the formation of a band gap. We then explore the effect of V on the structural properties and the relative energies of the two phases, finding an insulating global energy minimum for the monoclinic phase, consistent with experimental observations. With increasing V, this minimum becomes deeper relative to the rutile structure, and the transition from the metallic to the insulating state becomes sharper. We also analyze the effect of applying the +V correction either to all or only to selected vanadium-vanadium pairs, and both in the monoclinic as well as in the metallic rutile phase. Our results suggest that  DFT+V can indeed serve as a computationally inexpensive unbiased way of modeling VO₂ which is well suited for studies that, e.g., require large system sizes.

en: Incorporating static intersite correlation effects in vanadium dioxide through DFT+V

dft

display_name: dft

dftuv

display_name: dftuv


en: Deep Generative models have shown impressive capabilities in several applications, e.g., image, video, and audio synthesis. Importantly, they can infer probability distributions from data implicitly. For this reason, they have promising applications in learning stochastic dynamics, with potential applications in computational condensed matter physics and materials science. Indeed, random thermal fluctuations have a key role in several phenomena, such as surface roughening, nucleation, crystal growth, and phase transitions. Here we provide a comprehensive dataset of trajectories of one of such systems, i.e., the progressive roughening of monoatomic steps on top of a simple cubic (100) surface. The system has been simulated with a Kinetic Monte Carlo approach, implementing the edge diffusion conditions. Among other characteristics, this system is convenient because its dynamics can be represented as a series of binary images, with white and black pixels corresponding to occupied and unoccupied surface sites, respectively. The repository contains 3 KMC datasets composed of 300 independent trajectories each. Every trajectory contains 1000 subsequent states. The datasets differ in the initial condition (one flat stripe and one undulated one on a 64x64 computational cell) and the domain size (one undulated stripe on a 72x64 computational cell). Evolutions predicted by a Generative Adversarial Network approach trained on the flat stripe dataset are also provided, serving as a benchmark for comparing alternative implementations on the same problem. These comprise the evolution of flat profiles on domains of different lengths (32, 48, 64, 72, 80, 96, and 128) to the equilibrium value and relaxation dynamics from undulated profiles conformal to those used in the KMC datasets.

en: A kinetic Monte Carlo dataset of monoatomic step roughening for deep generative models approximation of stochastic dynamics

deep-learning

display_name: deep-learning

edge-diffusion

display_name: edge-diffusion

generative-adversarial-network

display_name: generative-adversarial-network

kinetic-monte-carlo

display_name: kinetic-monte-carlo

neural-networks

display_name: neural-networks

roughness

display_name: roughness

stochastic-dynamics

display_name: stochastic-dynamics

surface-science

display_name: surface-science


en:
Machine Learning (ML) can be conveniently applied to continuum materials simulations, allowing for the investigation of larger systems and longer timescales, pushing the limits of tractable systems. Here we provide a comprehensive dataset of strained Ge films on Si and their corresponding strain states, which can be used to train a ML model capable of such acceleration. Approximately 80k 2D cases are included, reporting the profiles h(x) and the corresponding elastic energy densities and strain fields. The profiles are conveniently sampled using Perlin-noise and pure-sine waves. A 100nm-large computational domain is considered. The mechanical equilibrium problem is solved using Finite Element Method (FEM). Ge is modeled as an isotropic material and an eigenstrain of 3.99% is used, as in Ge/Si(001).
The database has been exploited for training a (fully) Convolutional Neural Network (CNN) which maps the free surface profile h(x) to the corresponding energy density. If plugged into the proper time-dependent Partial Differential Equation, this term can be used to accelerate continuum simulations of the morphological evolution of strained films while retaining FEM-level accuracy. Tests of the reliability of such CNN model are also provided in the repository, together with the output of surface morphology minimization procedures and morphological evolution simulations during coarsening and growth. In the latter, evolution by surface diffusion has been considered as an important case, but applications to other mechanisms are possible. Generalization examples to larger computational cells with respect to those in the dataset are also available.

en: A FEM dataset of Ge film profiles and elastic energies for machine learning approximation of strain state and morphological evolution

continuum-models

display_name: continuum-models

convolutional-neural-network

display_name: convolutional-neural-network

elastic-energy

display_name: elastic-energy

finite-element-method

display_name: finite-element-method

germanium

display_name: germanium

machine-learning

display_name: machine-learning

strained-films

display_name: strained-films

surface-evolution

display_name: surface-evolution

thin-films

display_name: thin-films


en: Crafting neural-network interatomic potentials (NNIPs) remains a complex task, demanding specialized expertise in both machine learning and electronic-structure calculations. Here, we introduce AiiDA-TrainsPot, an automated, open-source, and user-friendly workflow that streamlines the creation of accurate NNIPs by orchestrating density-functional-theory calculations, data augmentation strategies, and classical molecular dynamics.Our active-learning strategy leverages on-the-fly calibration of committee disagreement against ab initio reference errors to ensure reliable uncertainty estimates. We use electronic-structure descriptors and dimensionality reduction to analyze the efficiency of this calibrated criterion, and show that it minimizes both false positives and false negatives when deciding what to compute from first principles. AiiDA-TrainsPot has a modular design that supports multiple NNIP backends, enabling both the training of NNIPs from scratch and the fine-tuning of foundation models. We demonstrate its capabilities through automated training campaigns targeting pristine and defective carbon allotropes, including amorphous carbon, as well as structural phase transitions in monolayer WxMo1-xTe2 alloys.

en: AiiDA-TrainsPot: Towards automated training of neural-network interatomic potentials

active-learning

display_name: active-learning

aiida

display_name: aiida

aiida-trainspot

display_name: aiida-trainspot

carbon-allotropes

display_name: carbon-allotropes

mace

display_name: mace

neural-network-interatomic-potentials

display_name: neural-network-interatomic-potentials

uncertainty-quantification

display_name: uncertainty-quantification


en: We present a combined density-functional theory and dynamical mean-field theory (DFT+DMFT) study of the full structural phase space of rutile-based vanadium dioxide (VO2), including also the less studied M2 and T phases, using an unconventional bond-centered orbital basis. The use of bond-centered orbitals allows us to treat all main phases of VO2, and the structural transitions between them, using one consistent approach with moderate computational cost and without pre-pattering of the structure into dimerized and undimerized V--V pairs. We obtain two distinct insulating states on the two different types of vanadium chains in the M2 phase, a singlet-insulator on the dimerized chains and a Mott-insulator on the zigzag-distorted chains, which, however, are strongly coupled in the M2 phase and thus the metal-insulator transition always occurs concomitantly for both types of sites. We also demonstrate that the M2 phase corresponds to a local energy minimum in the structural phase space of VO2, the stability of which, apart from the internal structural distortion, depends crucially on the unit cell strain relative to the undistorted rutile phase. Our calculations further indicate that the symmetry-distinct triclinic T phase corresponds electronically to either an M1 or an M2-type insulator with an abrupt transition as a function of distortion. Finally, we disentangle the effect of the dimerization and zigzag distortions by constructing hypothetical structures that contain only one site type, finding that the zigzag distortion strongly favors emergence of the Mott-insulating state, both as function of distortion and on-site interaction.

en: Towards a unified first-principles-based description of VO2 using DFT+DMFT with bond-centered orbitals

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).

dft

display_name: dft

dftdmft

display_name: dftdmft

mit

display_name: mit


en:
This is a summary of previous work, presenting a simulation dataset for the interactions between functionalized/unfunctionalized carbon nanomaterials and PVA. The primary objective is to investigate flow-assisted orientation and surface properties. Unreliable LAMMPS stress-strain simulations were excluded, and the rationality of the pull-out, nanoscratch, and cooling simulations was validated.

The synergistic reinforcement of polyvinyl alcohol (PVA) fibers using carbon nanomaterials is heavily constrained by interfacial compatibility and dynamic spatial orientation during the wet-spinning process. In this study, confined-shear molecular dynamics (MD) simulations were employed to systematically investigate the interfacial mechanics, structural evolution, and shear-induced orientation of PVA composites reinforced with one-dimensional (SWNT) and two-dimensional (graphene/graphene oxide) carbon nanofillers. The results reveal that surface functionalization (-COOH, -OH) significantly enhances interfacial adhesion via dense hydrogen bond networks, shifting the failure mechanism to highly dissipative plastic deformation and improving wear resistance. Interestingly, under static equilibrium, the functionalized 1D/2D hybrid system (GO+SWNT-COOH/PVA) exhibits an anomalous antagonistic effect in mechanical moduli due to spatial crowding and conformational frustration. However, the application of a dynamic shear flow field effectively overcomes this static steric hindrance, inducing a highly ordered axial alignment of both polymer chains and nanofillers. Consequently, the post-shear hybrid system unlocks its multi-scale synergistic reinforcement potential, achieving a remarkable axial Young's modulus of 8.10 GPa. These atomistic insights elucidate the critical transition from static interfacial chemistry to dynamic macroscopic orientation, providing robust theoretical guidance for the fabrication of high-performance PVA composite fibers.

en: Molecular dynamics insights into the shear-induced orientation and interfacial enhancement in functionalized CNTs and graphene/PVA composite fibers

materialscloud

en: Materials Cloud

carbon-nanotubes

display_name: carbon-nanotubes

graphene

display_name: graphene

md

display_name: md

pva

display_name: pva


en: Vacancy-ordered double perovskites have emerged as lead-free alternatives, offering remarkable stability and compositional tunability for optoelectronic applications. In this study, we provide first-principles insights into their electronic properties, surface stability, and energy level alignment using a non-empirical dielectric-dependent hybrid functional. For representative family of the Cs2MX6, with M = Zr, Sn, Te, and X= Cl, Br, I, the predicted electronic band gaps are comparable to those obtained with the state-of-the-art GW method. We investigate the stability of these materials under simulated experimental conditions, considering both the rich and poor chemical potentials of their precursor salts. Our results indicate distinct regions of surface energy stability that favor CsX terminations. In contrast, MX4 terminations show in-gap surface states, which can act as trap states and reduce carrier lifetime. Finally, based solely on the intrinsic absolute energy levels, we identify promising candidates as charge transport/injection layers for typical photovoltaic and light-emitting applications.

en: Dataset for Energy level alignment of vacancy-ordered halide double perovskites

envi

en: Environment

absolute-energy-levels

display_name: absolute-energy-levels

charge-transport-layer

display_name: charge-transport-layer

density-functional-theory

display_name: density-functional-theory

dielectric-dependent-hybrid-functional

display_name: dielectric-dependent-hybrid-functional

surface

display_name: surface

vacancy-ordered-double-perovskites

display_name: vacancy-ordered-double-perovskites


en: Germanium–silicon–germanium (Ge/SixGe1-x) heterostructures have emerged as a prominent platform for high-mobility electronic devices and hole-spin–based quantum technologies. In this work, we present an ab initio study of strained germanium, demonstrating that biaxial compressive strain in a Ge quantum well strongly alters the valence-band structure. Specifically, the strain lifts the heavy-hole/light-hole degeneracy and modifies the effective masses (significantly enhances hole mobility). Our findings offer a comprehensive theoretical description of the combined effects of strain and quantum confinement on the valence bands of Ge quantum wells, providing a solid foundation for predictive modeling of Ge-based high-mobility electronics and hole-spin qubits.

en: Ab initio study of strain and quantum confinement shaping the valence-band structure of Ge quantum wells

aiida

display_name: aiida

quantum-confinement

display_name: quantum-confinement

quantum-espresso

display_name: quantum-espresso

strained-germanium

display_name: strained-germanium

tight-binding

display_name: tight-binding

wannier-interpolation

display_name: wannier-interpolation


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-siesta

display_name: aiida-siesta

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: Density-functional theory with on-site U and inter-site V Hubbard corrections (DFT+U+V) is widely used to predict properties of transition-metal and rare-earth compounds, but its accuracy depends critically on how these parameters are obtained. While they can be determined empirically, first-principles approaches offer better consistency, though their results can differ and a systematic comparison is lacking. Here, we compare two widely used approaches, linear-response theory (LRT) and the Hartree–Fock-based pseudohybrid functional formalism, applied to representative oxides (MnO, NiO, CoO, FeO, BaTiO3, ZnO, and ZrO2). For partially occupied transition-metal d states, both methods yield comparable U values, but they differ significantly for nearly empty or fully filled d shells. For O-2p states, LRT systematically predicts large U values (~10 eV), whereas the pseudohybrid approach gives system-dependent values. Even larger differences appear for the inter-site V: the former yields small values (<1 eV), while the latter gives larger values (~3 eV) due to its dependence on charge redistribution. We further show that the pseudopotential choice, which defines Hubbard projectors, affects the parameters and their agreement. Overall, while parallels between these two methods exist, they rely on different assumptions, leading to variations in predictions of material properties.

en: Comparing Hubbard parameters from linear-response theory and a Hartree-Fock-based approach

coo

display_name: coo

dftu

display_name: dftu

dftuv

display_name: dftuv

feo

display_name: feo

mno

display_name: mno

nio

display_name: nio

zno

display_name: zno


en:
We establish a set of exact sum rules that relate the interatomic force constants to the frequency-dependent electromagnetic susceptibility of a solid or molecule, thereby generalizing the long-established principles of rototranslational symmetry to the nonadiabatic regime. Crucially, we show that in practical numerical implementations these sum rules are violated, unless special precautions are taken in the treatment of the atomic pseudopotentials. We solve these issues once and for all by correctly adapting the pseudopotential to the motion of the corresponding nucleus, with a velocity dependence of the nonlocal operator. This prescription restores the correct Galilean covariance of the Schr"odinger equation, and the expected identity between mechanical rototranslations and electromagnetic perturbations. These results conclusively fix a number of worrisome inconsistencies that were pointed out over the years in the context of linear-response theory restoring, e.g., the validity of the Larmor theorem, and the equivalence between the inertial and electrical definitions of the Drude weight in metals.

This dataset contains the input and output files used to produce the results presented in the manuscript.

en: Rototranslational sum rules for nuclear dynamics via traveling pseudopotentials

density-functional-theory

display_name: density-functional-theory

nonadiabatic-regime

display_name: nonadiabatic-regime

pseudopotentials

display_name: pseudopotentials


en: We present a density-functional theory study of the effects of oxygen vacancies on the structural and electronic properties of vanadium dioxide (VO2). Our motivation is the reported suppression of the metal-insulator transition by oxygen vacancies and the lack of a clear consensus on its origin. We use the DFT+V method with a static intersite vanadium-vanadium interaction term, V, to calculate the properties of the oxygen-deficient metallic rutile and insulating monoclinic M1 phases of VO2 on the same footing. We find that oxygen vacancies induce local distortions in the M1 phase, but do not destroy the dimerization usually associated with the insulating behavior. In spite of this, we find that the M1 phase becomes metallic as a result of the partial filling of the conduction band due to a rigid-band-like doping effect.

en: Oxygen vacancies in vanadium dioxide: A DFT+V study

dft

display_name: dft

vacancy

display_name: vacancy


en: We present a high-throughput computational screening for fast lithium-ion conductors aimed at identifying candidate materials for application in all-solid-state electrolytes. Beginning with more than 30,000 experimentally reported Li-containing structures drawn from the Inorganic Crystal Structure Database, the Materials Platform for Data Science (Pauling file), and the Crystallography Open Database, we apply a series of automated structural and compositional filters to obtain 1500 unique crystal structures suitable for electronic-structure calculations which yields nearly 1,000 electronic insulators. We then estimate Li-ion diffusivities for these insulating candidates using molecular dynamics simulations at multiple temperatures. To make simulations computationally feasible at this scale while preserving near first-principles fidelity, we employ a foundational machine-learned interatomic potential which is carefully fine-tuned on relevant Li-chemistries. We discuss the details of the fine-tuning strategies and data-consistency considerations required to obtain a very accurate and robust model. From the MD results, we identify three particularly promising novel oxide candidates for room-temperature solid-state electrolytes, including LiSn2(AsO4)3, LiIn(IO3)4, and LiB6S4(Cl3O4)2, which all exhibit ionic conductivity greater than 1 mS/cm at room temperature with diffusion barrier between 0.20 and 0.25 eV. We provide the full screening protocol as well as a prioritised list of materials for experimental follow-up, demonstrating the value of provenance-aware, ML-driven simulations in accelerating solid-state electrolyte discovery.

en: High-throughput screening for solid-state Li-ion conductors combining machine learning and 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).

first-principles

display_name: first-principles

high-throughput

display_name: high-throughput

li-ion-conductors

display_name: li-ion-conductors

molecular-dynamics

display_name: molecular-dynamics

solid-state-electrolytes

display_name: solid-state-electrolytes


en: Materials exhibiting coexisting and coupled ferroelectric and magnetic orders are rare. Here we show, using density functional theory calculations, that introducing FeLa antisite defects into pristine, non-ferroelectric, G-type antiferromagnetic (AFM) LaFeO3 renders the material simultaneously ferroelectric and a defect-induced local ferrimagnetic phase. We assess the thermodynamic stability of FeLa antisites and identify synthesis conditions under which they are likely to form. Nudged elastic band calculations indicate an accessible barrier for ferroelectric polarization switching. Interestingly, we observe an inverse magnetoelectric coupling in which the ferroelectric polarization controls the magnetic order. We further propose two defect-mediated magnetization-control mechanisms enabled by ferroelectric switching in LaFeO3, both involving a 180° reversal: (i) switching the FeLa antisite moments while the pristine G-type AFM order remains unchanged, and (ii) of the pristine G-type AFM order while the FeLa antisite moments are pinned by a static magnetic field. In both cases, exchange coupling between the antisite and pristine Fe sublattices enables magnetization switching, highlighting a defect-engineering route to electrically controlled magnetic states for data storage.

en: Multiferroicity and 180° magnetization switching in LaFeO3 via antisite defects

materialscloud

en: Materials Cloud

antisite-defect

display_name: antisite-defect

multiferroic

display_name: multiferroic


en: Sodium (Na) metal batteries are considered promising solutions for next-generation electrochemical energy storage because of their low costs and high energy densities. However, the slow Na dynamics result in unfavourable Na deposition and dendrite growth, which compromise cycling performance. Here we propose a nanotwinned alloy strategy prepared by high-pressure solid solution followed by Joule-heating treatment to address sluggish Na dynamics, achieving homogeneous Na deposition. By employing cost-effective Al-Si alloys for validation, Si solubility of 10 wt.% is extended through a high-pressure solid solution, and nanotwinned-Si particles, with a volume fraction of 82.7%, are subsequently formed through Joule-heating treatment. The sodiophilic nanotwinned-Si sites exhibit a high diffusion rate, which reduces the nondimensional electrochemical Damköhler number to far below 1, shifting the diffusion-controlled deposition behavior to reaction-controlled deposition. This transition facilitates spherical Na deposition and dendrite-free growth, allowing a symmetric cell to achieve stable Na plating/stripping over 5300 hours at 5 mA cm⁻² with a cumulative capacity of 13.25 Ah cm⁻². This strategy is also demonstrated in another CuAg system with nanotwinned Ag structures.The record contains data that support the findings described in the publication.

en: A nanotwinned-alloy strategy enables fast sodium deposition dynamics

envi

en: Environment

dynamics

display_name: dynamics

nanotwinned-alloy

display_name: nanotwinned-alloy

sodium-metal-battery

display_name: sodium-metal-battery


en:
This dataset contains the minimal set of AiiDA calculations supporting the results presented in the paper "Multiband Anisotropic Superconductivity in 2H-NbSe₂ Revealed by Ab-Initio Kohn--Sham Bogoliubov--de Gennes Calculations".

It includes the fully relativistic Kohn--Sham Bogoliubov--de Gennes (KS-BdG) calculations for pristine bulk 2H-NbSe₂ performed with the JuKKR code and the AiiDA-KKR plugin. The calculations use a single, physically transparent pairing strength λ_Nb applied only to the Nb sites and quantitatively reproduce the experimentally observed multigap density of states with coherence peaks at 1.85, 1.55, and 1.4 meV together with the ~30% momentum-dependent superconducting gap anisotropy. The archive provides the normal-state electronic structure and the self-consistent superconducting state, including all input parameters required for full reproducibility.

en: Multiband anisotropic superconductivity in 2H-NbSe2 revealed by ab-initio Kohn--Sham Bogoliubov--de Gennes calculations

aiida

display_name: aiida

aiida-kkr-ksbdg

display_name: aiida-kkr-ksbdg

bogoliubov-de-gennes

display_name: bogoliubov-de-gennes

superconductivity

display_name: superconductivity


en: This record contain data to support the result we published in the work "Collective All‐Carbon Magnetism in Triangulene Dimers". Triangular zigzag nanographenes, such as triangulene and its π‐extended homologues, have received widespread attention as organic nanomagnets for molecular spintronics, and may serve as building blocks for high‐spin networks with long‐range magnetic order, which are of immense fundamental and technological relevance. In the publication we present the on‐surface synthesis and a proof‐of‐principle experimental study of magnetism in covalently bonded triangulene dimers. On‐surface reactions of rationally designed precursor molecules on Au(111) lead to the selective formation of triangulene dimers in which the triangulene units are either directly connected through their minority sublattice atoms, or are separated via a 1,4‐phenylene spacer.

en: Collective all‐carbon magnetism in triangulene dimers

erc

display_name: erc

magnetic-carbon

display_name: magnetic-carbon

nanographenes

display_name: nanographenes

snsf

display_name: snsf

spin-chains

display_name: spin-chains


en: We develop an extensive dataset of Allen-Cahn simulations of crystal growth in 2D, including kinetic anisotropy to produce hexagonally faceted profiles, and a variable supersaturation parameter to control the growth rates and local faceting. A phase-field numerical approach based on a finite-difference explicit time integration scheme is used to generate such evolution sequences. Two Convolutional Recurrent Neural Network architectures are then exploited to train quantitative surrogate models reproducing such evolution sequences conditioned to the supersaturation value. The first infers the supersaturation parameter implicitly from an input mini-sequence of a few evolution frames while the second takes it as an explicit input along with a single initial frame. The predictive performances of the two models are evaluated by extensive testing on dedicated testsets. In particular, the analysis includes both the testing on a large dataset of the same kind of the training one, and on dedicated datasets with assigned values of the supersaturation parameter. While the architecture using the parameter as explicit input yields the best performance, with quantitative reproduction of the true phase-field solutions, the mini-sequence model produce satisfactory results only by taking comparatively larger dataset sizes. The generalization capabilities of the trained models are finally inspected by considering larger domains, longer evolution times and different initial coverages.

en: Neural surrogates for crystal growth dynamics with variable supersaturation: explicit vs. implicit conditioning

allen-cahn-equation

display_name: allen-cahn-equation

convolutional-recurrent-neural-network

display_name: convolutional-recurrent-neural-network

crystal-growth

display_name: crystal-growth

surrogate-model

display_name: surrogate-model


en: In rarefied atmospheric environments, where continuum fluid dynamics breaks down, aerodynamic drag is governed by gas–surface momentum exchange, making surface structure and chemistry key design knobs. Using molecular dynamics simulations, we show that coating the α-Al2O3(0001) surface with graphene markedly reduces the tangential momentum accommodation coefficient (TMAC) of N2, shifting scattering toward more specular reflection and thereby lowering drag; we further benchmark this response against graphite. The reduction is robust and strengthens up to 900 K. While structural defects can increase TMAC – and hence drag – via defect-induced corrugation and local atomic and electronic rearrangements, graphene retains near-ideal low-TMAC performance at experimentally relevant defect densities. These results identify graphene as a thermally resilient, low-drag coating for aerospace-relevant surfaces in rarefied flows.

en: Defects, corrugation and temperature govern rarefied-air drag on graphene coatings

accommodation-coefficients

display_name: accommodation-coefficients

air

display_name: air

alumina

display_name: alumina

coating

display_name: coating

defects

display_name: defects

drag

display_name: drag

graphene

display_name: graphene

graphite

display_name: graphite

molecular-dynamics

display_name: molecular-dynamics

rarefied-gas

display_name: rarefied-gas


en: The dataset provides records of creep properties for structural materials used in nuclear applications, focusing on the influence of specimen dimensions on time-dependent deformation behavior. It was developed through an extensive literature search of peer-reviewed articles, technical reports, and institutional databases, including contributions from 50 publications and databases. The search targeted studies presenting quantitative creep data for both standard and sub-sized specimens, with detailed information on testing conditions, specimen geometries, and material compositions. The compiled database contains 1,413 creep testing records covering parameters that include alloy type and grade, chemical composition, thermal and mechanical treatment history, irradiation conditions, specimen geometry and dimensions, and measured creep properties. The dataset encompasses material classes such as stainless steels (A709, SS304, SS316), reactor pressure vessel steels (SA533, SA508), nickel-based alloys (IN600, IN617), and zirconium alloys (Zr-2, Zr-3, Zr-4). Experts in materials science reviewed and validated all records, verifying material specifications, treatment conditions, and testing parameters for consistency and accuracy.

en: Creep dataset from peer-reviewed literature of nuclear structural materials

creep-test

display_name: creep-test

nuclear-structural-materials

display_name: nuclear-structural-materials

size-effect

display_name: size-effect

sub-sized-specimens

display_name: sub-sized-specimens


en: Cs2KInI6 is a promising lead-free halide double perovskite with a calculated direct band gap of 1.94 eV, ideal for solar cell applications. Our first-principles calculations reveal that its cubic phase (Fm3̅m) is dynamically unstable. Using an accelerated machine learning approach, we identify 42 dynamically stable structures and further validate these findings using first-principles calculations on 11 of these. The most stable phase has Cmc21 symmetry with 20 atoms/unit cell. It lies 13 meV/atom above the convex hull but lacks octahedral cation coordination. The most stable perovskite-like structure has P3̅ symmetry with 10 atoms/unit cell and low octahedral connectivity. Structure-property trade-offs are highlighted, with calculated distortions generally widening the band gap, shifting it from direct to indirect, and flattening the band edges. This work showcases the synergy of genetic algorithms, machine-learned potentials, and first principles validation for discovering stable, complex materials. This archive contains all our input, output files, the data and scripts.

en: From symmetry to stability: structural and electronic transformation in Cs₂KInI₆

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).

first-principles

display_name: first-principles

halide-double-perovskite

display_name: halide-double-perovskite

machine-learned-interatomic-potentials

display_name: machine-learned-interatomic-potentials

phonons

display_name: phonons

polymorphs

display_name: polymorphs


en: Materials underpin modern technologies, from energy harvesting, storage, and conversion to information and communication technologies. Their functionality is often governed by the interplay between competing phases, as thermodynamic behavior shapes microscopic properties and ultimately determines technological performance; for instance, the light absorption of inorganic metal-halide perovskites in solar cells. Accurately predicting crystal thermodynamics, however, remains a major challenge for computational approaches because strong anharmonic effects require extensive sampling of the potential energy surface. Here, we present an on-the-fly Bayesian framework, combined with the stochastic self-consistent harmonic approximation, for learning first-principles interatomic potentials. This approach enables the prediction of thermodynamic properties over a broad temperature range with first-principles accuracy while requiring training on only a few tens to a few hundreds of atomic configurations. To demonstrate its power, we investigate the thermodynamic and dynamical properties of Li2O, α-CsPbI3, and δ-CsPbI3, requiring only 44, 256, and 50 total-energy calculations, respectively. Notably, we show that this framework accurately captures the phase diagram of CsPbI3, which explains its spontaneous degradation into the non-absorbing yellow phase, predicting the transition temperature with remarkable accuracy and efficiency. More broadly, the method presented opens a novel route toward accelerated materials engineering under realistic conditions for a wide range of technologically relevant applications, including solid-state batteries, optoelectronic devices, and memristors.

en: Predicting challenging phase transitions with Bayesian active learning

materialscloud

en: Materials Cloud

active-learning

display_name: active-learning

anharmonicity

display_name: anharmonicity

machine-learning

display_name: machine-learning

phase-transitions

display_name: phase-transitions

thermodynamics

display_name: thermodynamics


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

envi

en: Environment

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