en:
Experiments involving resonant optical excitation of infrared-active phonons in crystals have emerged as a powerful new way to tune materials properties. A puzzling and so far unexplained aspect of some so-called nonlinear phononics experiments is that the observed lifetimes of the optically created metastable phases are sometimes orders of magnitude longer than expected based on the nonlinear phononics mechanism assumed in most works. We use a combination of phenomenological theory and first-principles calculations to demonstrate that strong coupling between different lattice degrees of freedom (strains and Raman-active phonons) can give rise to a long-lived metastable phase recently observed in experiments on perovskite LaAlO3 [Hortensius et al. npj Quantum Mater. 5 95 (2020)]. We show that the long-timescale oscillatory response in the experimental optical reflectivity data is not due solely to shear strains, as originally suggested, but arises from a “hybrid” mode involving displacements of Raman-active phonons of the same symmetry. Our work suggests that strong coupling between different order parameters can provide a mechanism for long-lived optically created metastable phases and points towards strategies, such as strain engineering, for modifying or increasing the lifetime of light-induced phases in ultrafast optical experiments.

This archive contains the data files used to produce Figures 2-4 of the described paper.

en: Understanding long-lived metastable phases in ultrafast optical experiments

complex-oxides

display_name: complex-oxides


$oid: OBSOLETE

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

first-principles

display_name: first-principles


en: Experiments reveal negative (non-Laplacian) surface stresses in metal oxide nanoparticles, partly associated with humidity during fabrication and annealing. Using a neural network interatomic potential for MgO, we prove that water adsorption induces surface hydroxylation, shifting facets from {100} to {110} to {111} and switching the average surface stress from positive to negative. Predicted lattice strains versus nanoparticle size agree well with experiments, clarifying experimental correlations. The new framework informs broad applications in catalysis, sensors, batteries, and biomedicine.

en: Hydroxylation-driven surface reconstruction at the origin of compressive-to-tensile stress transition in metal oxide nanoparticles

nanoparticles

display_name: nanoparticles

surface-reconstruction

display_name: surface-reconstruction

surface-stress

display_name: surface-stress

xrd

display_name: xrd


en: Ultrathin nanowires could play a central role in next-generation downscaled electronics. Here, we explore some of the most promising candidates identified from previous high-throughput screening: CuC2, TaSe3, and AuSe2, to gain insight into the thermodynamic and anharmonic behaviors of nanowires that could be exfoliated from weakly-bonded three-dimensional materials. We analyze thermal stability, linear thermal expansion, and anharmonic heat capacity using the stochastic self-consistent harmonic approximation. Notably, our work unveils exotic features common among all the 1D wires: a colossal record negative thermal expansion and very large deviations from the Dulong-Petit law due to strong anharmonicity.

en: Extreme anharmonicity and thermal contraction of 1D wires

anharmonicity

display_name: anharmonicity

density-functional-theory

display_name: density-functional-theory

heat-capacity

display_name: heat-capacity

negative-thermal-expansion

display_name: negative-thermal-expansion

one-dimensional-materials

display_name: one-dimensional-materials

sscha

display_name: sscha


en: The confining walls made by 2D materials are often considered as solid boundary conditions in studies of fluid transport through nanochannels, while the atomically thin walls inherently exhibit thermal fluctuations at finite temperature. In this work, we investigate the solid-liquid interfacial friction properties of water confined within flexible nanochannels using machine-learning-potential molecular dynamics. Surprisingly, we find that the friction coefficient (λ) increases with lateral size L in the flexible nanochannels, following a linear relationship with 1/L, which is absent in rigid channels. For thicker nanochannel walls with large bending stiffness (D), this size dependence weakens. This 1/L scaling resembles the size-dependent thermal fluctuation amplitude of 2D sheets, suggesting that λ can be well controlled by applying the mechanical strain to the channel walls via controlling the phonon modes associated with surface fluctuations. Further analysis reveals that the λ can be decomposed into the contribution from lattice roughness and the thermal-fluctuation-induced ripple of 2D surface, where the second term scales with D−1/2. These findings offer insights into the manipulation of nanoscale flow through precise control of local curvature and fluid-solid coupling.

en: Machine-learning potential molecular dynamics reveals the critical role of flexibility in solid-liquid nanofluidic friction

flexibility

display_name: flexibility

liquidsolid-friction

display_name: liquidsolid-friction

machine-learning-potential

display_name: machine-learning-potential


en: Quantum confinement endows two-dimensional (2D) layered materials with exceptional physics and novel properties compared to their bulk counterparts. Although certain two- and few-layer configurations of graphene have been realized and studied, a systematic investigation of the properties of arbitrarily layered graphene assemblies is still lacking. We introduce theoretical concepts and methods for the processing of materials information, and as a case study, apply them to investigate the electronic structure of multi-layer graphene-based assemblies in a high-throughput fashion. We provide a critical discussion of patterns and trends in tight binding band structures and we identify specific layered assemblies using low-dispersion electronic bands as indicators of potentially interesting physics like strongly correlated behavior. A combination of data-driven models for visualization and prediction is used to intelligently explore the materials space. This work more generally aims to increase confidence in the combined use of physics-based and data-driven modeling for the systematic refinement of knowledge about 2D layered materials, with implications for the development of novel quantum devices.

en: Electronic structure calculations of twisted multi-layer graphene superlattices

graphene

display_name: graphene

high-throughput-calculations

display_name: high-throughput-calculations

machine-learning

display_name: machine-learning

quantum-devices

display_name: quantum-devices

tight-binding-band-structures

display_name: tight-binding-band-structures

twistronics

display_name: twistronics


en:
Density functional theory (DFT) is a widely used method to compute properties of materials, which are often collected in databases and serve as valuable starting points for further studies. In this article, we present the Materials Cloud Three-Dimensional Structure Database (MC3D), an online database of computed 3D inorganic crystal structures. Close to a million experimentally reported structures were imported from the COD, ICSD and MPDS databases; these were parsed and filtered to yield a collection of 72589 unique and stoichiometric structures, of which 95% are, to date, classified as experimentally known. The geometries of structures with up to 64 atoms were then optimized using DFT with automated workflows and curated input protocols. The procedure was repeated for different functionals (and computational protocols), with the latest version (MC3D PBEsol-v2) comprising 32013 unique structures. All versions of the MC3D are made available on the Materials Cloud portal, which provides a graphical interface to explore and download the data. The database includes the full provenance graph of all the calculations driven by the automated workflows, thus establishing full reproducibility of the results and more-than-FAIR procedures.

This data entry includes the data with full provenance for all three versions: PBE-v1, PBEsol-v1 and PBEsol-v2.

en: MC3D: The Materials Cloud computational database of experimentally known stoichiometric inorganics

aiida

display_name: aiida

database

display_name: database

marvelhpc

display_name: marvelhpc

max

display_name: max

pasc

display_name: pasc

prace

display_name: prace


density-functional-theory

display_name: density-functional-theory


en: ZnO nanoparticle powders are an important and common starting material for functional devices that require the highest purity for electronic and optical applications. In this combined ab initio and experimental study, we use electron paramagnetic resonance (EPR) spectroscopy to detect shallow donor states in the ZnO lattice via their characteristic EPR resonance at g = 1.96. Using DFT calculations that account for the chemical environment and the high temperatures during the gas-phase synthesis in conjunction with control experiments with atomic hydrogen, we show that these paramagnetic defects correspond to neutral hydrogenated oxygen sites (OHO0). As a key implication of these findings, ZnO particle powders, although produced under pure gas-phase synthesis conditions and subsequently annealed in vacuum to 873 K, were found to be doped with hydrogen at a base concentration of 10-5 at% and higher. This, in turn, highlights the often-overlooked role of unwanted hydrogen in nanoscale ZnO as components for applications in optics, electronics, and sensing.

en: Ubiquitous shallow trap states and lattice hydrogenation of ZnO particles

defect

display_name: defect

hydrogen

display_name: hydrogen

zno

display_name: zno


en: Neural-network interatomic potentials (NNIPs) have transformed atomistic simulations, by enabling molecular dynamics simulations with near ab initio accuracy at reduced computational costs and improved scalability. Despite these advances, crafting NNIPs remains a complex task, demanding specialized expertise in both machine learning and electronic structure calculations. Here, we introduce an automated, open-source, and user-friendly workflow that streamlines the creation of accurate NNIPs. Our approach integrates density-functional theory with classical molecular dynamics to systematically explore the potential energy landscape using random distortions, strain, interfaces, neutral vacancies, clusters, and molecular dynamics trajectories at varied temperatures and pressures. We leverage active learning combined with on-the-fly calibration of committee disagreement against true errors to ensure reliability. The method is validated on the fully automated training of a NNIP for a diverse set of carbon allotropes, reaching state-of-the-art accuracy and data efficiency. This platform democratizes NNIP development, empowering users to achieve high-precision simulations with minimal human intervention.

en: Automated training of neural-network interatomic potentials

active-learning

display_name: active-learning

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:
A mathematical parameter is proposed to describe the p-p-s electronic coupling strength in transition metal dichalcogenides (TMDCs). Two key descriptors, Iband(p−s)Iband(p−s) and Cband(p−p′)Cband(p−p′), are defined as follows:

Iband(p−s)=ϵp−ϵsϵp,Cband(p−p′)=ϵp′−ϵpϵp′Iband(p−s)=ϵpϵp−ϵs,Cband(p−p′)=ϵp′ϵp′−ϵp

where ϵϵ represents the p-band or s-band center positions of the atoms. Here, pp, p′p′, and ss refer to the p-band of the doped anion atom, the p-band of the first neighboring six S/Se atoms around the doped anion atom, and the s-band of Li atoms, respectively. Iband(p−s)Iband(p−s) characterizes the energy gap between the doped TMDCs and lithium polysulfides (LiPS), where a smaller value indicates a reduced band center energy gap, leading to enhanced interfacial electron transfer dynamics and a stronger interaction between the doped TMDCs and LiPS. On the other hand, Cband(p−p′)Cband(p−p′) quantifies the coupling strength between the p-band of the doped anion atoms and the neighboring S/Se atoms, where a smaller value indicates stronger coupling.

The relationship between the Gibbs energy barrier (ΔGΔG) and the coupling descriptors Iband(p−s)Iband(p−s) and Cband(p−p′)Cband(p−p′) is established through a machine learning approach, represented as:

ΔGi=f(Iband(i)(p−s),Cband(i)(p−p′))ΔGi=f(Iband(i)(p−s),Cband(i)(p−p′))

The model demonstrates that Iband(p−s)Iband(p−s) and Cband(p−p′)Cband(p−p′) effectively capture the relationship between the p-p-s coupling strength and the Gibbs free energy barrier, with a fitting result achieving an average Root Mean Square Error (RMSE) of approximately 0.07 eV.

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).

en: A machine-learning (ML) assisted approach revealing p-p-s electronic coupling descriptor

catalytic-activity

display_name: catalytic-activity

electronic-coupling

display_name: electronic-coupling

li-s-batteries

display_name: li-s-batteries

machine-learning-prediction

display_name: machine-learning-prediction


materialscloud

en: Materials Cloud

envi

en: Environment


en: Due to their abundant use in all-solid-state lasers, nonlinear optical (NLO) crystals are needed for many applications across diverse fields such as medicine and communication. However, because of conflicting requirements, the design of suitable inorganic crystals with strong second-harmonic generation (SHG) has proven to be challenging to both experimentalists and computational scientists. In this work, we leverage a data-driven approach to accelerate the search for high-performance NLO materials. We construct an extensive pool of candidates using databases within the OPTIMADE federation and employ an active learning strategy to gather optimal data while iteratively improving a machine learning model. The result is a publicly accessible dataset of ∼2,200 computed SHG tensors using density-functional perturbation theory. We further assess the performance of machine learning models on SHG prediction and introduce a multi-fidelity correction-learning scheme to refine data accuracy. This study represents a significant step towards data-driven materials discovery in the NLO field and demonstrates how new materials can be screened in an automated fashion.

en: Accelerating the discovery of high-performance nonlinear optical materials using active learning and high-throughput screening

density-functional-perturbation-theory

display_name: density-functional-perturbation-theory

first-principles

display_name: first-principles

high-throughput

display_name: high-throughput

non-linear-optics

display_name: non-linear-optics

second-harmonic-generation

display_name: second-harmonic-generation


density-functional-theory

display_name: density-functional-theory


en: Machine-learning interatomic potentials (MLIPs) have greatly extended the reach of atomic-scale simulations, offering the accuracy of first-principles calculations at a fraction of the cost. Leveraging large quantum mechanical databases and expressive architectures, recent ''universal'' models deliver qualitative accuracy across the periodic table but are often biased toward low-energy configurations. We introduce PET-MAD, a generally applicable MLIP trained on a dataset combining stable inorganic and organic solids, systematically modified to enhance atomic diversity. Using a moderate but highly-consistent level of electronic-structure theory, we assess PET-MAD's accuracy on established benchmarks and advanced simulations of six materials. Despite the small training set and lightweight architecture, PET-MAD is competitive with state-of-the-art MLIPs for inorganic solids, while also being reliable for molecules, organic materials, and surfaces. It is stable and fast, enabling the near-quantitative study of thermal and quantum mechanical fluctuations, functional properties, and phase transitions out of the box. It can be efficiently fine-tuned to deliver full quantum mechanical accuracy with a minimal number of targeted calculations.

en: PET-MAD, a lightweight universal interatomic potential for advanced materials modeling

atomistic-modeling

display_name: atomistic-modeling

foundation-models

display_name: foundation-models

interatomic-potentials

display_name: interatomic-potentials

machine-learning

display_name: machine-learning


en:
Topological Weyl semimetals represent a novel class of quantum materials that exhibit remarkable properties arising from their unique electronic structure. In this work, we employ state-of-the-art ab initio methods to investigate the role of the electron-phonon interactions on the charge transport properties of TaAs. Our calculations of the temperature-dependent electrical conductivity with the iterative Boltzmann transport equation show excellent agreement with experimental measurements above 100 K. Extending the analysis to doped systems, we demonstrate that even small shifts in the Fermi level can lead to substantial changes in conductivity, driven by the complex topology of the Fermi surface. In particular, modifications in Fermi surface nesting emerge as a key factor influencing scattering processes and carrier lifetimes. These findings offer critical insights into the microscopic mechanisms that govern transport in TaAs and highlight the sensitivity of Weyl semimetals to doping and carrier dynamics.
This record includes (i) the code and input files used for the calculations, and (ii) the data and plotting scripts used to generate the figures in the paper.

en: Phonon-limited carrier transport in the Weyl semimetal TaAs

electron-phonon-coupling

display_name: electron-phonon-coupling

transport

display_name: transport

weyl-semimetals

display_name: weyl-semimetals


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

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

atomistic-simulations

display_name: atomistic-simulations

dataset

display_name: dataset


en: The electron-phonon Wannier interpolation (EPWI) method is an efficient way to compute the properties of electron-phonon interactions (EPIs) accurately. This study presents a GPU-accelerated implementation of the EPWI method for computing transport properties, followed by a performance analysis. The implementation is tested on common systems such as aluminum andsilicon. The results show complete consistency with those obtained through CPU computations. The proposed algorithm has the capability of computing the conductivity of aluminum in 20 minutes on a single NVIDIA Tesla V100 GPU, adopting a 2003 electron and phonon sampling grid. This speed is 54 times higher than the CPU-based algorithm, running on two nodes of the Intel Xeon Platinum 8260 CPU. Such impressive acceleration is achieved by carefully designing the algorithm to exploit the GPU’s specific features. The idea of this approach can also be applied to the GPU code for other EPI-related properties. This record includes the input/output files of this work. The code snippets of the core GPU implementation can be found in https://github.com/zheliu137/EPWI_BTE_GPU

en: A high-performance GPU implementation of the electron-phonon Wannier interpolation and the related transport properties

boltzmann-transport-equation

display_name: boltzmann-transport-equation

gpu-acceleration

display_name: gpu-acceleration


en:
A molecular level understanding of surface chemistry involved in the focused electron beam induced deposition (FEBID) with metalorganic molecules is crucial for enhancing metal content in the nanostructures. Here we investigate the FEBID process of trimethyl(methylcyclopentadienyl)platinum(IV) [MeCpPtMe₃] using focused electron beam induced mass spectrometry (FEBiMS), a recently developed in situ analytical technique. A comparison with gas-phase electron impact fragmentation, along with density-functional-theoretical calculations and molecular dynamics simulations are presented. The results indicate that charged fragments generated via dissociative ionization exhibit strong adsorption to the substrate and lack sufficient kinetic energy to desorb, suggesting that the most observed charged species during FEBID originate from gasphase fragmentation above the surface. Furthermore, we propose processes like charge neutralization and dissociative recombination, mechanisms not previously considered in FEBID, could be significant contributors for increasing metal content in the resulting nanostructures.

The data is discussed in the study "C. S. Jureddy, J. Jurczyk, K. Mackosz, H. Lyschuk, J. Kocisek, P. Weber, M. Ernst, A. Verkhovtsev, A. V. Solov'yov, J. Fedor and I. Utke, Phys. Chem. Chem. Phys., 2025"

en: Desorption of fragments upon electron impact on adsorbates: implications for electron beam induced deposition

dft

display_name: dft

experimental

display_name: experimental

febid

display_name: febid

molecular-dynamics

display_name: molecular-dynamics

surface-and-gas-phase-processes

display_name: surface-and-gas-phase-processes


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

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

automation

display_name: automation

equation-of-state

display_name: equation-of-state

pseudopotentials

display_name: pseudopotentials

verification

display_name: verification


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).


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

en: Materials Cloud

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

magnetism

display_name: magnetism

wannier-functions

display_name: wannier-functions


en: The existence of the hydroxyl ligand on a single- atom catalyst embedded in graphene, especially Fe(OH)−N4−C is suggested to improve the oxygen reduction reaction (ORR) from the pristine Fe−N4−C. However, the theoretical electrocatalytic activity of the ORR process is shown to be highly dependent on the electrolyte solution and the applied electrode potential. Herein, we performed the constant-potential simulation and microkinetic modeling to investigate the mechanism of ORR on well studied Fe(OH)−N4−C and Co(OH)−N4−C catalysts and compared to the pristine ones, using density functional theory (DFT) calculation combined with the effective screening medium method and the reference interaction site model (ESM-RISM). It was found that the Fe(OH)−N4−C and Co(OH)−N4−C have comparable ORR activities to Fe−N4−C and Co−N4−C, and the calculated limiting potentials and half-wave potentials agree with the experimental values, in contrast to the results obtained using the constant-charge method (i.e., under the neutral condition) regardless of consideration of the solvation effect.

en: Effect of the OH functionalization on the catalytic activity of single-atom catalyst

envi

en: Environment

density-functional-theory

display_name: density-functional-theory

implicit-solvation-model

display_name: implicit-solvation-model

oxygen-reduction-reaction

display_name: oxygen-reduction-reaction

single-atom-catalyst

display_name: single-atom-catalyst


en: The inner structure of polymeric microgels critically influences their responsiveness and potential applications, yet remaining challenging to resolve at molecular resolution. In this work, a structural characterization of thermoresponsive copolymer microgels is provided by integrating small-angle neutron scattering (SANS), dynamic light scattering (DLS), and nuclear magnetic resonance (NMR) measurements with multi-scale simulations. Specifically, Poly(N-isopropylacrylamide-co-N-isopropylmethacrylamide), P(NIPAM-co-NIPMAM), copolymer microgels, in which a random monomer distribution is conventionally assumed, are considered. By synthesizing different samples, including isotopically labeled microgels via selective deuteration, the microgels swelling behavior is probed and distinct polymer-specific signatures are revealed. To elucidate their internal distribution, monomer-resolved microgel simulations are performed across different copolymer models. A direct comparison between experimental and numerical form factors provides evidence of preferential organization into block structures, challenging the prevailing view of random distribution. 13C-NMR experiments confirm NIPAM-rich blocks and atomistic simulations link this unexpected block-like architecture to distinct local hydrogen-bonding patterns. This integrated approach provides the first direct evidence of preferential block formation in P(NIPAM-co-NIPMAM) microgels. Beyond this system, these results establish a generalizable strategy for unveiling hidden structural order in copolymer microgels, offering new strategies to tailor their design and to enhance control of material responsivity.

en: Thermoresponsive copolymer microgels synthesized via single-step precipitation polymerization: random or block structure?

copolymerization

display_name: copolymerization

microgels

display_name: microgels

molecular-modeling

display_name: molecular-modeling

nmr-spectroscopy

display_name: nmr-spectroscopy

selective-neutron-scattering

display_name: selective-neutron-scattering


electron-phonon-coupling

display_name: electron-phonon-coupling


en:
In this paper, we show many-body calculations of magnon-phonon (mag-ph) coupling based on the ab initio Bethe-Salpeter equation. We derive expressions for mag-ph coupling matrices and compute them in 2D ferromagnets, focusing on hydrogenated graphene and monolayer CrI3. Our analysis shows that electron-phonon (e-ph) and mag-ph interactions differ significantly, where modes with weak e-ph coupling can exhibit strong mag-ph coupling (and vice versa), and reveals which phonon modes couple more strongly with magnons. In both materials studied here, the inelastic magnon relaxation times decrease abruptly above the threshold for emission of strongly coupled phonons, revealing a low-energy window where magnons are long-lived. Averaging over this window, we compute the temperature-dependent magnon mean-free path, a key figure of merit for spintronics, entirely from first principles. The theory and computational tools shown in this work enable studies of magnon interactions, scattering, and dynamics in generic materials, advancing the design of magnetic systems and magnon- and spin-based devices.

This archive include the necessary inputs for DFT, DFPT in Quantum Espresso, and GW-BSE in Yambo code. Since the routine for magnon-phonon coupling is not published, we provided the raw data files used for the figures in our paper as the reference.

en: Magnon-phonon interactions from first principles

bethe-salpeter-equation

display_name: bethe-salpeter-equation

first-principles

display_name: first-principles

magnon-phonon-coupling

display_name: magnon-phonon-coupling


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: Phase change materials are exploited in several enabling technologies such as storage class memories, neuromorphic devices and memories embedded in microcontrollers. A key functional property for these applications is the fast crystal nucleation and growth in the supercool liquid phase. Over the last decade, atomistic simulations based on density functional theory (DFT) have provided crucial insights on the early stage of this process. These simulations are, however,restricted to a few hundred atoms for at most a few ns. More recently, the scope of the DFT simulations has been greatly extended by leveraging on machine learning techniques. In our paper, we show that the exploitation of a recently devised neural network potential for the prototypical phase change compound Ge2 Sb2Te5 (GST)  allows simulating the crystallization process in a multimillion atom model at the length and time scales of the real memory device. The simulations provide a vivid atomistic picture of the subtle interplay between crystal  nucleation and crystal growth from the crystal/amorphous rim in a model mimicking the operationof the memory in the Wall geometry. Our simulation showes that at realistic conditions of the set operation of the memory in the Wall architecture the crystallization is dominated by growth at the crystal-amorphous interface. Moreover, the simulations have allowed quantifying the distribution of point defects that controls electronic transport, in a very large crystallite grown at the real conditions of the set process of the device.

en:
Million-Atom Simulation of the Set Process in Phase
Change Memories at the Real Device Scale

crystallization

display_name: crystallization

device-scale

display_name: device-scale

electronic-memories

display_name: electronic-memories

machine-learning-potentials

display_name: machine-learning-potentials

phase-change-materials

display_name: phase-change-materials


en: Open-shell nanographenes have attracted significant attention due to their structurally tunable spin ground state. While most characterization has been conducted on weakly interacting substrates such as noble metals, the influence of magnetic surfaces remains largely unexplored. In a recent publication,  we investigate how TbAu2, a rare-earth-element-based surface alloy, affects the magnetic properties of phenalenyl (or [2]triangulene (2T)), the smallest spin-1/2 nanographene. Scanning tunneling spectroscopy (STS) measurements reveal a striking contrast: while 2T on Au(111) exhibits a zero-bias Kondo resonance─a hallmark of a spin-1/2 impurity screened by the conduction electrons of the underlying metal─deposition on TbAu2 induces a symmetric splitting of this feature by approximately 20 mV. This opening is large compared to the few meV splitting induced by externally applied magnetic fields. We attribute this splitting to a strong proximity-induced interaction with the ferromagnetic out-of-plane magnetization of TbAu2. Moreover, our combined experimental and many-body model analysis demonstrates that this interaction is spatially modulated, following the periodicity of the TbAu2 surface superstructure. These findings highlight that TbAu2 serves as a viable platform for stabilizing and characterizing the magnetic properties of spin-1/2 nanographenes and for studying the interactions of more complex π-magnetic materials with magnetic substrates. The record contains data supporting the results presented in the publication

en: Proximity-driven magnetic coupling between an open-shell nanographene and a rare-earth surface alloy

dftu

display_name: dftu

open-shell-molecules

display_name: open-shell-molecules

proximity-interaction

display_name: proximity-interaction

scanning-tunneling-microscopy

display_name: scanning-tunneling-microscopy

spin

display_name: spin


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).


en: Predicting the dynamical properties of topological matter is a challenging task, not only in theoretical and experimental settings, but also computationally. Numerical studies are often constrained to studying simplified models and lattices. Here we propose a time-dependent correlated ansatz for the dynamical preparation of a quantum-spin-liquid state on a Rydberg atom simulator. Together with a time-dependent variational Monte Carlo technique, we can faithfully represent the state of the system throughout the entire dynamical preparation protocol. We are able to match not only the physically correct form of the Rydberg atom Hamiltonian but also the relevant lattice topology at system sizes that exceed current experimental capabilities. This approach gives access to global quantities such as the topological entanglement entropy, providing insight into the topological properties of the system. Our results confirm the topological properties of the state during the dynamical preparation protocol, and deepen our understanding of topological entanglement dynamics. We show that, while the simulated state exhibits local properties resembling those of a resonating-valence-bond state, in agreement with experimental observations, it lacks the latter’s characteristic topological entanglement entropy signature irrespective of the degree of adiabaticity of the protocol.

en: Predicting topological entanglement entropy in a Rydberg analog simulator

dynamics

display_name: dynamics

marvelqs

display_name: marvelqs

quantum-spin-liquids

display_name: quantum-spin-liquids

topological

display_name: topological

variatonal-monte-carlo

display_name: variatonal-monte-carlo


materialscloud

en: Materials Cloud


creep-test

display_name: creep-test

nuclear-structural-materials

display_name: nuclear-structural-materials

size-effect

display_name: size-effect


en:
Nuclear Magnetic Resonance (NMR) chemical shifts are powerful probes of local atomic and electronic structure that can be used to resolve the structures of powdered or amorphous molecular solids.
Chemical shift driven structure elucidation depends critically on accurate and fast predictions of chemical shieldings, and machine learning (ML) models for shielding predictions are increasingly used as scalable and efficient surrogates for demanding ab initio calculations. However, the prediction accuracies of current ML models still lag behind those of the DFT reference methods they approximate, especially for nuclei such as 13C and 15N. Here, we introduce \shiftmlthree{}, a deep-learning model thatimproves the  accuracy of predictions of isotropic chemical shieldings  in molecular solids, and does so while also predicting the full shielding tensor.
On experimental benchmark sets, we find root-mean-squared errors with respect to experiment for ShiftML that approach those of DFT reference calculations, with RMSEs of 0.53 ppm for 1H, 2.4 ppm for 13C, and 7.2 ppm for 15N, compared to DFT values of 0.49 ppm, 2.3 ppm, and 5.8 ppm, respectively.

en: A deep learning model for chemical shieldings in molecular organic solids including anisotropy

machine-learning

display_name: machine-learning

nmr

display_name: nmr

snsf

display_name: snsf


envi

en: Environment


en: Liquid electrolytes adsorbed at the surface of metallic electrodes display a multitude of structures that can largely differ from the parent bulk system, both in terms of composition and local organization. In particular, the existence of disorder-order or order-order transitions has been increasingly reported in experimental and simulation studies, and the electrode potential identified as the corresponding driving force. The microscopic mechanisms and the stages of the process are, however, poorly understood, and the free energy variation during the transition remains insufficiently characterized. To fill this gap, we investigate the crystallization of the adsorbed layer in a prototypical molten salt-metal interface. We demonstrate that the transition from the disordered to ordered structures proceeds in two stages. Pre-ordering effects are observed across a wide range of potentials, resulting in the formation of a poly-crystalline structure on the electrode surface, before an abrupt ordering transition finally occurs. The pre-ordering displays signature of a continuous transition. On the other hand, finite-size effects analysis proves the first-order character of the transition towards the mono-crystalline state. Upon increasing the system size, a shift in the onset applied voltage is observed, accompanied by a dramatic increase in the free energy barrier. The latter reflects in the interfacial capacitance, which displays a peak that sharpens with increasing system size.

en: Electrically driven first-order phase transition of a 2D ionic crystal at the electrode/electrolyte interface

electrochemical-interface

display_name: electrochemical-interface

electrolyte

display_name: electrolyte

molecular-dynamics

display_name: molecular-dynamics

order-disorder-transition

display_name: order-disorder-transition


en:
In this paper, we present and benchmark our first-principles calculations of magnon dispersions and wave functions in monolayer Cr trihalides using the finite-momentum Bethe-Salpeter equation (BSE) to describe collective spin-flip excitations. We study the dependence of magnon dispersions on the halide species and resolve the small topological gap at the Dirac point in the magnon spectrum by including spin-orbit coupling. We analyze the obtained magnon wave functions to gain insight into magnon states in real and reciprocal space. We also compare our method with the Heisenberg spin model. This work advances the quantitative modeling of magnons, providing the starting point for studying magnon interactions in a first-principles BSE framework.

This archive includes all the inputs for DFT calculations in QuantumEspresso and GW-BSE with the Yambo code to reproduce the data in our paper.

en: Magnons in chromium trihalides calculated with the ab initio Bethe-Salpeter equation

bethe-salpeter-equation

display_name: bethe-salpeter-equation

dft

display_name: dft

magnons

display_name: magnons


en: Polyacene analogues, consisting of short acene segments separated by nonbenzenoid rings, offer intriguing electronic properties and magnetic interactions. Pentalene-bridged polyacenes (PPs), in particular, hold promise for enhancing the electrical conductivity and potential open-shell ground states. However, PPs have remained elusive in solution chemistry due to poor solubility and limited synthetic protocols. In a recent publication,  we report the on-surface synthesis of PPs through the annulation between ortho-xylene groups. Scanning tunneling microscopy and atomic force microscopy reveal that the reconstructed Au(110) surface significantly enhances the chemoselectivity of the annulation process. Scanning tunneling spectroscopy combined with density functional theory suggests that PP exhibits a narrow direct band gap, similar to long acenes. This work demonstrates the potential for band structure engineering in polyacene analogues by incorporating nonbenzenoid rings, paving the way to advancements in organic electronics and spintronics. This record contains computational data supporting the results discussed in the manuscript.

en: Enhancing chemoselectivity of on-surface reactions on reconstructed Au(110): the case of a pentalene-bridged polyacene analogue

annulation

display_name: annulation

hydrocarbons

display_name: hydrocarbons

on-surface-synthesis

display_name: on-surface-synthesis

scanning-tunneling-microscopy

display_name: scanning-tunneling-microscopy


en: DFT calculations were carried out using the CP2K and Quantum ESPRESSO packages to study the structural, electronic, and catalytic properties of platinum nanoparticles supported on ceria surfaces. The models included Pt nanoparticles of different sizes on CeO₂ surfaces. The DFT+U approach was used to describe Ce 4f electrons, and 4f-in-core pseudopotentials were employed to model Ce³⁺ species. Adsorption energies, XPS binding energies, and reaction energies were computed to analyze the interaction of Pt with the ceria support and adsorbate (CO), providing insights into the size-dependent electronic behavior and catalytic activity of Pt species.

en: Quantifying electronic and geometric effects on the activity of platinum catalysts for water-gas shift

nanoparticle

display_name: nanoparticle


en: The direct conversion of methane to methanol (DCMM) under continuous flow and atmospheric pressure offers notable environmental benefits and industrial promise, but remains a long-standing challenge due to the difficulty of activating CH4 while avoiding over-oxidation of methanol. Here, we demonstrate that pure ceria (CeO2), without any metal promoters, enables gas-phase DCMM with up to 80 % selectivity at 300–350 °C, upon optimization of the H2O/O2 ratio. At 550 °C, methanol and formaldehyde are formed at rates of 24 and 36 μmol g-1 h-1, respectively-both dropping below 1 μmol g-1 h-1 in the absence of O2. Ex situ transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and Raman spectroscopy confirm that CeO2 maintains structural integrity and resists carbon deposition during reaction. Combining kinetic studies, steady-state in-situ diffuse reflectance infrared Fourier transform spectroscopy (in-situ DRIFTS), and density functional theory (DFT) reveals that hydroxyl groups (OH), generated from water dissociation, play a multifaceted role: they facilitate C–H bond activation, promote methoxy formation, and enhance methanol desorption. In-situ ambient-pressure X-ray photoelectron spectroscopy (AP-XPS) directly reveals the evolution of surface intermediates and shows that co-feeding O2 and H2O suppresses CH3O and CHx accumulation while boosting methanol yield—indicating a rapid intermediate turnover as key to sustained activity. AP-XPS O 1s spectra further highlight that O2 promotes H2O dissociation, regenerating reactive OH groups and maintaining performance at elevated temperature. These findings offer molecular-level insights into how water and oxygen cooperatively tune reactivity, enabling efficient methane-to-methanol conversion on a metal-free oxide catalyst.

en: Synergy of oxygen and water in ceria-catalyzed direct conversion of methane to methanol under continuous flow

ap-xps

display_name: ap-xps

in-situ

display_name: in-situ

in-situ-drifts

display_name: in-situ-drifts

methane-to-methanol

display_name: methane-to-methanol

reaction-mechanism

display_name: reaction-mechanism


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).


en: Transition metal dichalcogenides (TMDCs) are promising candidates for future nano-transistor channels due to their outstanding intrinsic transport properties. However, their electron mobility is highly sensitive to the surrounding dielectric, often falling well below theoretical expectations. In this work, we explore how a stacked Al2O3 dielectric affects electron mobility in monolayer WS2 using first-principles quantum transport simulations. We identify that fluctuations in the electrostatic potential, arising from the disordered structure of Al2O3, significantly degrade mobility, especially when WS2 interfaces with under-coordinated aluminum atoms. Our calculated mobilities (≃1–30 cm2/(V ⋅ s)) align with experimental observations and remain far from the ideal limit (≃300 cm2/(V ⋅ s)). We further demonstrate that encapsulating WS2 with hexagonal boron nitride (hBN) or employing a crystalline oxide can recover high mobility values. However, these strategies introduce trade-offs in electrostatic control and fabrication complexity, underlining the need for careful dielectric engineering in TMDC-based devices.

en: Mobility calculation in disordered WS2-Al2O3 stacks from first principles

mobility

display_name: mobility


materialscloud

en: Materials Cloud


en: We present a comprehensive first-principles study of the magnetoresistance in ZrSiX (X = S, Se, Te) topological nodal-line semimetals. Our study demonstrates that all primary features of the experimentally measured magnetoresistance in these materials are captured by our calculations, including the unusual butterfly-shaped anisotropic magnetoresistance. This anisotropic magnetoresistance can be accurately reproduced using the semiclassical Boltzmann transport theory without introducing any information on the topological nature of bands or the concepts of topological phase transition. Considering the complex structure of the Fermi surface in these topological materials, we develop a theoretical description explaining the features observed in magnetoresistance measurements. Additionally, the atypical Hall resistance can be interpreted by the same semiclassical approach. Our findings establish magnetotransport as a powerful tool for analyzing the geometry of the Fermi surface, complementing angle-resolved photoemission spectroscopy and quantum oscillation measurements. This approach is demonstrated to be particularly useful for determining the role of non-trivial topology in transport properties.

en: Magnetoresistance in ZrSiX (X = S, Se, Te) nodal-line semimetals

boltzmann-theory

display_name: boltzmann-theory

density-functional-calculations

display_name: density-functional-calculations

fermi-surface

display_name: fermi-surface

first-principles-calculations

display_name: first-principles-calculations

hall-effect

display_name: hall-effect

magnetoresistance

display_name: magnetoresistance

node-line-semimetals

display_name: node-line-semimetals

topological-materials

display_name: topological-materials


envi

en: Environment


bethe-salpeter-equation

display_name: bethe-salpeter-equation

dft

display_name: dft

magnons

display_name: magnons


en:
Traditional atomistic machine learning (ML) models serve as surrogates for quantum mechanical (QM) properties, predicting quantities such as dipole moments and polarizabilities, directly from compositions and geometries of atomic configurations. With the emergence of ML approaches to predict the "ingredients" of a QM calculation, such as the ground state charge density or the effective single-particle Hamiltonian, it has become possible to obtain multiple properties through analytical physics-based operations on these intermediate ML predictions. We present a framework to seamlessly integrate the prediction of an effective electronic Hamiltonian, for both molecular and condensed-phase systems, with PySCFAD, a differentiable QM workflow. This integration facilitates training models indirectly against functions of the Hamiltonian such as electronic energy levels, dipole moments, polarizability, etc. We then use this framework to explore various possible choices within the design space of hybrid ML/QM models, examining the influence of incorporating multiple targets on model performance and learning a reduced-basis ML Hamiltonian that can
reproduce targets computed from a much larger basis. Our benchmarks evaluate the accuracy and transferability of these hybrid models, compare them against predictions of atomic properties from their surrogate models, and provide indications to guide the design of the interface between the ML and QM components of the model. For our benchmarks we have used a subset of the QM7 and QM9 datasets as well as two extrapolative datasets for long chain polyalkenes/acenes and polyenoic acid series, along with a small graphene dataset.

en: Exploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework

automatic-differentiation

display_name: automatic-differentiation

hamiltonian

display_name: hamiltonian

machine-learning

display_name: machine-learning


en: We perform a high-throughput computational search for novel phonon-mediated superconductors, starting from the Materials Cloud three-dimensional database (MC3D) of experimentally known inorganic stoichiometric compounds. We first compute the Allen-Dynes critical temperature Tc for 4533 non-magnetic metals using a direct and progressively finer sampling of the electron-phonon couplings. For the candidates with the largest Tc, we use automated Wannierizations and electron-phonon interpolations to obtain a high-quality dataset for the most promising 250 dynamically stable structures, for which we calculate spectral functions, superconducting bandgaps, and isotropic Migdal-Eliashberg critical temperatures. For 140 of these, we also provide anisotropic Migdal-Eliashberg superconducting gaps and critical temperatures. The approach is remarkably successful in finding known superconductors, and we find 24 unknown ones with a predicted anisotropic Tc above 10 K. Among them, we identify a possible double gap superconductor (p-doped BaB2), a non-magnetic half-Heusler ZrRuSb, and the perovskite TaRu3C, all exhibiting significant Tc. Finally, we introduce a sensitivity analysis to estimate the robustness of the predictions.

en: Charting the landscape of Bardeen-Cooper-Schrieffer superconductors in experimentally known compounds

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

high-throughput

display_name: high-throughput

migdal-eliashberg

display_name: migdal-eliashberg

superconductivity

display_name: superconductivity


en:
Identifying organic molecules with desirable properties from the extensive chemical space can be challenging, particularly when property evaluation methods are time-consuming and resource intensive. In this study, we illustrate this challenge by exploring the chemical space of large oligomers, constructed from monomeric building blocks, for potential use in organic photovoltaics (OPV). To facilitate this exploration, we developed a Python package called stk-search, which employs a building block approach. For this purpose, we developed a python package to search the chemical space using a building block approach: stk-search. We use stk-search (GitHub link) to compare a variety of search algorithms, including those based upon Bayesian optimization and evolutionary approaches. Initially, we evaluated and compared the performance of different search algorithms within a precomputed search space. We then extended our investigation to the vast chemical space of molecules formed of 6 building blocks (6-mers), comprising over 1014 molecules. Notably, while some algorithms show only marginal improvements over a random search approach in a relatively small, precomputed, search space, their performance in the larger chemical space is orders of magnitude better. Specifically, Bayesian optimization identified a thousand times more promising molecules with the desired properties compared to random search, using the same computational resources.
This record contains the dataset generated during the exploration of the space of molecules formed of 6 building blocks for application as donor molecules for OPV application, with calculated properties such as Ionisation potential, excited state energy and oscillator strength.

en: Exploring different search approaches to discover donor molecules for organic solar cells

bayesian-optimisation

display_name: bayesian-optimisation

building-block-approach

display_name: building-block-approach

genetic-algorithm

display_name: genetic-algorithm

organic-solar-cells

display_name: organic-solar-cells

representation-learning

display_name: representation-learning


en: Rare-earth superhydrides have attracted considerable attention because of their high critical superconducting temperature under extreme pressures. They are known to have localized valence electrons, implying strong electronic correlations. However, such many-body effects are rarely included in first-principles studies of rare-earth superhydrides because of the complexity of their high-pressure phases. In this work, we use a combined density functional theory and dynamical mean-field theory approach to study both electrons and phonons in the prototypical rare-earth superhydride CeH9, shedding light on the impact of electronic correlations on its critical temperature for phonon-mediated superconductivity. Our findings indicate that electronic correlations result in a larger electronic density at the Fermi level, a bigger superconducting gap, and softer vibrational modes associated with hydrogen atoms. Together, the inclusion of these correlation signatures increases the Migdal-Eliashberg superconducting critical temperature from 47 K to 96 K, close to the measured 95 K. Our results reconcile experimental observations and theoretical predictions for CeH9 and herald a path towards the quantitative modeling of phonon-mediated superconductivity for interacting electron systems.

en: Impact of electronic correlations on the superconductivity of high-pressure CeH9

electronic-correlations

display_name: electronic-correlations

high-pressure

display_name: high-pressure


en: We develop a convolutional neural network (NN) approach able to predict the elastic contribution to chemical potential μₑ at the surface of a 2D strained film given its profile h(x). Arbitrary h(x) profiles are obtained by using a Perlin Noise generator and the corresponding μₑ profiles are calculated either by a Green's function approximation (GA) or by Finite Element Method (FEM). First, a large dataset is produced by exploiting the GA method and it is then used for the training of the NN model. The performance of the trained NN is extensively examined, demonstrating its ability to predict μₑ looking both to the training/validation set and to an additional testing set containing different profiles, including sinusoids, gaussians and sharp peaks never considered in the NN training. The NN is then applied to simulate the morphological evolution of strained Ge films, where the predicted μₑ at each integration timestep plays the role of driving force for material redistribution in competition with a surface energy term (proportional to local curvature) and eventually a wetting energy contribution. Both surface diffusion and evaporation/condensation dynamics are considered and the proposed NN approach is shown to well match the evolution expected by using GA. On this basis, a smaller dataset is built with μₑ profiles calculated by FEM and a new NN model is trained on it. Once again the trained NN-model returns reliable prediction of the FEM μₑ. The findings suggest that the proposed NN-based strategy can be used in replacement of the computationally intensive FEM calculations, enabling the simulation of larger scales and longer time scales untreatable by direct FEM calculation.

en: Deep learning of surface elastic chemical potential in strained films: from statics to dynamics

deep-learning

display_name: deep-learning

elastic-chemical-potential

display_name: elastic-chemical-potential

surface-evolution

display_name: surface-evolution

thin-films

display_name: thin-films


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).


en:
The rapid progress in generative models for molecular design has led to extensive libraries of candidate molecules for biological and chemical applications. However, ensuring these molecules are diverse and representative of broader chemical space remains challenging, with researchers often over-exploring limited regions or missing promising candidates due to inadequate monitoring tools. This work presents NaviDiv (Navigating Diversity in Chemical Space), a comprehensive web-based framework for managing chemical diversity in the string-based generative molecular design through three integrated capabilities: multi-metric diversity analysis capturing structural, syntactic, and molecular framework variations; interactive real-time visualization enabling immediate detection of model collapse; and adaptive constraint generation that dynamically guides optimization while preserving diversity. Through a singlet fission material discovery case study using REINVENT4, we demonstrate that different diversity metrics (i.e. structural similarity, fragment composition, and sequence patterns) respond differently during optimization, with constraint effectiveness depending critically on representational alignment with the generative model. N-gram-based constraints outperform fingerprint-based approaches due to direct correspondence with SMILES generation, while combined constraints maintain diversity across all metrics while achieving optimization performance within 15\% of unconstrained baselines. The framework is freely available at : https://github.com/LCMD-epfl/NaviDiv, providing accessible tools for data-driven decisions about diversity-property trade-offs in automated molecular discovery.

This dataset contains all materials generated during the singlet fission material discovery case study presented in this work. It includes: (1) the REINVENT4 prior model used for molecule generation, (2) the trained ChemProp model checkpoints used to evaluate singlet fission potential, and (3) the generated molecules from each experimental run. For each run, molecules are stored in a CSV file alongside the corresponding chemical diversity analysis outputs in the same directory.

en: NaviDiv: a web app for monitoring chemical diversity in generative molecular design

chemical-diversity

display_name: chemical-diversity

generative-models

display_name: generative-models

singlet-fission

display_name: singlet-fission


materialscloud

en: Materials Cloud


en: Diboron trioxide (B2O3) represents an unusual case among polymorphic oxides, for its vitrified state features superstructural units – planar boroxol groups – that are never observed in its three-dimensional crystalline polymorphs. Crystalline polymorphs that incorporate boroxol groups have only been predicted theoretically, although their formation is crucial to rationalize the ability of B2O3 to vitrify. Here we present the synthesis of a two-dimensional crystalline B2O3 polymorph constituted by boroxol groups arranged in an atomically thin honeycomb lattice. By combining surface science experimental techniques with ab initio calculations, we characterize the structural and electronic properties of this B2O3 polymorph down to the atomic level. This discovery enlarges the family of two-dimensional materials and enables the atomic tracking of individual structural units in trioxides.

en: Two-dimensional diboron trioxide crystal composed by boroxol groups

boroxol-groups

display_name: boroxol-groups


envi

en: Environment


en: Recent advances in the synthesis of graphene fragments that possess unpaired π-electrons and display high-spin ground states have unlocked possibilities to explore exotic physical phenomena related to magnetism. The high degree of spin-delocalisation makes these non-metal-based systems ideal building blocks for the construction of chains and lattices with strongly correlated magnetic ground states, which is the main requisite for measurement-based quantum computation.In a recent publication, we demonstrate the magnetic bistability of a diradical nanographene that allows direct spin manipulation at the single-molecule level. To this end, we made use of solution-phase synthesis and tip-induced activation on a metallic surface to construct a helical non-Kekulé hydrocarbon spin switch, with a reversible transformation between a magnetic ground state and a non-magnetic one via intramolecular bond formation/breaking. The switching process was monitored by scanning tunneling spectroscopy measurements, illustrating that this, and related systems, hold potential as spin-switch units for direct manipulation of magnetism and quantum information in entangled spin systems. This record contains data that support the results discussed in the publication.

en: Controlled magnetic bistability of a helical non-Kekulé hydrocarbon on a Au(111) surface

ab-initio-simulations

display_name: ab-initio-simulations

hydrocarbons

display_name: hydrocarbons

magnetic-nanocarbons

display_name: magnetic-nanocarbons

scanning-probe-microscopy

display_name: scanning-probe-microscopy

snsf

display_name: snsf


en: Optimal exploitation of supercomputing resources for the evaluation of electrostatic forces remains a challenge in molecular dynamics simulations of very large systems. The most efficient methods are currently based on particle-mesh Ewald sums and achieve semi-logarithmic scaling in the number of particles. These methods solve the problem in reciprocal space, requiring extensive use of Fast Fourier transforms (FFTs). While highly efficient in many contexts, FFTs may encounter scalability challenges at very large processor counts due to their communication requirements. To mitigate these problems, the development and scalable coding of real-space approaches to solve the Poisson equation on a grid is an active field of research. In this work, we introduce a novel real-space approach that provides some advantages over alternatives. Our method exploits an extended Lagrangian in which the values of the field at the grid points are treated as auxiliary variables of zero inertia and the discretized Poisson equation is enforced as a dynamical constraint. The solution of the constraints leads to a linear system - different from those appearing in other real-space approaches - that can be efficiently solved via state-of-the-art iterative methods. The method inherits the numerical scaling of the adopted iterative solver, e.g. linear with a multigrid (MG) approach, but converges with fewer cycles. We analyze this approach considering realistic simulations of molten NaCl that validate its ability to reproduce structural and transport properties. Using this non-trivial benchmark, we demonstrate linear scaling and illustrate some features of our algorithm.

en: Mass-zero constrained molecular dynamics for electrostatic interactions

cecam

display_name: cecam

electrostatic-interactions

display_name: electrostatic-interactions

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

molten-salts

display_name: molten-salts


en: PROTACs are of high interest because of their ability to target previously challenging disease-related proteins. PROTACs promote the degradation of targets by ensuring the proximity of the E3-ligase and the target. The relation between structure and function of the ternary complex is of great interest. Of equal importance is the delivery of these drugs and determining the key interactions that stabilize the solid drug forms. Here, we determine the complete atomic-level structure of an amorphous VHL-based SMARCA2 PROTAC (PROTAC 2). The structure is solved using ultra-fast MAS, and DNP enhanced, NMR crystallography. We found that PROTAC 2 is more disordered as compared to previously studied amorphous formulations, and that the three functional units of the molecule have three distinct structural types. Overall, in contrast to structures of smaller drug molecules, where intermolecular H-bonding interactions were found to be the main stabilization mechanism for the amorphous solid form, for PROTAC 2 we postulate that, in analogy to glassy polymers, the main stabilization mechanism of the amorphous form is the entropic contribution introduced by the overall flexibility, especially in the linker region of the molecule. We also note that the most populated conformations found in the amorphous form differ from those of bound PROTAC 2 found in the ternary protein complex as determined via X-ray crystallography. Our results provide insight into key structural features that stabilize amorphous formulations, specifically for molecules that can target proteins previously considered undruggable.

en: Determination of key functional structures of an amorphous VHL-based SMARCA2 PROTAC

amorphous

display_name: amorphous

drug-formulation

display_name: drug-formulation

nmr

display_name: nmr


en: Strongly correlated physics arises from electron-electron scattering within partially filled orbitals. Organic molecules in open-shell configurations are therefore good candidates to exhibit many-body effects. We focus on electron transport in a two-terminal single-molecule junction setup, where the molecular bridge consists of an organic radical with a molecular orbital that hosts a single unpaired electron (SOMO). We perform beyond state-of-the-art numerical simulations combining an ab-initiodescription of the chemical environment, with quantum field-theoretical techniques that account for many-body effects. The key observation is that the SOMO resonance is prone to splitting and we identify a giant electronic scattering rate as the driving many-body mechanism, akin to that of the Mott metal-to-insulator transition. By comparing linear and cyclic radicals, we show that the spatial distribution of the SOMO and its projection on the molecular backbone have dramatic consequences for the transport properties of the junction. We argue that the phenomenon and the underlying microscopic mechanism apply to a broad family of open-shell molecular systems, and can explain puzzling experimental observations such as suppressed conductance in radical junctions.

en: Strongly correlated physics in organic open-shell quantum systems

dft

display_name: dft

dmft

display_name: dmft

exact-diagonalization

display_name: exact-diagonalization

transport

display_name: transport


en: We present a high-throughput computational screening for fast lithium-ion conductors to identify promising materials for application in all solid-state electrolytes. Starting from more than 30,000 Li-containing experimental structures sourced from Crystallography Open Database, Inorganic Crystal Structure Database and Materials Platform for Data Science, we perform highly automated calculations to identify electronic insulators. On these ~1000 structures, we use molecular dynamics simulations to estimate Li-ion diffusivities using the pinball model, which describes the potential energy landscape of diffusing lithium with accuracy similar to density functional theory while being 200-500 times faster. Then we study the ~60 most promising and previously unknown fast conductors with full first-principles molecular dynamics simulations at several temperatures to estimate their activation barriers. The MD trajectories of these structures are included along with all the structures on which electronic strutcure calculations were performed. The entire screening provenance is provided for one structure to facilitate the reproduction of this screening.

en: Novel fast Li-ion conductors for solid-state electrolytes from first-principles

computational-high-throughput-screening

display_name: computational-high-throughput-screening

first-principles

display_name: first-principles

li-ion-conductors

display_name: li-ion-conductors

molecular-dynamics

display_name: molecular-dynamics

solid-state-electrolytes

display_name: solid-state-electrolytes


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).


en: Cobalt-based oxides have been investigated as potential alternatives to Ir/Ru-based oxides for catalyzing the oxygen evolution reaction (OER) in acidic media. Past research, however, is mainly focused on the spinel oxide structure so far. Exploring alternative crystal structures is essential for expanding the material library and developing highly efficient OER catalysts for acidic environments. As a proof of concept, we demonstrate that Co-based perovskite oxides can drive acidic OER effectively. Appling hard/soft X-ray absorption spectroscopy (hXAS/sXAS) characterizations, we show that the La and Ce doped SrCoO3 (denoted as LSC and CSC, respectively) have a bulk-average Co oxidation state close to 3+ and surface-dominant low-spin CoIII species. Electrochemical analysis reveals that they only show one Co redox pair, similar to CoOOH in acidic environments. The recorded Tafel slopes are around ~65 mV dec-1, comparable to the benchmarking Ir/Ru-based catalysts. The combination of the spectroscopic and electrochemical findings presented here highlights the important role of low-spin CoIII species in catalyzing OER in acidic environments and contributes to the rational design of non-noble metal OER catalysts.

en: Cobalt-based perovskite oxides as catalysts for acidic oxygen evolution reaction

acidic-oxygen-evolution-reaction

display_name: acidic-oxygen-evolution-reaction

cobalt-based-catalysts

display_name: cobalt-based-catalysts

cobalt-oxidation-state

display_name: cobalt-oxidation-state

experimental

display_name: experimental

perovskite-oxides

display_name: perovskite-oxides

x-ray-adsorption-spectroscopy

display_name: x-ray-adsorption-spectroscopy


materialscloud

en: Materials Cloud


en: Core-shell (CS) microgels–comprising a solid core chemically linked to a crosslinked polymer shell–are an important class of stimuli-responsive colloids widely employed for both fundamental and applied studies. Despite extensive experimental investigations, their numerical modeling remains underdeveloped; in particular, a detailed, temperature-resolved insight of their effective interactions is missing. While pairwise descriptions known for standard microgels are expected to be partially extensible to CS-microgels, the effect of the solid core and in turn, the extent and character of many-body contributions remain elusive. To clarify these open issues, this work introduces a computational method to generate realistic CS-microgels at different crosslinker concentrations, accurately reproducing the structure of experimental silica core–poly-N-isopropylacrylamide (pNIPAM) shell microgels at different temperatures across the Volume Phase Transition (VPT) and for different shell-to-core ratios. After validating the model, effective interactions between CS-microgels are calculated, extracting not only two-body interactions but also providing the first quantitative investigation of three-body effects in any microgel system. The present findings show that, albeit small, the latter contributions are non-negligible and exhibit an intriguing sign reversal: while being predominantly attractive at low temperatures, they become repulsive at high ones, nearly canceling out at the VPT. These results reveal a subtle interplay between microgel architecture, temperature and many-body correlations, with direct implications on the design of responsive soft materials and their mutual interactions.

en: Numerical investigation of realistic core-shell microgels: insights on two- and three-body effective interactions

coarse-grained-models

display_name: coarse-grained-models

core-shell-microgels

display_name: core-shell-microgels

effective-interaction

display_name: effective-interaction

many-body-effects

display_name: many-body-effects

molecular-simulation

display_name: molecular-simulation


envi

en: Environment


en: The intrinsic stress in nanomultilayers (NMLs) is typically dominated by interface stress, which is particularly high in immiscible Cu/W NMLs. Here, atomistic simulations with a chemically-accurate neural network potential reveal the role of interfacial intermixing and metastable phase formation on the interface stress levels.  These results rationalize an experimentally-reported compressive-to-tensile transition as a function of NML deposition conditions and the extremely high interface stresses under some conditions.

en: Uncovering the origin of interface stress enhancement and compressive-to-tensile stress transition in immiscible nanomultilayers

cuw-nanomultilayers

display_name: cuw-nanomultilayers

interface-stress

display_name: interface-stress

intermixing

display_name: intermixing

metastable-phases

display_name: metastable-phases

molecular-statics

display_name: molecular-statics


en: Microgels made of poly(N-isopropylacrylamide) are the prototype of soft, thermoresponsive particles widely used to study fundamental problems in condensed matter physics. However, their internal structure is far from homogeneous, and existing mean-field approaches, such as Flory–Rehner theory, provide only qualitative descriptions of their thermoresponsive behavior. Here, we combine machine learning and numerical simulations to accurately predict the concentration and spatial distribution of crosslinkers, the latter hitherto unknown experimentally, as well as the full swelling behavior of microgels, using only polymer density profiles. Our approach provides unprecedented insight into the structural and thermodynamic properties of any standard microgel.

en: Predicting the structure and swelling of microgels with different crosslinker concentrations by combining machine learning with numerical simulations

machine-learning

display_name: machine-learning

microgels

display_name: microgels

volume-phase-transition

display_name: volume-phase-transition


en: Spin-wave excitations are fundamental to understanding the behavior of magnetic materials and hold promise for future information and communication technologies. Yet, modeling these accurately in transition-metal compounds remains challenging, starting from the self-interaction errors affecting localized and partially filled $d$-orbitals in density-functional theory (DFT) with (semi-)local functionals. In this work, we compare three advanced first-principles approaches for computing magnetic exchange parameters and magnon dispersions in NiO and MnO, all based on a common DFT+$U$ ground state with ab initio Hubbard $U$ values obtained from density-functional perturbation theory. Two methods extract exchange parameters directly: one via total-energy differences using the four-state mapping ($\Delta E$), and the other via the magnetic force theorem (MFT) using infinitesimal spin rotations. Magnon dispersions are then obtained from a Heisenberg Hamiltonian through linear spin-wave theory (LSWT). The third approach, time-dependent density-functional perturbation theory with $U$ (TDDFPT+$U$), yields magnon dispersions directly from the dynamical spin susceptibility, with exchange parameters fitted a posteriori, for comparison, via LSWT. Our results show that TDDFPT+$U$ and the Heisenberg model based on $\Delta E$-derived parameters align well with experimental neutron scattering data, whereas the MFT-based approach shows larger discrepancies, possibly due to some inherent approximations and limitations of the particular implementation used. This study benchmarks the accuracy of state-of-the-art first-principles techniques for spin-wave modeling and contributes to advancing reliable computational tools for the study and design of magnetic materials.

en: Comparative study of magnetic exchange parameters and magnon dispersions in NiO and MnO from first principles

dftu

display_name: dftu

four-state-mapping-analysis

display_name: four-state-mapping-analysis

heisenberg-j

display_name: heisenberg-j

linear-spin-wave-theory

display_name: linear-spin-wave-theory

magnetic-force-theorem

display_name: magnetic-force-theorem

magnon-dispersion

display_name: magnon-dispersion

magnons

display_name: magnons

mno

display_name: mno

nio

display_name: nio

quantum-espresso

display_name: quantum-espresso

spin-waves

display_name: spin-waves


en: Understanding how oxide nanomorphology directs metal–support interactions is key to designing selective, low-cost catalysts. The preferential oxidation of CO (CO-PROX) is vital for purifying H₂ streams for fuel cell applications, as even trace CO strongly poisons the electrode catalysts. Cu/CeO2 systems provide a cost-effective alternative to noble metals, yet the influence of ceria morphology on performance remains unclear. Here, we compare low-loaded Cu catalysts supported on CeO2 nanospheres and nanocubes. Although distinct in shape, electron microscopy, low-temperature CO adsorption infrared spectroscopy, and DFT calculations reveal surface reconstructions in nanocubes that diminish structural differences between the two supports. Nevertheless, the Cu/nanosphere catalyst shows higher CO oxidation activity, while the Cu/nanocube catalyst offers superior CO₂ selectivity and a broader full-conversion temperature window. In situ DRIFTS and DFT attribute these contrasts to stronger CO adsorption sites in the nanocube system. Copper speciation and the nature of surface carbonyls were resolved through complementary techniques including STEM-HAADF imaging, XEDS mapping, EPR, and CO adsorption IR spectroscopy, together with DFT. These results demonstrate that subtle variations in ceria morphology steer interfacial chemistry and reaction pathways, providing design principles for next-generation Cu-based catalysts for CO-PROX and related oxidation reactions.

en: Fine-tuning catalysts: The role of support nanomorphology in shaping Cu/CeO2 CO-PROX properties

carbonyls-infrared

display_name: carbonyls-infrared

co-prox

display_name: co-prox

dft

display_name: dft

electron-microscopy

display_name: electron-microscopy

hydrogen

display_name: hydrogen


en: This dataset contains the optimized structures of hole polarons in anatase and rutile TiO₂, calculated using the DFT+U and PBE0(α) functionals, with parameters tuned to satisfy the piecewise linearity condition. It also includes hole polaron migration paths in anatase and rutile, optimized using the nudged elastic band method at the DFT+U level. Additionally, pristine TiO₂ structures and an example input file for Quantum ESPRESSO are provided.

en: Migration of hole polarons in anatase and rutile TiO₂ through piecewise-linear functionals

anatase

display_name: anatase

hopping

display_name: hopping

polaron

display_name: polaron

rutile

display_name: rutile

semiconductor

display_name: semiconductor


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).


en: This dataset accompanies the study "Getting the manifold right: The crucial role of orbital resolution in DFT+U for mixed d-f electron compounds" and provides input and output files used to investigate the electronic structure of AUO4 compunds (A = Ni, Mn, Co). The calculations systematically compare standard PBEsol density-functional theory (DFT), shell-averaged DFT+U, and orbital-resolved DFT+U approaches based on orthogonalized atomic orbital (OAO) projectors. The dataset includes self-consistent-field calculations, geometry optimizations, and projected density of states data. In addition, hybrid-functional HSE06 calculations performed with VASP are provided for benchmarking purposes. A detailed molecular orbital and bonding analysis for NiUO4, carried out using the LOBSTER code, is also included to elucidate orbital hybridization and chemical bonding effects.

actinide-materials

display_name: actinide-materials

dftu

display_name: dftu

electronic-structure

display_name: electronic-structure

orbital-resolved

display_name: orbital-resolved


en: Getting the manifold right: The crucial role of orbital resolution in DFT+U for mixed d-f electron compounds


en:
Molecular dynamics (MD) provides insights into atomic-scale processes by integrating over time the equations that describe the motion of atoms under the action of interatomic forces. Machine learning models have substantially accelerated MD by providing inexpensive predictions of the forces, but they remain constrained to minuscule time integration steps, which are required by the fast time scale of atomic motion. In this work, we propose FlashMD, a method to predict the evolution of positions and momenta over strides that are between one and two orders of magnitude longer than typical MD time steps. We incorporate considerations on the mathematical and physical properties of Hamiltonian dynamics in the architecture, generalize the approach to allow the simulation of any thermodynamic ensemble, and carefully assess the possible failure modes of such a long-stride MD approach. We validate FlashMD's accuracy in reproducing equilibrium and time-dependent properties, using both system-specific and general-purpose models, extending the ability of MD simulation to reach the long time scales needed to model microscopic processes of high scientific and technological relevance.

This repository contains the scripts used for the training and analysis of the FlashMD models presented in the associated paper/preprint.

materialscloud

en: Materials Cloud

en: FlashMD: long-stride, universal prediction of molecular dynamics

atomistic-machine-learning

display_name: atomistic-machine-learning

long-stride-simulations

display_name: long-stride-simulations

ml-accerelated-simulations

display_name: ml-accerelated-simulations

molecular-dynamics

display_name: molecular-dynamics


envi

en: Environment


en: Understanding electrical resistivity in metals remains a central challenge in quantifying charge transport at finite temperature. Current first-principles calculations based on the Boltzmann transport equation often match experiments, yet they almost always neglect the effect of thermal expansion and phonon anharmonicity. We show that both effects exert an opposite impact on electron–phonon coupling and on electrical resistivity. Thermal expansion enhances the coupling and leads to overestimation of resistivity, whereas anharmonic effects reduce it. By explicitly incorporating both effects, we establish a more complete description of resistivity in elemental metals, demonstrated here for Pb, Nb, and Al. This archive contains all our input, output files and the data.

en: Antagonistic impact of thermal expansion and phonon anharmonicity on the phonon-limited resistivity of elemental metals from first principles

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

transport

display_name: transport


en: Opposite impact of thermal expansion and phonon anharmonicity on the phonon-limited resistivity of elemental metals from first principles


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

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

atomistic-simulations

display_name: atomistic-simulations

dataset

display_name: dataset

density-functional-theory

display_name: density-functional-theory

interatomic-potentials

display_name: interatomic-potentials

machine-learning

display_name: machine-learning

universal-models

display_name: universal-models


en: Maximally-localized Wannier functions (MLWFs) are widely employed as an essential tool for calculating the physical properties of materials due to their localized nature and computational efficiency. Projectability-disentangled Wannier functions (PDWFs) have recently emerged as a reliable and efficient approach for automatically constructing MLWFs that span both occupied and lowest unoccupied bands. Here, we extend the applicability of PDWFs to magnetic systems and/or those including spin-orbit coupling, and implement such extensions in automated workflows. Furthermore, we enhance the robustness and reliability of constructing PDWFs by defining an extended protocol that automatically expands the projectors manifold, when required, by introducing additional appropriate hydrogenic atomic orbitals. We benchmark our extended protocol on a set of 200 chemically diverse materials, as well as on the 40 systems with the largest band distance obtained with the standard PDWF approach, showing that on our test set the present approach delivers a success rate of over 98% in obtaining accurate Wannier-function interpolations, defined as an average band distance below 20 meV between the DFT and Wannier-interpolated bands, up to 2 eV above the Fermi level for metals or above the conduction band minimum for insulators (and a 100% success rate when including only bands up to 1 eV above these values).

en: Robust Wannierization including magnetization and spin-orbit coupling via projectability disentanglement

aiida

display_name: aiida

automation

display_name: automation

marvel

display_name: marvel

projectability-disentanglement

display_name: projectability-disentanglement

snsf

display_name: snsf

wannier-functions

display_name: wannier-functions

wannierization

display_name: wannierization


en: Here we present an on-demand strategy for electrolytes design to surpass 99.9% Coulombic efficiency (CE) in zinc metal anode. This strategy synergizes various effects by specifically targeting the two critical factors: plating morphology and the anode-electrolyte interface.  In this dataset, we simulated the solvation structures and bilayer structures of various electrolytes by molecular dynamics simulations. We found Triethyl phosphate and dimethylformamide can  induce the free-water-poor inner Helmholtz plane and reduce the interfacial water activity. Furthermore, the MD results imply that the dual-salt introduces more anions into the Zn2+ primary solvation sheath, and the DMF co-solvent is also able to enter the solvation sheath.

en: Electrolyte design for reversible zinc metal chemistry

batteries

display_name: batteries

electrolyte

display_name: electrolyte

molecular-dynamics

display_name: molecular-dynamics