en: Chalcogenide perovskites have emerged as a promising class of materials for the next generation of optoelectronic applications, with BaZrS3 attracting significant attention due to its wide bandgap, earth-abundant composition, and thermal and chemical stability. However, previous studies have consistently reported weak and ambiguous photoluminescence (PL), regardless of synthesis method, raising questions about the intrinsic optoelectronic quality of this compound. In this work, strong, band-to-band-dominated PL is demonstrated at room temperature in high-quality BaZrS3 single crystals, with a PL quantum yield of ∼0.005%. Despite the narrow, single-component PL emission band, time-resolved PL measurements reveal a carrier lifetime of 1.0 ± 0.2 ns. To understand the origin of the strong PL and short carrier lifetime, multiwavelength excitation and polarization-dependent Raman measurements are performed, supported by first-principles lattice dynamics calculations. All 23 theoretically predicted Raman-active modes and their symmetries are identified, providing a comprehensive reference for future studies. These results indicate that phonon-assisted carrier decay and nontrivial electron-phonon interactions contribute to the short carrier lifetimes, as evidenced by Raman spectroscopy and DFT calculations. Further studies on compositional variations or partial cation/anion substitutions can mitigate electron-phonon coupling and enhance carrier lifetimes. By establishing a detailed reference for the intrinsic vibrational and optoelectronic properties of BaZrS3, this work paves the way for further advancements in chalcogenide perovskites for energy and optoelectronic technologies.

en: BaZrS3 lights up: the interplay of electrons, photons, and phonons in strongly luminescent single crystals

envi

en: Environment

chalcogenide-perovskite

display_name: chalcogenide-perovskite

electron-phonon-coupling

display_name: electron-phonon-coupling

optoelectronic

display_name: optoelectronic

raman-spectroscopy

display_name: raman-spectroscopy


$oid: OBSOLETE

en: This dataset contains experimental and theoretical results from the study of heat transport in suspended single-crystal diamond cantilevers with sub-micrometer widths. "Imaging heat transport in suspended diamond nanostructures with integrated spindefect thermometers", Physical Review Letters, 2026Temperature profiles are measured using embedded nitrogen-vacancy (NV) spin defects acting as in-situ thermometers, enabling spatially resolved thermal mapping under laser heating. The data include measured temperature gradients, extracted effective thermal conductivities as a function of cantilever width, and first-principles simulations based on the linearized phonon Boltzmann transport equation and viscous heat equations. The combined experimental and computational results demonstrate non-diffusive, sub-ballistic heat transport arising from the interplay of intrinsic phonon scattering and boundary effects.

en: Imaging heat transport in suspended diamond nanostructures with integrated spin defect thermometers

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

fr: Environnement

it: Ambiente

diamond-nanostructures

display_name: diamond-nanostructures

heat-transport

display_name: heat-transport

nanoscale-thermometry

display_name: nanoscale-thermometry

nitrogen-vacancy-centers

display_name: nitrogen-vacancy-centers

non-diffusive-thermal-conduction

display_name: non-diffusive-thermal-conduction

phonon-boltzmann-transport-equation

display_name: phonon-boltzmann-transport-equation

viscous-heat-equations

display_name: viscous-heat-equations


en: Skutterudites are promising materials for thermoelectric and spintronics applications. Here we explore spin fluctuations in the FeSb3 skutterudite and their effect on its electronic structure using Hubbard-corrected density-functional theory calculations. We identify multiple magnetic and charge-disproportionated configurations, with an antiferromagnetic metallic ground state. Paramagnetic fluctuations modeled through a special quasirandom spin structure open a 61 meV gap, consistent with experiments. This state features non-degenerate spin channels and band-avoided crossings, hinting at a potential altermagnetic transition with topological features. Mapping the electronic structure to a Heisenberg Hamiltonian fails to explain the low Néel temperature (10 K), highlighting the role of magnetic exchange frustration and the need for more in-depth experimental investigations.This dataset contains first-principles calculations supporting the study “Spin fluctuations steer the electronic behavior in the FeSb₃ skutterudite”. It includes linear-response Hubbard parameters, phonon dispersions, electronic band structures, special quasirandom structures modeling of paramagnetism, and magnetic exchange interactions for FeSb₃. The data document how spin fluctuations stabilize the structure, open a small electronic gap, and strongly affect magnetic energetics.

en: Spin fluctuations steer the electronic behavior in the FeSb3 skutterudite

condensed-matter-physics

display_name: condensed-matter-physics

first-principles-simulations

display_name: first-principles-simulations

heisenberg-model

display_name: heisenberg-model

magnetism

display_name: magnetism

paramagnetism

display_name: paramagnetism

special-quasi-random-structures

display_name: special-quasi-random-structures

spin-fluctuations

display_name: spin-fluctuations


en: Defect engineering in two-dimensional semiconductors has been exploited to tune the optoelectronic properties and introduce new quantum states in the band gap. Chalcogen vacancies in transition metal dichalcogenides in particular have been found to strongly impact charge carrier concentration and mobility in 2D transistors as well as feature subgap emission and single-photon response. In this Letter, we investigate the layer-dependent charge-state lifetime of Se vacancies in WSe2. In one monolayer WSe2,we observe ultrafast charge transfer from the lowest unoccupied orbital of the top Se vacancy to the graphene substrate within 1 ± 0.2 ps measured via the current saturation in scanning tunneling approach curves. For Se vacancies decoupled by transition metal dichalcogenide (TMD) multilayers, we find a subexponential increase of the charge lifetime from 62 ±14 ps in bilayer to a few nanoseconds in four-layer WSe2, alongside a reduction of the defect state binding energy. Additionally, we attribute the continuous suppression and energy shift of the dI=dV in-gap defect state resonances at very close tip-sample distances to a current saturation effect. Our results provide a key measure of the layer-dependent charge transfer rate of chalcogen vacancies in TMDs.

en: Layer-dependent charge-state lifetime of single Se vacancies in WSe2

defect-engineering

display_name: defect-engineering

scanning-tunneling-microscopy-stm

display_name: scanning-tunneling-microscopy-stm

transition-metal-dichalcogenide-tmd

display_name: transition-metal-dichalcogenide-tmd


en: The origin of the metal-to-insulator transition (MIT) in RNiO3 perovskites with R = trivalent 4 f ion has challenged the condensed matter research community for almost three decades. A drawback for progress in this direction has been the lack of studies combining physical properties and accurate structural data covering the full nickelate phase diagram. Here we focus on a small region close to the itinerant limit (R = Pr, 1.5 K < T < 300 K), where we investigate the gap opening and the simultaneous emergence of charge order in PrNiO3. We combine electrical resistivity, magnetization, and heat capacity measurements with high-resolution neutron and synchrotron x-ray powder diffraction data that, in contrast to previous studies, we analyze in terms of symmetry-adapted distortion modes. Such analysis allow us to identify the contribution of the different modes to the global distortion in a broad temperature range. Moreover, it shows that the structural changes at the MIT, traditionally described in terms of the evolution of the interatomic distances and angles, appear as abrupt increases of all nonzero mode amplitudes at TMIT = TN ∼ 130 K accompanied by the appearance of new modes below this temperature. A further interesting observation is the existence of a nearly perfect linear correlation between the amplitude of the breathing mode associated to the charge order and the staggered magnetization below the MIT. Our data also uncover a previously unnoticed anomaly at T ∗ ∼ 60 K (∼0.4 × TMIT), clearly visible in the electrical resistivity, lattice parameters and some mode amplitudes. Since phase coexistence is only observed in a small temperature region around TMIT (∼±10 K), these observations suggest the existence of a hidden symmetry in the insulating phase. We discuss some possible origins, among them the theoretically predicted existence of polar distortions induced by the noncentrosymmetric magnetic order [Perez-Mato et al., J. Phys.: Condens. Matter 28, 286001 (2016); Giovanetti et al., Phys. Rev. Lett. 103, 156401 (2009)].

en: Distortion mode anomalies in bulk PrNiO3: Illustrating the potential of symmetry-adapted distortion mode analysis for the study of phase transitions

distortion-mode-amplitudes

display_name: distortion-mode-amplitudes

metal-insulator-transition

display_name: metal-insulator-transition

neutron-diffraction

display_name: neutron-diffraction

physical-properties

display_name: physical-properties

synchrotron-x-ray-diffraction

display_name: synchrotron-x-ray-diffraction


en:
If one carries out a molecular simulation of N particles using periodic boundary conditions, linear momentum is conserved and hence the number of degrees of freedom is set to 3N-3. In most programs, this number of degrees of freedom is the default setting. However, if one carries out a molecular simulation in an external field, one needs to ensure that degrees of freedom are changed from this default setting to 3N, as in an external field the velocity of the center of mass can change. Using the correct degrees of freedom is important in calculating the temperature and in some algorithms to simulate at constant temperature.
For sufficiently large systems, the difference between 3N and 3N-3 is negligible in the way. However, there are systems in which the comparison with experimental data requires molecular dynamics simulations of a small number of particles. In this work, we illustrate the effect of an incorrect setting of degrees of freedom in molecular dynamic simulations studying the diffusion properties of guest molecules in nanoporous materials. We show that previously published results have reported a surprising diffusion dependence on the loading, which could be traced back to an incorrect setting of the degrees of freedom. As the correct settings are convoluted and counter-intuitive in some of the most commonly used molecular dynamics programs, we carried out a systematic study on the consequences of the various commonly used (incorrect) settings. Our conclusion is that for systems smaller than 50 particles the results are most likely unreliable as these are either performed at an incorrect temperature or the temperature is incorrectly used in some of the results.
Furthermore, a novel and efficient method to calculate diffusion coefficients of guest molecules into nanoporous materials at zero loading conditions is introduced.

en: On the effects of the degrees of freedom on calculating diffusion properties in nanoporous materials

diffusion

display_name: diffusion

marvel

display_name: marvel

metal-organic-frameworks

display_name: metal-organic-frameworks

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

nanoporous-materials

display_name: nanoporous-materials

prace

display_name: prace

snsf

display_name: snsf

zeolites

display_name: zeolites


en: Cu(I)-containing oxide semiconductors containing early transition-metal cations have been of growing interest as small bandgap semiconductors. Though, their lack of thermodynamic stability has slowed the progress of their synthetic discovery. Two new Cu(I)-molybdovanadates and one new Cu(I)-tungstovanadate have been discovered in high temperature reactions and their structures characterized using single crystal X-ray diffraction. Their chemical compositions are given by CuVMoO6, Cu1.51(1)V3.57(1)Mo0.43(1)O11 and Cu1.59(1)V3.52(1)W0.48(1)O11. The latter two structures possess a Cu site with ~25% occupancy, whereas all structures show a statistical disorder of V/Mo or V/W cations over equivalent crystallographic sites. Using a supercell approach, density functional theory calculations have been performed to understand their thermodynamic stabilities and their relationships to their band structures and densities-of-states (DOS). Results of the calculations showed lower total energies are attained when the partially-filled Cu sites are oxygen-bridged to the V atoms rather than the Mo/W atoms. Results of their DOS and band structures were also found to provide deeper insights into their physical properties as ternary, Cu(I)-containing oxides.

en: Electronic structure calculations of Cu(I) molybdovanadate and tungstovanadate compounds

bulk-structures

display_name: bulk-structures

copper-oxides

display_name: copper-oxides

electronic-structure

display_name: electronic-structure


en: Electron-phonon coupling is a key interaction that governs diverse physical processes such as carrier transport, superconductivity, and optical absorption. Calculating such interactions from first-principles with methods beyond density-functional theory  remains a challenge. We introduce here a finite-difference framework for computing electron-phonon couplings for any electronic structure method that provides eigenvalues and eigenvectors, and showcase applications for hybrid and Koopmans functionals, and $GW$ many-body perturbation theory. Our approach introduces a novel projectability scheme based on eigenvalue differences and bypasses many of the limitations of the direct finite difference methods. It also leverages symmetries to reduce the number of independent atomic displacements, thereby keeping computational costs manageable. This approach enables seamless integration with established first-principles codes for generating displaced supercells, performing Wannier interpolations, and evaluating transport properties. Applications to silicon and gallium arsenide show that advanced electronic-structure functionals predict different electron-phonon couplings and modify band curvatures, resulting in much more accurate estimates of intrinsic carrier drift mobilities and effective masses. In general, our method provides a robust and accessible framework for exploring electron-phonon interactions in complex materials with state-of-the-art electronic structure methods.

en: Carrier mobilities and electron-phonon interactions beyond DFT

beyond-dft-methods

display_name: beyond-dft-methods

boltzmann-transport-equation

display_name: boltzmann-transport-equation

electron-phonon-coupling

display_name: electron-phonon-coupling

koopmans-functional

display_name: koopmans-functional


en: We investigate the absorption spectra of the F centers in MgO and CaO using material-specific hybrid functionals combined with time-dependent hybrid-functional theory calculations. Our study accounts for electron-hole interaction, zero-phonon renormalization, and finite-size effects, which are all found to contribute to the achieved accuracy. The spectra of the neutral states solely present absorption lines stemming from internal defect-to-defect transitions without involving conduction band states of the host. At variance, the spectra of the singly positively charged states show two lines, one analogous to those seen in the neutral states and an extra line corresponding to electron excitations from the valence band to the unoccupied defect level in the band gap. In MgO, the three transition energies pertaining to these two charged states are found to coincide, but are spread out in the case of CaO. The agreement with the energies of experimental lines is excellent, indicating that our scheme reaches an accuracy of 0.2 eV or better.

en: Absorption lines of F centers in MgO and CaO through time-dependent hybrid-functional theory calculations

envi

en: Environment

defect

display_name: defect

dft

display_name: dft

oxide

display_name: oxide

time-dependent-dft

display_name: time-dependent-dft


en: Cu(In,Ga)Se2 (CIGSe) is a highly efficient absorber material in thin-film solar cells that demonstrates a unique ability to tolerate massive Cu deficiency, which is often manifested in the formation of ordered defect compounds (ODCs). The conventional explanation is that the off-stoichiometry is mediated by the charge-neutral complexes forming in high concentrations. In this work, we refute this simplistic picture by treating the off-stoichiometry as an integral part of the crystal structure. We discover a topotactic transformation series of stable ODC-like structures with different compositions that enable large Cu deficiency in the material while being practically invisible to X-ray and neutron diffraction. These structures reproduce the known band gap widening and allow reconcile several lasting conflicts between experimental findings and first-principles calculations. This dataset contains the PBEsol-optimized primitive cells of the near-stable ODC structures in the topotactic series.

en: Functional off-stoichiometry in Cu(In,Ga)Se2

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).

copper-deficiency

display_name: copper-deficiency

ordered-vacancy-compounds

display_name: ordered-vacancy-compounds

topotactic-transformation

display_name: topotactic-transformation

vacancy-arrangements

display_name: vacancy-arrangements


en: The limited choice of oxygen evolution reaction catalysts for proton exchange membrane water electrolyzers hinders their large-scale commercialization. Cobalt-based catalysts are promising candidates and usually undergo surface reconstruction into CoOOH-like structures. Despite this, the directly synthesized CoOOH has not yet been investigated in acidic environments. Here, we show that the CoOOH is active across the whole pH range, while its redox features is pH dependent. Operando hard X-ray absorption spectroscopy characterizations show a pH-induced change Co oxidation onset, but no change in the coverage of redox-active Co species before the oxygen evolution reaction. The pH-dependent catalytic performance is connected to the interfacial Co oxidative transformations under OER conditions. Combining the kinetic isotope effect and the apparent activation energy with theoretical verification, we provide the mechanistic insight on the possible OER pathway for CoOOH. In addition, CoOOH demonstrate a stable cell potential at 100 mA cm-2 for 400 hours in a proton exchange membrane water electrolyzer. These results provide insight into both the fundamental electrochemical properties of CoOOH and its potential for practical device performance.

en: Directly synthesized cobalt oxyhydroxide as an oxygen evolution catalyst in proton exchange membrane water electrolyzers

materialscloud

en: Materials Cloud

coooh

display_name: coooh

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction


en: Metal organic frameworks (MOFs) have a promising perspective as oxygen evolution reaction (OER) electrocatalysts due to their high surface areas and tunable structures. However, one of the main challenges for their further application is inferior stability during alkaline OER. Herein, operando x-ray absorption spectroscopy and operando x-ray diffraction of NiCo-MOF-74 materials unveil their electrochemical transformations differentiating between electrolyte-induced, beam-induced, and electrochemically induced changes of the electronic state and local structure around the transition metal centers in addition to their overall crystal structure. An inferior electrolyte- and beam stability of Co-MOF-74 is revealed in comparison to a more stable performance of Ni-MOF-74 and Ni0.25Co0.75-MOF-74. Based on the operando measurement results, good experimental practices for future MOF OER electrocatalyst studies are presented.

en: Operando X-ray absorption and diffraction investigations on Ni- and Co-MOF-74 oxygen evolution electrocatalysts

chemical-stability

display_name: chemical-stability

operando-x-ray-absorption-spectroscopy

display_name: operando-x-ray-absorption-spectroscopy

operando-x-ray-diffraction

display_name: operando-x-ray-diffraction

radiation-stability

display_name: radiation-stability

stability-of-mofs

display_name: stability-of-mofs


en: The behavior of charge carriers in polar materials is governed by electron-phonon interactions, which affect their mobilities via phonon scattering and may localize carriers into self-induced deformation fields, forming self-trapped polarons. We present a first-principles study of self-trapped polaron formation in paradigmatic polar semiconductors and insulators using the variational polaron equations framework and self-consistent gradient optimization. Our method incorporates long-range corrections to the electron-phonon interaction, essential for finite-size systems. We demonstrate how the variational approach enables the identification of multiple polaronic states and supports the analysis of polarons with arbitrarily large spatial extent via energy filtering. The potential energy surfaces of the resulting polarons exhibit multiple local minima, reflecting distinct, symmetry-broken polaronic configurations in systems with degenerate band edges. Our findings align with previous theoretical studies and establish a robust foundation for future ab initio studies of polarons, especially those employing variational methods.This archive contains the data necessary to reproduce the results of the "Variational first-principles approach to self-trapped polarons" article by Vasilii Vasilchenko, Matteo Giantomassi, Samuel Poncé, and Xavier Gonze.

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles-calculations

display_name: first-principles-calculations

polarons

display_name: polarons

variational-wave-functional-methods

display_name: variational-wave-functional-methods


en: The direct conversion of methane-to-methanol remains a critical challenge in methane valorization. In this study, we unveil the crucial role of PdAu/CeO2 catalysts in enabling selective methane transformation under mild conditions, using only water as the sole oxidant. Through a combination of experimental techniques, including XPS and catalytic testing, alongside density functional theory (DFT) calculations, we demonstrate that a Pd0.3Au0.7/CeO2 catalyst, which predominantly exposes isolated Pd atoms, achieves remarkable methanol selectivity (∼80%) at 500 K with a 1:1 methaneto-water ratio. While Pd/CeO2 efficiently activates methane, its tendency for overreaction leads to complete methanol decomposition, thereby limiting selectivity. Alloying Pd withAu on ceria mitigates this over-reactivity, preventing methanol degradation while maintaining sufficient catalytic activity. The PdAu/CeO2 composite exhibits a synergistic effect: Pd in contact with the ceria support facilitates methane activation and water dissociation, while Au fine-tunes reactivity to promote methanol formation. DFT calculations confirm that isolated Pd sites at the PdAu/CeO2 interface play a key role in balancing activity and selectivity. This work underscores the importance of alloy/oxide interfaces in controlling selective methane conversion with water and offers valuable insights for designing highly efficient catalysts for methanol synthesis.

en: Engineering PdAu/CeO2 alloy/oxide interfaces for selective methane-to-methanol conversion with water

alloy

display_name: alloy

bimetallic

display_name: bimetallic

ceria

display_name: ceria

computational-chemistry

display_name: computational-chemistry

dft

display_name: dft

gofee

display_name: gofee

gold

display_name: gold

heterogeneous-catalysis

display_name: heterogeneous-catalysis

methane-to-methanol

display_name: methane-to-methanol

nanoparticle

display_name: nanoparticle

palladium

display_name: palladium


en: Two-dimensional topological insulators are characterized by an insulating bulk and conductive edge states protected by the nontrivial topology of the bulk electronic structure. They remain robust against moderate disorder until Anderson localization occurs and destroys the topological phase. Interestingly, disorder can also induce a topological phase—known as a topological Anderson insulator—starting from an otherwise pristine trivial phase. While topological invariants are generally regarded as global quantities, we argue that space-resolved topological markers can act as local order parameters, revealing the role of fluctuations and correlations in the local topology under Anderson disorder and vacancies. With this perspective, we perform numerical simulations of disorder-driven topological phase transitions in the Haldane and Kane-Mele models, using supercells with both open and periodic boundary conditions. We find that short-scale fluctuations of topological markers vanish upon coarse-graining, except at the topological phase transition, where their correlation length peaks and large-scale fluctuations remain. Notably, such a topological correlation function is characterized by critical exponents that appear universal across disorder types, yet they can resolve different topological phase transitions.

en: Fluctuations and correlations of local topological order parameters in disordered two-dimensional topological insulators

anderson-disorder

display_name: anderson-disorder

critical-exponents

display_name: critical-exponents

fluctuations-and-correlations

display_name: fluctuations-and-correlations

haldane-model

display_name: haldane-model

kane-mele-model

display_name: kane-mele-model

local-topology

display_name: local-topology

topological-order-parameters

display_name: topological-order-parameters

vacancies

display_name: vacancies


en: Polymers are versatile materials with a wide range of applications. The improvement of polymer properties rises the importance on the way that the repeating units are connected (head-to-tail,head-to-head,tail-to-tail) to build the polymer structure since it directly influences the morphology, chain topology and consequently its properties. Artificial intelligence (AI) based approaches are beginning to impact several domains of human life, science and technology. Polymer informatics is one such domain where AI and machine learning (ML) tools are being used in the efficient development, design and discovery of polymer. One key enabling factor for the essential foundations for Polymer Informatics is the machine-readable polymer representation. Polymer have been represented in a string format with special characters used to tag the head and tail positions indicating where the linking bond happens between repeat units. Available tools to assign the head and tail position limits its applicability in a broad sense. In this work we show a new tool to assign the head and tail atoms for a given monomer. From a database of 206 polymer precursors curated from the literature, our algorithm correctly predicted the class of 201 data points, which represents 97.6% of accuracy and regarding the the head and tail assignment, correctly assigned the positions for 188 data points, which translates to 91.3% of accuracy.

en: HTA - An open-source software for assigning heads and tails to SMILES in polymerization reactions

head-and-tail-atom-position

display_name: head-and-tail-atom-position

polymer

display_name: polymer

repeat-unit

display_name: repeat-unit


en:
Water inherently contains trace amounts of various salts, yet the microscopic processes by which salts influence some of its physical properties remain elusive. Notably, the mechanisms that reduce the dielectric constant of water upon salt addition are still debated. The primary absorption peak for electromagnetic radiation-commonly used in microwave heating-shifts towards higher frequencies in saline solutions, suggesting faster water molecular dynamics. This observation, however, contrasts with the simultaneous increase in viscosity and experimental reports that ionic solutes would slow down water molecular motion. In this work, we use molecular dynamics (MD) simulations with deep-neural-network models trained on high-quality quantum mechanical data to mimic interatomic forces and molecular dipoles, to compute the dielectric spectra of perchlorate water saline solution, which may be relevant to the recent discovery of liquid water beneath the thick ice crust at Mars’s south pole. Our results reveal that both the reduction in the dielectric constant and the absorption peak shift can be attributed to ion-induced changes in the orientational ordering of water molecules. Additionally, we demonstrate that the self-part of the molecular dipole-dipole correlation function reveals clear signatures of the slowing dynamics within the first cationic solvation shell, consistent with the experimentally observed increase in viscosity.

This record contains the DeepMD neural network potential and the underlying training data as well as the neural network for prediciting Wannier centroids and the underlying training data.

en: How salt solvation slows water dynamics while blue-shifting its dielectric spectrum

deepmd

display_name: deepmd

max

display_name: max

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

neural-network-potential

display_name: neural-network-potential

salt

display_name: salt

water

display_name: water


en: Thermal transport in low-dimensional materials is of great fundamental and applied interest due to their unusual and widely tunable properties, as demonstrated by numerous studies of two- and one-dimensional (1D) crystals. In grim contrast, low-dimensional amorphous materials remain largely unexplored, despite their potentially unique characteristics and applications. Here, we theoretically explore thermal transport in quasi-1D single-walled amorphous carbon nanotubes (a-CNTs) by combining homogeneous nonequilibrium molecular dynamics and lattice dynamics, based on a custom-trained high-accuracy machine-learned potential. For both zigzag and armchair a-CNTs of different diameters, the quantum-corrected thermal conductivity (κ) consistently drops by about an order of magnitude as the degree of disorder increases from 0.01 to 0.08, which is quantified by the concentration of Stone-Wales defects. Spectral analysis reveals the predominant role of the low-frequency (<3 THz) vibrational modes, the mean free paths (MFPs) of which are long but finite, thus leading to converged κ as the tube length reaches several micrometers. Further, lattice dynamics calculations yield much larger MFPs for the longitudinal acoustic modes compared to the transverse acoustic modes, which is primarily attributed to the distinctly higher group velocity of the former. Moreover, the κ suppression appears to be dictated by the structural disorder while anharmonicity barely contributes. Our work expands the understanding of thermal transport in low-dimensional amorphous materials and may help promote their eventual application. This record contains data and scripts that support the findings of this article.

en: Thermal transport in amorphous carbon nanotubes

envi

en: Environment

amorphous-carbon-nanotube

display_name: amorphous-carbon-nanotube

lattice-dynamics

display_name: lattice-dynamics

machine-learned-potential

display_name: machine-learned-potential

molecular-dynamics

display_name: molecular-dynamics

thermal-transport

display_name: thermal-transport


en: Spinodal decomposition is a phenomenon involving spontaneous phase separation in a solid alloy. It is often considered as the prototype of second order phase transitions and is known for the formation of non-trivial patterns. In the presence of a lattice mismatch f between the components, even more complex, qualitatively different patterns emerge, depending on the specific f values. Despite being interesting in practical and theoretical settings, modeling this class of phenomena may be hindered by computational costs. Lately, applications of Machine Learning (ML) promise to mitigate these issues. The availability of suitable datasets is therefore of primary importance for the development of said models. We here provide a collection of phase field simulations of spinodal decomposition involving elastic effects under different mismatch conditions f. These may be used in training a ML model both for the forward problem of predicting the evolution given an initial condition and for the inverse problem of extracting the misfit parameter from a sequence. The dataset is conveniently already divided into training, validation and test sets. The data also support the ML framework, based on a convolutional recurrent scheme, discussed in the related publication in refrences and allows for the full reproduction of the reported findings. Once trained, the neural network is able to accurately reproduce ground-truth evolution even in critical regions of the parameter space (e.g., near the onset of metastability) and predict the misfit parameters to a high degree of accuracy (~0.01% absolute strain).

en: Strained alloy microstructure dataset for ML property prediction and time-evolution simulation

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).

alloy-microstructures

display_name: alloy-microstructures

convolutional-neural-network

display_name: convolutional-neural-network

elastic-field

display_name: elastic-field

lattice-mismatch

display_name: lattice-mismatch

machine-learning

display_name: machine-learning

phase-field-simulations

display_name: phase-field-simulations

recurrent-neural-network

display_name: recurrent-neural-network

spinodal-decomposition

display_name: spinodal-decomposition

surrogate-modeling

display_name: surrogate-modeling


en: Understanding the behavior of materials under irradiation is crucial for the design and safety of nuclear reactors, spacecraft, and other radiation environments. The threshold displacement energy (Ed) is a critical parameter for understanding radiation damage in materials, yet its determination often relies on costly experiments or simulations.This work presents a compilation of threshold displacement energies (Ed) and fundamental material parameters (e.g., density, atomic mass, melting temperature) designed to enable the application of the machine learning-based Sure Independence Screening and Sparsifying Operator (SISSO) method. The goal is to develop accurate, analytical models for predicting Ed based on intrinsic material properties. The models outperform traditional approaches for monoatomic materials, capturing key trends with high accuracy. While predictions for polyatomic materials highlight challenges due to dataset complexity, they reveal opportunities for improvement with expanded data. This study identifies cohesive energy and melting temperature as key factors influencing Ed, offering a robust framework for efficient, data-driven predictions of radiation damage in diverse materials.

en: Machine learning-driven analytical models for threshold displacement energy prediction in materials

materialscloud

en: Materials Cloud

materials

display_name: materials

radiation-damage

display_name: radiation-damage

threshold-displacement-energy

display_name: threshold-displacement-energy


en: Super-exchange most often leads to antiferromagnetism in transition-metal perovskite oxides, yet ferromagnetism or ferrimagnetism would be preferred for many applications, for example in data storage. While alloying, epitaxial strain and defects were shown to lead to ferromagnetism, engineering this magnetic order remains a challenge. We propose, based on density functional theory calculations, a novel route to defect-engineer ferrimagnetism, which is based on preferential displacements of oxygen vacancies due to finite temperature vibrations. This mechanism has an unusual temperature dependence, as it is absent at 0K, strengthens with increasing temperature before vanishing once oxygen vacancies disorder, giving it a unique experimentally detectable signature.

en: Ferrimagnetism induced by thermal vibrations in oxygen-deficient manganite heterostructures

ferrimagnetism

display_name: ferrimagnetism

oxygen-vacancy

display_name: oxygen-vacancy

perovskite-manganite

display_name: perovskite-manganite


en: Lithium thiophosphate (LPS) has demonstrated promising properties for use as a solid electrolyte for the next-generation of lithium ion batteries. However, the high reactivity of LPS with common contaminants such as atmospheric water hinders commercialization of the cells. We employ a machine learning interatomic potential to gain fundamental, atomistic understanding on the mechanical, chemical and electronic properties of an LPS surface. Our focus lies first on the identification of relevant surface complexions formed by surface reconstructions, which differ greatly in properties from the bulk and define the form of the reactive sites. Then, we reveal the complex reactivity of the LPS surface with water and identify the surface moieties that play a relevant role in the material’s degradation. Thus, we uncover the impact of dynamical changes of the surfaces and their electronic structure on the material’s reactivity.This dataset includes all files necessary to reproduce the data reported in Reconstructions and dynamics of 𝛽 -lithium thiophosphate surfaces.This includes the MLIP model and dataset, generated surface structures with certain Miller indices, Wulff constructions, as well the input and result files of LAMMPS and DFT calculations.

deutsche-forschungsgemeinschaft

display_name: deutsche-forschungsgemeinschaft

lps

display_name: lps

machine-learning-interatomic-potentials

display_name: machine-learning-interatomic-potentials

marvel

display_name: marvel

molecular-dynamics

display_name: molecular-dynamics

smoothsoap

display_name: smoothsoap

solid-electrolyte

display_name: solid-electrolyte

surface-complexion

display_name: surface-complexion


en: Fullerides forms a big familiy of molecular crystals exhibiting various useful electro- and magnetochemical properties. Therefore, an efficient in silico method is desirable to look into such chemical system. To alleviate the burden of computationally-heavy first-principles calculations, here we present the successful attempts of using Machine Learning Force Field (MLFF) in predicting dynamical properties of the alkali-doped fulleride K₃C₆₀. Two on-the-fly Gaussian-Process-MLFF schemes based on different atomistic descriptors, Smooth Overlap of Atomic Position (SOAP) and Atomic Cluster Expansion (ACE), have been experimented. The performance of generated K₃C₆₀ MLFFs are validated by accurate prediction on energy and forces of 1,000 randomly disturbed K₃C₆₀ structures compared to respective DFT results. Several other dynamical properties including phonon dispersion, elastic moduli and heat capacity obtained by MLFFs have also shown good agreement with results from DFT calculations, whose computational cost on such post-processing is several times more expensive than training a single MLFF that performs effortless post-processing. This shows the potential of applying MLFF to complex molecular crystal systems and pave the way to the investigation of much more intricate fulleride properties.

en: Efficient modeling of dynamic properties in K₃C₆₀ using machine learning force fields

dynamical-properties

display_name: dynamical-properties

machine-learning-force-field

display_name: machine-learning-force-field

molecular-crystal

display_name: molecular-crystal


en:
This database contains the reference data used for direct force training of Artificial Neural Network (ANN) interatomic potentials using the atomic energy network (ænet) and ænet-PyTorch packages (https://github.com/atomisticnet/aenet-PyTorch). It also includes the GPR-augmented data used for indirect force training via Gaussian Process Regression (GPR) surrogate models using the ænet-GPR package (https://github.com/atomisticnet/aenet-gpr).
Each data file contains atomic structures, energies, and atomic forces in XCrySDen Structure Format (XSF). The dataset includes all reference training/test data and corresponding GPR-augmented data used in the four benchmark examples presented in the reference paper, "Scalable Training of Neural Network Potentials for Complex Interfaces Through Data Augmentation".
A hierarchy of the dataset is described in the README.txt file, and an overview of the dataset is also summarized in supplementary Table S1 of the reference paper.

en: Database of scalable training of neural network potentials for complex interfaces through data augmentation

aenet

display_name: aenet

ann

display_name: ann

first-principles

display_name: first-principles

gpr

display_name: gpr

li-metal-battery

display_name: li-metal-battery

machine-learning

display_name: machine-learning

potential-energy-surface

display_name: potential-energy-surface


en: To investigate charged defects in gapped materials by first-principles calculations, they must be charged by either adding/removing electrons or compensating donors/acceptors. The former approach is more common, but it is not without drawbacks. We tested the latter method for a collection of model systems consisting of charge-compensated point defects in diamond (NV/SiV-centers, substitutional nitrogen/phosphorous/oxygen/sulfur donors, and substitutional boron/beryllium acceptors), comparing the geometrical and electronic properties of the compensated defect pairs with those of the individual defects in charged supercells. We find that the charging by explicit donors/acceptors works well and can be advantageous if properly designed, although interpretation of the results can be challenging. In this archive, we share the final optimized geometries of all studied structural models (in CIF format).

en: Neutral supercells for charged impurities by explicit acceptor/donor compensation - defects in diamond

compensating-defect

display_name: compensating-defect

jellium-counter-charge

display_name: jellium-counter-charge

nv-center

display_name: nv-center

siv-center

display_name: siv-center


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


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

envi

en: Environment

nanoparticles

display_name: nanoparticles

surface-reconstruction

display_name: surface-reconstruction

surface-stress

display_name: surface-stress

xrd

display_name: xrd


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

materialscloud

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

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

graphene

display_name: graphene

high-throughput-calculations

display_name: high-throughput-calculations

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

materialscloud

en: Materials Cloud

aiida

display_name: aiida

database

display_name: database

density-functional-theory

display_name: density-functional-theory

marvelhpc

display_name: marvelhpc

max

display_name: max

pasc

display_name: pasc

prace

display_name: prace


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


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: 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: 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

density-functional-theory

display_name: density-functional-theory


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: 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

envi

en: Environment

automation

display_name: automation

dft

display_name: dft

equation-of-state

display_name: equation-of-state

pseudopotentials

display_name: pseudopotentials

verification

display_name: verification


en: 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

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).

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: 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

materialscloud

en: Materials Cloud

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


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


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


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: 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

dft

display_name: dft

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

envi

en: Environment

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


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

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).

mobility

display_name: mobility


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

materialscloud

en: Materials Cloud

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


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

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

envi

en: Environment

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


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

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).

chemical-diversity

display_name: chemical-diversity

generative-models

display_name: generative-models

singlet-fission

display_name: singlet-fission


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

materialscloud

en: Materials Cloud

boroxol-groups

display_name: boroxol-groups


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: 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: 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

dft

display_name: dft

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