en: NavCt crystal pictures taken by light microscope at the PSI crystallisation facility.

en: 3D Crystal pictures of NavCT voltage-gated sodium channel


$oid: OBSOLETE

en: Test of scicat

en: Test 2

approval_status: approved

en: The PSI Data Catalog is the central repository directory for scientific metadata generated at the Paul Scherrer Institut (PSI). Powered by SciCat—a community-driven, open-source metadata management framework co-developed by PSI—the catalog provides discoverability and standardized access pathways to petabyte-scale datasets produced across PSI’s large-scale research facilities, including the Swiss Light Source (SLS), the Swiss Spallation Neutron Source (SINQ), and the SwissFEL X-ray free-electron laser.

en: PSI Data Catalog

de: PSI Data Catalog

fr: PSI Data Catalog

it: PSI Data Catalog

political_level: other

state: active

type: organization

psi

capacity: public

de: PSI Open Data Provider

type: dataset

key: guid

key: uri

value: DCAT Harvester SciCat

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente


en: In this dataset the catalytic fast pyrolysis of ortho-methoxyphenol isomers was investigated over different zeolites. The data is part of a manuscript submitted to the journal Nat. Commun., entitled " Tuning the Zeolite Acidity Enables Selectivity Control by Suppressing Ketene Formation in Lignin Catalytic Pyrolysis". This work is supported by the Swiss National Science Foundation (project number 200021_178952).

en: Data: Tuning the Zeolite Acidity Enables Selectivity Control by Suppressing Ketene Formation in Lignin Catalytic Pyrolysis


en: Data to reproduce the experiments described in the paper: "Image2Reg: Linking Chromatin Images to Gene Regulation using Genetic perturbations screens" by Daniel Paysan, Adityanarayanan Radhakrishnan, G.V. Shivashankar and Caroline Uhler. The raw imaging data from Rohban et al. (2017) and from the Joint Undertaking in Morphological Profiling Cell Painting Consortium (Chandrasekaran et al., 2023) used in the study was obtained from IDR0033 available at "http://idr-demo.openmicroscopy.org/webclient/?show=screen-1751" , respectively from the Cell Painting Gallery on the Registry of Open Data on AWS "https://registry.opendata.aws/cellpainting-gallery/". Please note that these data resources need to be appropriately referenced if this dataset is used. Please refer to the above mentioned paper for a complete list of the used data sets.

en: Data set for the manuscript: Image2Reg - Linking Chromatin Images to Gene Regulation using Genetic Perturbation Screens


en: We present a detailed study of the magnetic, spectroscopic and structural properties of ultrathin LaMnO3 films deposited on SrTiO3(001) substrates by oxide molecular beam epitaxy. We find that the as-grown LaMnO3 films are slightly reduced and present a significant magnetic moment, while annealing to 600 oC fully oxidises and enhances its magnetic properties. From synchrotron x-ray photoemission spectromicroscopy, we find the presence of Sr, Ca and Si in the LaMnO3 film that diffuse from the SrTiO3 substrate; these impurities act as hole dopants, and can explain the presence of ferromagnetism in LaMnO3. This work highlights the importance of cation mobility at the elevated growth temperature in modifying the magnetic properties of ultrathin LaMnO3 films.

en: Role of interdiffusion on the magnetism of ultrathin LaMnO3 films


en: Acoustic levitation has attracted attention in terms of chemical and biochemical analysis in combination with various analytical methods because of its unique container-less environment for samples that is not reliant on specific material characteristics. However, loading samples with very high viscosity is difficult. To expand the scope, we propose the use of polymer thin films as sample holders, whereby the sample is dispensed on a film that is subsequently loaded onto an acoustic levitator. When applied for protein crystallography experiments, rotation controllability and positional stability are important prerequisites. We therefore study the acoustic levitation and rotation of thin films with an aspect ratio (the diameter-to-thickness ratio) of 80–240, which is an order of magnitude larger than those reported previously. For films with empirically optimized shapes, we find that it is possible to control the rotation speed in the range of 1–4 rotations per second while maintaining a positional stability of 12 ± 5 µm. The acoustic radiation force acting on the films is found to be a factor of 26–30 higher than that for same-volume water droplets. We propose use cases of the developed films for protein crystallography experiments and demonstrate data collections for large single crystal samples at room temperature.

en: Data for Acoustic levitation and rotation of thin films and their application for room temperature protein crystallography


en:
Ductal carcinoma in situ (DCIS) is a pre-invasive tumor that can give rise to invasive breast cancer, a
leading cause of cancer death. We generated a large-scale tissue microarray imaging dataset, stained for
chromatin using DAPI, from 560 tissue samples from 122 patients at 3 disease stages and 11 phenotypic
categories of the tumor during DCIS progression. We investigated what cell or tissue features mark DCIS
progression and if such features could be obtained from chromatin images alone without the use of
highly multiplexed staining or high-throughput sequencing. Using unsupervised representation learning
methods, we identified eight distinct morphological cell states just based on the chromatin images and
showed that these distinct cell states were associated with interpretable nuclear and chromatin features.
All cell states were observed in all 3 disease stages and 11 phenotypic categories with different
proportions, indicating that cell states that are enriched in invasive cancer already exist in small fractions
in normal breast tissue. Tissue-level analysis revealed that the spatial organization of the cell states, in
terms of a cell state’s position relative to the ducts and relative to each other, were significantly altered
during DCIS progression. Importantly, we showed that disease stage and phenotypic category could be
accurately predicted just based on statistics of cell state neighborhoods. Collectively, our analysis
demonstrates that chromatin imaging represents a powerful measure of cell state and disease stage of
DCIS, thereby providing a simple and effective imaging-based biomarker for tumor progression.

en: Imaging data related to the paper "Unsupervised representation learning of chromatin images identifies changes in cell state and tissue organization during DCIS progression"


en: We report the observation of fully magnetically polarized ultrathin La0.8Sr0.2MnO3 films by using LaMnO3 and La0.45Sr0.55MnO3 buffer layers grown epitaxially on SrTiO3(001) substrates by molecular beam epitaxy. Specifically, we show that La0.8Sr0.2MnO3 films grown on 12-unit-cell LaMnO3 have bulk-like magnetic moments starting from a single unit cell thickness, while for the 15-unit-cell La0.45Sr0.55MnO3 buffer layer, the La0.8Sr0.2MnO3 transitions from an antiferromagnetic state to a fully spin-polarized ferromagnetic state at 4 unit cells. The magnetic results are confirmed by X-ray magnetic circular dichroism, while linear dichroic measurements carried out for the La0.8Sr0.2MnO3/La0.45Sr0.55MnO3 series show the presence of an orbital reorganization at the transition from the antiferromagnetic to ferromagnetic state corresponding to a change from a preferred in-plane orbital hole occupancy, characteristic of the A-type antiferromagnetic state of La0.45Sr0.55MnO3, to preferentially out of plane. We interpret our findings in terms of the different electronic charge transfers between the adjacent layers, confined to the unit cell in the case of insulating LaMnO3 and extended to a few unit cells in the case of conducting La0.45Sr0.55MnO3. Our work demonstrates an approach to growing ultrathin mixed-valence manganite films that are fully magnetically polarized from the single unit cell, paving the way to fully exploring the unique electronic properties of this class of strongly correlated oxide materials.

en: Fully Magnetically Polarized Ultrathin La0.8Sr0.2MnO3 Films


en: Reconstruction code for burst ptychography and tomography used at the at the cSAXS beamline, Swiss Light Source in Paul Scherrer Institute.

en: X-ray Tomography of a Data Processing Chip at 4 nm Resolution


en: Data of a computer chip obtained by using Ptychographic X-ray computed tomography (PXCT) with a 4.2 nm voxel size in 3D. The sample pillar diameter was 5 micrometers and the sample was acquired at the cSAXS beamline, Swiss Light Source in Paul Scherrer Institute.

en: The PSI Data Catalog is the central repository directory for scientific metadata generated at the Paul Scherrer Institut (PSI). Powered by SciCat—a community-driven, open-source metadata management framework co-developed by PSI—the catalog provides discoverability and standardized access pathways to petabyte-scale datasets produced across PSI’s large-scale research facilities, including the Swiss Light Source (SLS), the Swiss Spallation Neutron Source (SINQ), and the SwissFEL X-ray free-electron laser.


en: The evolution of the magnetic ground state of ultrathin 0–10 unit cells (uc) thick La0.8Sr0.2MnO3 films interfaced to an antiferromagnetic La0.45Sr0.55MnO3/SrTiO3(001) buffer layer was investigated with x-ray photoemission electron microscopy. For 0–3 uc La0.8Sr0.2MnO3, we observe antiferromagnetic domains but no ferromagnetic contrast, showing that nominally ferromagnetic La0.8Sr0.2MnO3 adopts the antiferromagnetic ground state of the buffer layer. For larger thicknesses, ferromagnetic domains emerge, confirming that the additional layers revert to the ferromagnetic ground state. We also observe a drastic increase in the complexity of the domain configuration between 3 and 5 uc, which we attribute to competing magnetic and electronic ground states in the system. We attribute the interfacial modified magnetic ground state to charge sharing at the interface due to the chemical potential mismatch, which leads to hole doping at the La0.8Sr0.2MnO3 interface. The present work sheds light on the impact of charge sharing at the interface of complex oxide materials, in particular on the magnetic and electronic states, and presents a strategy for modulating the electronic ground state properties at metallic interfaces.

en: Charge-exchange-driven interfacial antiferromagnetic ground state in La0.8Sr0.2MnO3 ultrathin films

en: PSI Data Catalog

en: Environment


en: Morphology and defects in 3D self-assembled diamond networks


en: The resonant excitation of electronic transitions with coherent laser sources creates quantum coherent superpositions of the involved electronic states. Most time-resolved studies have focused on gases or isolated subsystems embedded in insulating solids, aiming for applications in quantum information1–6. Here, we focus on the coherent control of orbital wavefunctions in the correlated quantum material Tb2Ti2O7, which forms an interacting spin liquid ground state. We show that resonant excitation with a strong THz pulse creates a coherent superposition of the lowest energy Tb 4f states. The coherence manifests itself as a macroscopic oscillating magnetic dipole, which is detected by ultrafast resonant x-ray diffraction. We envision the coherent control of orbital wavefunctions demonstrated here to become a new tool for the ultrafast manipulation and investigation of quantum materials.

en: Coherent control of rare earth 4f shell wavefunctions in the quantum spin liquid Tb2Ti2O7 Source Data


en: The interaction of magnetic order and spontaneous polarization is a fundamental coupling with the prospect for the control of electronic properties and magnetism. The connection among magnetic order, charge localization and associated metal-insulator transition (MIT) are cornerstones for materials control. Materials that combine both effects are therefore of great interest for testing models that claim the occurrence of spontaneous polarization from and magnetic charge order. One class of materials proposed to combine these functionalities is the family of RNiO3 (R: lanthanide or Y), whose members show a clear MIT and an antiferromagnetic ground state and for which an electric polarization has been predicted. Using resonant magnetic x-ray scattering with circular polarization and an applied electric field we show that YNiO3 possess a magnetic structure containing domains of spin-rotations that are consistent with an electric polarization. We show a reversal of the magnetic structure with the applied electric field confirming that charge ordered RNiO3 are magnetoelectric type II multiferroics with a MIT.

en: Magnetoelectric effect in multiferroic nickelate perovskite YNiO3


en: In this paper we present a model system built out of artificially layered materials, allowing us to understand the interrelation of magnetic phases with the metallic-insulating phase at long length scales, and enabling new strategies for the design and control of materials in devices. The artificial model system consists of superlattices made of SmNiO3 and NdNiO3 layers, – two members of the fascinating rare earth nickelate family, having different metal-to-insulator and magnetic transition temperatures. By combining two complementary techniques—resonant elastic x-ray scattering and muon spin relaxation—we show how the magnetic order evolves, in this complex multicomponent system, as a function of temperature and superlattice periodicity. We demonstrate that the length scale of the coupling between the antiferromagnetic and paramagnetic phases is longer than that of the electronic metal-insulator phase transition—despite being subsidiary to it. This can be explained via a Landau theory—where the bulk magnetic energy plus a gradient cost between magnetic and nonmagnetic phases is considered. These results provide a clear understanding of the coupling of magnetic transitions in systems sharing identical order parameters.

en: Coupling of magnetic phases at nickelate interfaces (SNO-NNO)


en: In this study, we explore the intricate interplay between the electronic properties within the solid and field emission beams characteristics. By examining the fundamental principles governing field emission, we investigate how transverse momentum impact the propagation of the emitted electron beams. We demonstrate that transverse momentum and the spatial coherence of the emission source significantly affect beam properties, leading to notable changes in interference patterns.

en: Beam Divergence and Interference Characteristics of Field Emission Beam


en: Acoustic tweezers comprising a surface acoustic wave chip and a disposable silicon microfluidic chip are potentially advantageous to stable and cost-effective acoustofluidic experiments while avoiding the cross-contamination by reusing the surface acoustic wave chip and disposing of the microfluidic chip. For such a device, it is important to optimize the chip-to-chip bonding and the size and shape of the microfluidic chip to enhance the available acoustic pressure. In this work, aiming at studying samples with the size of a few tens of microns, we explore the device structure and assembly method of acoustic tweezers. By using a polymer bonding layer and shaping the silicon microfluidic chip via deep reactive ion etching, we were able to attain the acoustic pressure up to 2 MPa with a corresponding acoustic radiation pressure of 0.2 kPa for 50 MHz ultrasound, comparable to reported values at lower ultrasound frequencies. We utilized the fabricated acoustic tweezers for non-contact viscoelastic deformation experiments of soft matter and  trapping of highly motile cells. These results suggests that the feasibility of the hybrid chip approach to attaining the high acoustic force required to conduct acoustomechanical testing of small soft matters and cells.

en: Dataset for Surface-acoustic-wave driven silicon microfluidic chips for acoustic tweezing of motile cells and viscoelastic microbeads


en: Resonant X-ray diffraction (RXD) provides a unique capability to investigate electronic ordered states in matter. The circular dichroism in RXD can determine the absolute chirality formed by anisotropic multipole arrangement. Here, we demonstrate that the scattering interference between distinct atomic electric multipoles can create circular contrast in resonant RXD even in a non-chiral crystal. These general considerations are applied to 1T-TiSe2, where recently reported circular dichroism in RXD was used to claim chiral charge density wave order. We show that the experimental observations are well reproduced by our ab initio calculations on the basis of the centrosymmetric crystal structure. Our results not only illuminate a way for a better understanding of possible chiral electronic order but also propose a general approach to extract buried high-order atomic multipoles via circular contrast in RXD. Besides, we discuss the difference in the circular dichroism of RXD originated by crystal chirality in chiral crystals and the interference effect in non-chiral crystals.

en: Non-chiral 1T-TiSe2 creates circular dichroism in resonant X-ray diffraction via multipole scattering interference


en: This dataset contains tomographic data (grayscale and segmented) of the commercial Fumasep bipolar membrane assembled in a CO2-electrolyzer. One sample is a fully functional cell, while the other does not contain catalyst layers to avoid metal artifacts. The samples were imaged in dry and water saturated state.

en: Laboratory source X-ray tomographic microscopy of bipolar membrane swelling in an assembled CO2-electrolyzer

en: The PSI Data Catalog is the central repository directory for scientific metadata generated at the Paul Scherrer Institut (PSI). Powered by SciCat—a community-driven, open-source metadata management framework co-developed by PSI—the catalog provides discoverability and standardized access pathways to petabyte-scale datasets produced across PSI’s large-scale research facilities, including the Swiss Light Source (SLS), the Swiss Spallation Neutron Source (SINQ), and the SwissFEL X-ray free-electron laser.


cd-rixs-data-on-cuo

en: Resonant inelastic x-ray scattering (RIXS) has become a prominent technique to study quasiparticle excitations. With advances in polarization analysis capabilities at different facilities, RIXS offers exceptional potential for investigating symmetry-broken quasiparticles like chiral phonons and magnons. At optical wavelengths birefringence can severely affect polarization states in low-symmetry systems. Here we show its importance for soft x-ray resonances. Given the growing interest in Circular Dichroism (CD) in RIXS, it is important to evaluate how birefringence may affect the RIXS spectra of anisotropic systems. We investigate CuO, a well-known anisotropic material, using Cu L3-edge RIXS and detect significant CD in both magnetic and orbital excitations in the collinear antiferromagnetic phase. We demonstrate that the CD can be modeled by a proper treatment of RIXS scattering amplitudes derived from single-ion calculations with birefringence. Recognizing these effects is crucial for unambiguous identification of subtle dichroic effects induced by symmetry-broken quasiparticles. Furthermore, the combined sensitivity of RIXS and birefringence to local symmetry presents an opportunity to study microscopic changes driven by external perturbations.

en: CD RIXS data on CuO

en: PSI Data Catalog

title_for_slug: cd-rixs-data-on-cuo

en: Environment


en: Chirality is a fundamental symmetry element existing at disparate scales and in many categories of scientific fields. Even though symmetry breaking is an indispensable concept for describing qualitatively distinct phenomena, symmetry cannot quantitatively predict measurable quantities. Since chirality is ternary, i.e., left-handed, right-handed, or achiral, the role of its “amplitude” in material properties is habitually not addressed. Considering the role of “amplitude” for time-reversal symmetry breaking, which can be described by magnetization, chirality amplitude is intuitively a key quantity for controlling emergent phenomena. Here, we propose two types of chiral order parameters and demonstrate the tunability of their amplitude at ultrafast timescales. Resonant X-ray diffraction with circular polarization is an established technique to measure crystal chirality directly. Extending this technique to the time domain, we quantify the ultrafast change in chirality amplitude in real time after an optical excitation. Besides, using a THz excitation, we observe oscillations in the resonant diffraction intensities with specific phonon frequencies. This indicates the creation of additional asymmetry, which could also be described as an enhancement in chirality amplitude. Our proposed concept of chirality amplitude and its ultrafast control may lead to a unique approach to control chirality-induced emergent phenomena at ultrafast timescales.

en: Tuning chirality amplitude at ultrafast timescales


en: Van der Waals (vdW) materials exhibit a variety of states that can be switched with low power at low temperatures, offering a viable cryogenic "flash memory" required for the classical control electronics for solid-state quantum information processing. In 1T-TaS2, a non-volatile metallic 'hidden' state can be induced from an insulating equilibrium charge-density wave ground state using either optical or electrical pulses. Given that conventional memristors form localized, filamentary channels which support the current, a key question for design concerns the geometry of the conduction region in highly energy-efficient 1T-TaS2 devices. Here, we report in operando micro-beam X-ray diffraction, fluorescence, and concurrent transport measurements, allowing us to spatially image the non-thermal hidden state induced by electrical switching of 1T-TaS2. Our results reveal a long-range ordered, non-filamentary switched state that extends well below the electrodes, implying that the self-organized, collective growth of the hidden phase is driven by a combination of charge flow and lattice strain. Our unique combination of techniques showcases the potential of non-destructive, three-dimensional X-ray imaging to study bulk switching properties in microscopic detail, namely electrical control of the vdW layer stacking.

en: Imaging of electrically controlled van der Waals layer stacking in 1T-TaS2


en: Raw data associated with the paper entitled 'Pinning of antiferromagnetic domain walls and domain structure in ultrathin La0.45Sr0.55MnO3 films'.


en: Aligned complex-valued projections and tomographic datasets from 3 PXCT acquisitions on the same catalyst specimen primarily composed of zeolite, alumina, and nickel, acquired at 3 different photon energies close to the nickel absorption edge. The file information.txt contains a description of the data. We also include the software scripts used to process the data.

en: Aligned complex-valued projections and 3D tomographic datasets from a catalyst specimen


en: The data available here consists of XPEEM, SQUID, transport, RHEED, and atomic force probe microscopy raw data that was used for a publication entitled "Direct Imaging of the Magnetoelectric Coupling in BaTiO3/La0.9Ba0.1MnO3 Multiferroic Heterostructures" by F. Stramaglia, G. Panchal, L. Tovaglieri, C. Lichtensteiger, F. Nolting, and C. A. F. Vaz.


en: The binding and release of ligands from their protein targets is central to fundamental biological processes as well as to drug discovery. Photopharmacology introduces chemical triggers that allow the changing of ligand affinities and thus biological activity by light. Insight into the molecular mechanisms of photopharmacology is largely missing because the relevant transitions during the light-triggered reaction cannot be resolved by conventional structural biology. Using time-resolved serial crystallography at a synchrotron and X-ray free-electron laser, we have captured the release of azo-combretastatin A4 and the resulting conformational changes in tubulin. Nine structural snapshots from 1 ns to 100 ms complemented by simulations show how cis-to-trans isomerization of the azobenzene bond leads to a switch in ligand affinity, opening of an exit channel, and collapse of the binding pocket upon ligand release. The resulting global backbone rearrangements are related to the action mechanism of tubulin-binding drugs against gout, cancer, and COVID-19.

en: Watching the release of a photopharmacological drug from tubulin using time-resolved serial crystallography: 10 microseconds after photoactivation.


en: Raw data from dataset C319_EPL1_cSAXS_273_S01394-S02309, obtained at the cSAXS beamline at the Swiss Light Source during beamtime 20190654 (e17964).

en: Non-destructive X-ray tomography of brain tissue ultrastructure. Supporting raw data. Dataset C319_EPL1_cSAXS_273_S01394-S02309.


en: The associated article to this dataset describes a new genus and species, Renbernia zhoui E.M.Friis, P.R.Crane & K.R.Pedersen from the Early Cretaceous (early-middle Albian) mesofossil flora collected in Potomac Group sediments at the Puddledock locality, Virginia, U.S.A. The new taxon is based on microsporangiate structures with Decussosporites-type pollen in situ in densely arranged microsporangiophores. The microsporangia are elongate with extrorse and valvate dehiscence. Synchrotron radiation X-ray tomographic microscopy shows that they are bisporangiate. The in situ Decussosporites-type pollen is elliptical with an unusual arrangement of apertures. On the presumed distal face there is a long median colpus flanked laterally by two equally long lateral colpi. On the presumed proximal side there is a short transverse colpus perpendicular to the longitudinal colpi. Renbernia zhoui is compared to Brenneria potomacensis, another microsporangiate structure from the Potomac Group sediments with Decussosporites-type pollen in situ. B. potomacensis is slightly older than Renbernia zhoui and is distinguished by the more rounded microsporangia and the pollen is more distinctly saccate. A comparison with the extinct pollen genus Eucommiidites and a possible link of Renbernia zhoui to the Bennettitales-Erdtmanithecales-Gnetales group, which is diverse and common in the Puddledock mesofossil flora, is discussed.

en: Extinct seed plant diversity in the Early Cretaceous: An enigmatic new microsporangiate fossil with Decussosporites pollen in situ

en: The PSI Data Catalog is the central repository directory for scientific metadata generated at the Paul Scherrer Institut (PSI). Powered by SciCat—a community-driven, open-source metadata management framework co-developed by PSI—the catalog provides discoverability and standardized access pathways to petabyte-scale datasets produced across PSI’s large-scale research facilities, including the Swiss Light Source (SLS), the Swiss Spallation Neutron Source (SINQ), and the SwissFEL X-ray free-electron laser.


en:
Circular dichroism observed by resonant x-ray diffraction at the Cu L3 edge was investigated in a chiral antiferromagnet, Pb(TiO)Cu4(PO4)4, which shows magnetic quadrupole order (TN = 7K). At temperatures
above TN, space-group-forbidden reflection 100 is observed due to the anisotropic tensor of susceptibility (ATS) scattering. With decreasing temperature, the reflection intensity shows substantial circular dichroism below
TN. We found one-to-one correspondence between the sign of the circular dichroism and that of the sample’s crystallographic chirality. In addition, the reflection intensity depends on the product of poling magnetic and
electric fields. The circular dichroism observed in this study is interpreted as the interference between the ATS and the magnetic scatterings. This finding shows that the interference scattering probes both the chirality and the
order parameter of the magnetic quadrupole order in this chiral antiferromagnetic.

en: Chirality and magnetic quadrupole order in Pb(TiO)Cu4(PO4)4 probed by interference scattering in resonant x-ray diffraction

en: PSI Data Catalog


en: Data to reproduce the experiments described in the paper: "Image2Reg: Linking Chromatin Images to Gene Regulation using Genetic perturbations screens" by Daniel Paysan, Adityanarayanan Radhakrishnan, G.V. Shivashankar and Caroline Uhler. The raw imaging data from Rohban et al. (2017) and from the Joint Undertaking in Morphological Profiling Cell Painting Consortium (Chandrasekaran et al., 2023) used in the study was obtained from IDR0033 available at "http://idr-demo.openmicroscopy.org/webclient/?show=screen-1751" , respectively from the Cell Painting Gallery on the Registry of Open Data on AWS "https://registry.opendata.aws/cellpainting-gallery/". Please note that these data resources need to be appropriately referenced if this dataset is used. Please refer to the above mentioned paper for a complete list of the used data sets.

en: Data for Image2Reg

envi

en: Environment


en: Multiple genomic and proteomic studies have suggested that peripheral blood mononuclear cells (PBMCs) respond to tumor secretomes and thus could provide possible avenues for tumor prognosis and treatment evaluation. We hypothesized that the chromatin organization of PBMCs obtained from liquid biopsies, which integrates secretome signals with gene expression programs, provides efficient biomarkers to characterize tumor signals and the efficacy of proton therapy in tumor patients. In the paper associated to the enclosed data set, we show that chromatin imaging of PBMCs combined with machine learning methods provides such robust and predictive chromatin biomarkers. Our work shows that such chromatin biomarker enable the accurate diagnoses of different tumor types and monitoring of treatment effects of patients undergoing proton therapy and thus highlights their potential for accurate cancer diagnostics and therapy evaluation.

en: Imaging data related to the paper: "Imaging and AI based Chromatin Biomarkers for Diagnosis and Therapy Evaluation from Liquid Biopsies" by Kiran Challa* and Daniel Paysan*, Dominic Leiser, Nadia Sauder, Damien C. Weber and G.V. Shivashankar.


en: The controlled rotation of acoustically levitated samples is beneficial for analyzing sample properties, e.g., in a recently reported room temperature x-ray diffraction experiment, wherein thin film sample holders comprising thin film disks with short blades attached around their circumference were utilized. However, the mechanism of producing the torque and the determinant factor of the rotation direction for these planar ultrasonic rotors have been elusive. We, therefore, study the impact of the size and shape on the rotation characteristics of these ultrasonic rotors in air and further study the influence of the viscosity of fluid. Theory and experiment demonstrate the essential role of the short blades in producing the acoustic torque both in air and water. In the airborne case, the shape and arrangement of the blades are found to determine the rotation direction. In water, with a dynamic viscosity 55 times higher than that of air, we demonstrate that ultrasonic rotors down to 25-μm-disk-diameter function in an optimized experimental geometry with approximately the same actuation efficiency as in air. Our results will be beneficial to further improve the applicability of the ultrasonic rotors as sample holders for airborne experiments and to explore the micrometer-scale ultrasonic rotors in liquid.

en: Data for Size and shape dependent rotation characteristics of thin film ultrasonic rotors


en: In this work, we present a comprehensive dataset designed to facilitate the prediction of metal fatigue life using deep learning techniques. The dataset includes detailed experimental data from 40 different metallic materials, comprising a total of 1195 data points under 48 distinct loading paths. Each data point is stored in a CSV file, capturing the loading path as a time-series with axial and tangential stress or strain values.The primary purpose of this dataset is to support the development and validation of deep learning models aimed at accurately predicting the fatigue life of metals under various loading conditions. This dataset includes stress-controlled and strain-controlled data, ensuring a broad representation of experimental scenarios. Additionally, an Excel file accompanies the dataset, providing detailed mechanical properties of each material, such as elastic modulus, tensile strength, yield strength, and Poisson's ratio, along with references to the original experimental sources.This dataset is intended for researchers in materials science and mechanical engineering, offering a robust foundation for training and testing deep learning algorithms in fatigue analysis. By making this dataset publicly available, we aim to foster collaboration and further advancements in the field of metal fatigue prediction. Researchers are encouraged to utilize and contribute to the dataset, thereby enhancing its scope and applicability.

en: A deep learning dataset for metal multiaxial fatigue life prediction

materialscloud

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

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

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

value: DCAT Harvester Materials Cloud

deep-learning

display_name: deep-learning

fatigue-life-prediction

display_name: fatigue-life-prediction

metal-fatigue

display_name: metal-fatigue

multiaxial-fatigue

display_name: multiaxial-fatigue


en: A transient state of the excess electron in liquid water preceding the development of the solvation shell, the so-called wet electron, has been invoked to explain spectroscopic observations, but its binding energy and atomic structure have remained highly elusive. Here, we carry out hybrid functional molecular dynamics to unveil the ultrafast solvation mechanism leading to the hydrated electron. In the pre-hydrated regime, the electron is found to repeatedly switch between a quasi-free electron state in the conduction band and a localized state with a binding energy of 0.26 eV, which we assign to the wet electron. This transient state self-traps in a region of the liquid which extends up to ∼4.5 Å and involves a severe disruption of the hydrogen-bond network. Our picture provides an unprecedented view on the nature of the wet electron, which is instrumental to understand the properties of this fundamental species in liquid water.

en: Picture of Wet Electron: A Localized Transient State in Liquid Water

first-principles

display_name: first-principles

hydrated-electron

display_name: hydrated-electron

liquid-water

display_name: liquid-water

molecular-dynamics

display_name: molecular-dynamics

wet-electron

display_name: wet-electron


en:
Project abstract: In this work, a theoretical framework is developed to explain and predict changes in the product distribution of the propene dimerization reaction, which yields a mixture of C6 olefin isomers, resulting from the use of different porous materials as catalysts. The MOF-74 class of materials has shown promise in catalyzing the dimerization of propene with high selectivity for valuable linear olefin products. We show that experimentally observed changes in the product distribution can be explained in terms of the contribution of the pores to the free energy of formation, which are directly computed using molecular simulation. Our model is used to screen a library of 118 existing and hypothetical MOF and zeolite structures to study how product distribution can be tuned by changing pore size, shape, and composition of porous materials. Using these molecular descriptors, catalyst properties are identified that increase the selective reaction of linear olefin isomers, which are valued as industrial feedstocks. A pore size commensurate with the size of the desired linear products enhances linear conversion by sterically hindering the branched isomers. Another promising feature is the presence of open metal sites, which interact with the olefin π-bond to provide favorable binding sites for the linear isomers.

About this entry: We provide the geometrical framework descriptors, linear conversions, and thermodynamic data for the 118 frameworks screened in this study, as well as thermodynamics of adsorption properties computed for 12 C6 olefin isomers in each framework. For details about the methods used to obtain these data, please see the corresponding paper (DOI: 10.1021/acscatal.7b00712). The data provided here was used to create the scatter plots in the original paper and can be used to find the product distribution and adsorption properties for all of the materials considered in the study.

en: Predicting Product Distribution of Propene Dimerization in Nanoporous Materials (Data Download)

metalorganic-frameworks

display_name: metalorganic-frameworks

molecular-simulation

display_name: molecular-simulation

monte-carlo

display_name: monte-carlo

product-distribution

display_name: product-distribution

propene-dimerization

display_name: propene-dimerization

zeolites

display_name: zeolites


en: The leucine-lysine amphiphilic peptide LKα14 has been used to study fundamental driving forces in processes such as peptide-surface binding and biomineralization. Here, we employ molecular dynamics (MD) simulations in tandem with replica exchange metadynamics to probe the binding mechanism and thermodynamics of LKα14 on silica. We also investigate the effect that the nature of the silica surface – crystalline vs. amorphous, has on the binding properties and peptide-surface conformations. We find that water adsorbs differently on both surfaces; it forms a denser interfacial layer on the crystalline surface, compared to the amorphous surface. This causes the peptide to bind more strongly on the amorphous surface than the crystalline surface. Cluster analysis shows that the peptide adopts a helical conformation at both surfaces, with a greater distribution of states on the crystalline surface. Peptide binding is primarily through lysine interactions, in line with prior experimental results.

en: Amphiphilic Peptide Binding on Crystalline vs. Amorphous Silica from Molecular Dynamics Simulations

enhanced-sampling

display_name: enhanced-sampling

molecular-dynamics-simulations

display_name: molecular-dynamics-simulations

peptide-surface-interactions

display_name: peptide-surface-interactions


en: Molecular self-assembly plays a very important role in various aspects of technology as well as in biological systems. Governed by covalent, hydrogen or van der Waals interactions - self-assembly of alike molecules results in a large variety of complex patterns even in two dimensions (2D). Prediction of pattern formation for 2D molecular networks is extremely important, though very challenging, and so far, relied on computationally involved approaches such as density functional theory, classical molecular dynamics, Monte Carlo, or machine learning. Such methods, however, do not guarantee that all possible patterns will be considered and often rely on intuition. Here we introduce a much simpler, though rigorous, hierarchical geometric model founded on the mean-field theory of 2D polygonal tessellations to predict extended network patterns based on molecular-level information. Based on graph theory, this approach yields pattern classification and pattern prediction within well-defined ranges. When applied to existing experimental data, our model provides an entirely new view of self-assembled molecular patterns, leading to interesting predictions on admissible patterns and potential additional phases. While developed for hydrogen-bonded systems, an extension to covalently bonded graphene-derived materials or 3D structures such as fullerenes is possible, significantly opening the range of potential future applications.

en: Polygonal tessellations as predictive models of molecular monolayers

molecular-self-assembly

display_name: molecular-self-assembly

tesselation

display_name: tesselation


en: Weyl semimetals in three-dimensional crystals provide the paradigm example of topologically protected band nodes. It is usually taken for granted that a pair of colliding Weyl points annihilate whenever they carry opposite chiral charges. In stark contrast, here we report that Weyl points in systems symmetric under the composition of time-reversal with a π-rotation are characterized by a non-Abelian topological invariant. The topological charges of the Weyl points are transformed via braid phase factors which arise upon exchange inside symmetric planes of the reciprocal momentum space. We elucidate this process with an elementary two-dimensional tight-binding model implementable in cold-atoms setups and in photonic systems. In three dimensions, interplay of the non-Abelian topology with point-group symmetry is shown to enable topological phase transitions in which pairs of Weyl points may scatter or convert into nodal-line rings. By combining our theoretical arguments with first-principles calculations, we predict that Weyl points occurring near the Fermi level of zirconium telluride (ZrTe) carry non-trivial values of the non-Abelian charge, and that uniaxial compression strain drives a non-trivial conversion of the Weyl points into nodal lines.

en: Non-Abelian reciprocal braiding of Weyl points and its manifestation in ZrTe

envi

en: Environment

epfl

display_name: epfl

non-abelian-braiding

display_name: non-abelian-braiding

snsf

display_name: snsf

taas

display_name: taas

weyl-semimetal

display_name: weyl-semimetal

zrte

display_name: zrte


en: The solar water splitting process assisted by semiconductor photocatalysts attracts growing research interests worldwide for the production of hydrogen as a clean and sustainable energy carrier. Due to their optical and electrical properties several oxynitride materials show great promise for the fabrication of efficient photocatalysts for solar water splitting. This study reports a comparative investigation of particle- and thin films-based photocatalysts using three different oxynitride materials. The absolute comparison of the photoelectrochemical activities favors the particle-based electrodes due to the better absorption properties and larger electrochemical surface area. However, thin films surpass the particle-based photoelectrodes due to their more suitable morphological features that improve the separation and mobility of the photo-generated charge carriers. Our analysis identifies what specific insights into the properties of materials can be achieved with the two complementary approaches.

en: Oxynitride thin films versus particle-based photoanodes: a comparative study for photoelectrochemical solar water splitting

marvel

display_name: marvel

oxynitride-photoanode

display_name: oxynitride-photoanode

pulsed-laser-deposition

display_name: pulsed-laser-deposition

solar-fuel

display_name: solar-fuel

solar-water-splitting

display_name: solar-water-splitting


en: Skutterudites are crystals with a cagelike structure that can be augmented with filler atoms ("rattlers"), usually leading to a reduction in thermal conductivity that can be exploited for thermoelectric applications. Here, we leverage the recently introduced Wigner formulation of thermal transport to elucidate the microscopic physics underlying heat conduction in skutterudites, showing that filler atoms can drive a crossover from the Boltzmann to the Wigner regimes of thermal transport, i.e., from particlelike conduction to wavelike tunneling. At temperatures where the thermoelectric efficiency of skutterudites is largest, wavelike tunneling can become comparable to particlelike propagation. We define a Boltzmann deviation descriptor able to differentiate the two regimes and relate the competition between the two mechanisms to the materials' chemistry, providing a design strategy to select rattlers and identify optimal compositions for thermoelectric applications.

en: Crossover from Boltzmann to Wigner thermal transport in thermoelectric skutterudites

phonons

display_name: phonons

thermal-conductivity

display_name: thermal-conductivity

thermoelectrics

display_name: thermoelectrics


en: We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form A2B2X6, based on the known material Cr2Ge2Te6, using density functional theory (DFT) calculations and determine their magnetic properties, such as magnetic order and magnetic moment. We also examine formation energies and use them as a proxy for chemical stability.

en: Data-driven studies of magnetic two-dimensional materials

materialscloud

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

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

machine-learning

display_name: machine-learning

magnetic-materials

display_name: magnetic-materials

two-dimensional-materials

display_name: two-dimensional-materials


en: High Entropy Alloys (HEAs) are a new broad class of near-random solid solution alloys that can possess some impressive mechanical and physical properties including high stability against grain growth (i.e. low grain boundary (GB) mobility). Here, it is shown that an initially flat GB in an HEA can become spontaneously rough, driven by natural local compositional fluctuations. Roughening lowers the total GB energy and thus can inhibit migration. A parameter-free theoretical framework is developed to demonstrate the energetics and size scales of the roughening in terms of solute/GB interaction energies and GB disconnection energies. Above a critical level of solute/GB interactions, a planar GB is predicted to roughen down to the scale of the GB periodic unit. A similar theory for 1D GBs (minimum periodic length in one direction) is also developed since such geometries are common in atomistic simulations. Specific predictions are made for the symmetric tilt boundaries Σ17 [100] (530) and Σ5 [100] (310) in a model CoCuFeNi alloy and atomistic simulations demonstrate roughening consistent with the theory. Analysis of the stresses needed to drive migration shows how migration can be inhibited or enhanced, rationalizing variations in mobility of GBs in HEAs.

en: Theory of spontaneous grain boundary roughening in high entropy alloys

materialscloud

en: Materials Cloud

grain-boundaries

display_name: grain-boundaries

high-entropy-alloys

display_name: high-entropy-alloys

pinning

display_name: pinning

spontaneous-roughening

display_name: spontaneous-roughening


en:
The uploaded archive provides a ML-ready data set extracted from the juHemd database (see references) augmented with supplemental data for atomic descriptors. Descriptors provided in this data set include structural, magnetic, atomic quantities as well as derived (summed) quantities. In total, 118 possible descriptors are included of which 12 are DFT generated. For each simulation type (LDA/GGA) there is also a data set cleaned from DFT data available.

After data cleaning and preprocessing we extracted 387 LDA calculated magnetic Heusler structures as well as 408 GGA structures which have a full structural and magnetic data set. As we only aim at magnetic compounds, we chose to filter out compounds from the original JuHemd which have at least 0.1 Bohr magneton as total absolute magnetic moment. For each data file there is an existing descriptor file naming all the descriptors included in the data set.

en: ML-ready Curie temperatures and descriptors extracted from the JuHemd database

critical-temperature

display_name: critical-temperature

curie-temperature

display_name: curie-temperature

density-functional-theory

display_name: density-functional-theory

electronic-structure

display_name: electronic-structure

heusler-alloy

display_name: heusler-alloy

judft

display_name: judft

kkr

display_name: kkr

magnetic

display_name: magnetic

magnetic-phase-transition

display_name: magnetic-phase-transition

magnetic-structure

display_name: magnetic-structure

max

display_name: max

monte-carlo

display_name: monte-carlo


en: We present evidence of a strong circular photon drag effect (PDE) in topological insulators (TIs) through the observation of threefold rotationally symmetric helicity-dependent topological photocurrents using THz spectroscopy in epitaxially-grown Bi₂Se₃ with reduced crystallographic twinning. We establish how twinned domains introduce competing nonlinear optical (NLO) responses inherent to the crystal structure that obscure geometry-sensitive optical processes through the introduction of a spurious mirror symmetry. Minimizing the twinning defect reveals strong NLO response currents whose magnitude and direction depend on the alignment of the excitation to the crystal axes and follow the threefold rotational symmetry of the crystal. Notably, photocurrents arising from helical light reverse direction for left/right circular polarizations and maintain a strong azimuthal dependence—a result uniquely attributable to the circular PDE, where the photon momentum acts as an applied in-plane field stationary in the laboratory frame. Our results demonstrate new levels of control over the magnitude and direction of photocurrents in TIs and that the study of single-domain films is crucial to reveal hidden phenomena that couple topological order and crystal symmetries.

en: Emergence of threefold symmetric helical photocurrents in epitaxial low twinned Bi₂Se₃

experimental

display_name: experimental

photon-drag-effect

display_name: photon-drag-effect

spectroscopy

display_name: spectroscopy

terahertz

display_name: terahertz

topological-materials

display_name: topological-materials


en: The accurate description of the structural and thermodynamic properties of ferroelectrics has been one of the most remarkable achievements of Density Functional Theory (DFT). However, running large simulation cells with DFT is computationally demanding, while simulations of small cells are often plagued with non-physical effects that are a consequence of the system's finite size. Therefore, one is often forced to use empirical models that describe the physics of the material in terms of effective interaction terms, that are fitted using the results from DFT, to perform simulations that do not suffer from finite size effects. In this study we use a machine-learning (ML) potential trained on DFT, in combination with accelerated sampling techniques, to converge the thermodynamic properties of Barium Titanate (BTO) with first-principles accuracy and a full atomistic description. Our results indicate that the predicted Curie temperature depends strongly on the choice of DFT functional and system size, due to the presence of emergent long-range directional correlations in the local dipole fluctuations. Our findings demonstrate how the combination of ML models and traditional bottom-up modeling allow one to investigate emergent phenomena with the accuracy of first-principles calculations and the large size and time scales afforded by empirical models.

en: Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling

dielectric-correlations

display_name: dielectric-correlations

ferroelectrics

display_name: ferroelectrics

finite-size-effects

display_name: finite-size-effects

hybrid-dft-ml

display_name: hybrid-dft-ml

machine-learning-potentials

display_name: machine-learning-potentials

marvel

display_name: marvel

metadynamics

display_name: metadynamics

phase-transitions

display_name: phase-transitions

sinergia

display_name: sinergia

snsf

display_name: snsf


en:
Prediction of microstructure evolution during material processing is essential to control the material properties. Simulation tools for microstructure evolution prediction based on physical concepts are computationally expensive and time-consuming. Therefore, they are not practical when either there is an urgent need for microstructure morphology during the process, or there is a need to generate big microstructure datasets. Essentially, microstructure evolution prediction is a spatiotemporal sequence prediction problem, where the prediction of material microstructure is difficult due to different process histories and chemistry. We propose a Predictive Recurrent Neural Network (PredRNN) model for the microstructure prediction, which extends the inner-layer transition function of memory states in LSTMs to spatiotemporal memory flow. As a case study, we used a dataset from spinodal decomposition simulation of FeCrCo alloy created by the phase-field method for training and predicting future microstructures by previous observations. The results show that the trained network predicts quantitatively accurate microstructure morphologies while it is several orders of magnitude faster than the phase field method.
The trained model aims to generate future material microstructures by learning from the historical microstructures, where the visual dynamics are believed to have modular structures that can be learned with compositional subsystems. A PredRNN (https://github.com/thuml/predrnn-pytorch) model was trained to predict Fe-Cr-Co microstructures evolution during the spinodal decomposition.

en: Spatiotemporal prediction of microstructure evolution with predictive recurrent neural network

envi

en: Environment

phase-field-spinodal-decomposition

display_name: phase-field-spinodal-decomposition


en: In contrast to inorganic quantum wells, hybrid quantum wells (HQWs) based on metal-organic semiconductors are characterized by relatively soft lattices, in which excitonic states can strongly couple to lattice phonons. Therefore, understanding the lattice's impact on exciton dynamics is essential for harnessing the optoelectronic potential of HQWs. Beyond 2D metal halide perovskites, layered metal-organic chalcogenides (MOCs) are an air-stable, underexplored material class hosting room-temperature excitons that could be exploited as photodetectors, light emitting devices, and ultrafast photoswitches. Here, we elucidate the role of phonons in the optical transitions of the prototypical MOC [AgSePh]∞. Impulsive stimulated Raman scattering (ISRS) allows us to detect coherent exciton oscillations driven by Fröhlich interaction with low-energy optical phonons. Steady state absorption and Raman spectroscopies reveal a strong exciton-phonon coupling (Huang-Rhys parameter ~1.7) and its anharmonicity, manifested as non-trivial temperature-dependent Stokes shift. Our ab initio calculations support these observations, hinting at an anharmonic behavior of the low-energy phonons < 200 cm⁻¹. Our results untangle complex exciton-phonon interactions in MOCs, establishing an ideal testbed for room-temperature many-body phenomena.

en: Anharmonic exciton-phonon coupling in metal-organic chalcogenides hybrid quantum wells

exciton-phonon-coupling

display_name: exciton-phonon-coupling

metal-organic-chalcogenides

display_name: metal-organic-chalcogenides

raman-spectroscopy

display_name: raman-spectroscopy


en: Augmented plane wave methods enable an efficient description of atom-centered or localized features of the electronic density, circumventing high energy cutoffs and thus prohibitive computational costs of pure plane wave formulations. To complement existing implementations for ground-state properties and excitation energies, we present the extension of the Gaussian and augmented plane wave method to excited-state nuclear gradients within the Tamm–Dancoff approximation of time-dependent density functional theory and its implementation in the CP2K program package. Benchmarks for a test set of 35 small molecules demonstrate that maximum errors in the nuclear forces for excited states of singlet and triplet spin multiplicity are smaller than 0.1 eV/Å. The method is furthermore applied to the calculation of the zero-phonon line of defective hexagonal boron nitride. This spectral feature is reproduced with an error of 0.6 eV in comparison to GW–Bethe–Salpeter reference computations and 0.4 eV in comparison to experimental measurements. Accuracy assessments and applications thus demonstrate the potential use of the outlined developments for large-scale applications on excited-state properties of extended systems. The data in this record are supplementary data to the published article with DOI 10.1021/acs.jctc.4c00614.

en: Excited-state forces with the Gaussian and augmented plane wave method for the Tamm–Dancoff approximation of time-dependent density functional theory

augmented-plane-wave-methods

display_name: augmented-plane-wave-methods

gaussian-basis-sets

display_name: gaussian-basis-sets


en: In situ interfacial rheology and numerical simulations are used to investigate microgel monolayers in a wide range of packing fractions, ζ2D. The heterogeneous particle compressibility determines two flow regimes characterized by distinct master curves. To mimic the microgel architecture and reproduce experiments, an interaction potential combining a soft shoulder with the Hertzian model is introduced. In contrast to bulk conditions, the elastic moduli vary nonmonotonically with ζ2D at the interface, confirming long-sought predictions of reentrant behavior for Hertzian-like systems.

en: Interfacial fluid rheology of soft particles

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).

elastic-properties

display_name: elastic-properties

hertzian-model

display_name: hertzian-model

liquid-liquid-interfaces

display_name: liquid-liquid-interfaces

microgels

display_name: microgels

softness-and-elasticity

display_name: softness-and-elasticity


en:
The discovery of chiral helical magnetism (CHM) in Cr1/3NbS2 and the stabilization of a chiral soliton lattice (CSL) has attracted considerable interest in view of their potential technological applications. However, there is an ongoing debate regarding whether the sister compound, Mn1/3NbS2, which shares the same crystal structure, exhibits similar nontrivial properties, which rely on the stabilization of the lack of inversion symmetry at the magnetic ion. In this study, we conduct a comprehensive investigation of the magnetically ordered states of both compounds, using 53Cr, 55Mn and 93Nb nuclear magnetic resonance.  Our results, supported by density functional  calculations, detect in a high quality single crystal of Cr1/3NbS2 all the signatures of the monoaxial CHM in  a magnetic field, identifying it as a textbook NMR case. The detailed understanding of this prototypic  behavior provides a reference for Mn1/3NbS2.
Despite the much larger density of specific defects in this second single crystal, we confirm the presence of a CHM phase in the Mn compound, characterized by a very large critical field for the forced ferromagnetic phase (~ 10 T for H || c).

en: Disorder-resilient transition of Helical to Conical ground states in M1/3NbS2, M=Cr,Mn

materialscloud

en: Materials Cloud

chiral-helical-magnetism

display_name: chiral-helical-magnetism

chiral-soliton-lattice

display_name: chiral-soliton-lattice

dft

display_name: dft

nuclear-magnetic-resonance

display_name: nuclear-magnetic-resonance


en: Kagome metals have emerged as pivotal materials in condensed matter physics due to their unique geometric arrangement and intriguing electronic properties. Understanding the origin of magnetism in these materials, particularly in iron rich Fe-Sn binary compounds like Fe₃Sn, holds a significant importance, as they represent potential candidates for permanent magnets with a high Curie temperature and a strong magnetic anisotropy. In the present study, we employ density-functional theory and dynamical mean-field theory to analyze the electronic structure and magnetic properties of Fe₃Sn. Our investigation reveals the presence of several nearly-flat bands and Weyl nodes at low excitation energies. The inclusion of local correlation effects is shown to push these features even closer to the Fermi energy, which may be important for their manipulation via external stimuli. Regarding magnetism, the Hubbard-like interaction leads to an increase of orbital polarization at the expenses of a minor reduction of the spin moment. The magnetic anisotropy energy exhibits a strong dependence on the particular choice of the Coulomb interaction parameters. Additionally, our detailed analysis of the interatomic exchange interactions indicates a significant contribution from the antisymmetric exchange, i.e. the Dzyaloshinskii-Moriya interaction, which showcases the existence of magnetic chirality in the system. Overall, our investigation highlights a strong interplay between the flat bands near the Fermi level, the local Coulomb interaction and the triangular geometry of the lattice, which plays a crucial role in driving the magnetic properties of this material.

en: Exploring strong electronic correlations in the breathing kagome metal Fe₃Sn

dftdmft

display_name: dftdmft

dzyaloshinskii-moriya-interaction-dmi

display_name: dzyaloshinskii-moriya-interaction-dmi

electronic-structure

display_name: electronic-structure

flat-band

display_name: flat-band

kagome-metal

display_name: kagome-metal

magnetism

display_name: magnetism


en: Comprehending water dynamics is crucial in various fields such as water desalination, ion separation, electrocatalysis, and biochemical processes. While ab-initio molecular dynamics (AIMD) accurately portray water's structure, computing its dynamic properties over nanosecond timescales proves cost-prohibitive. This study employs machine learning potentials (MLPs) to accurately determine the dynamical properties of liquid water with ab-initio accuracy. Our findings reveal diversity in the calculated diffusion coefficient (D) and viscosity of water (η) across different methodologies. Specifically, while the GGA, meta-GGA, and hybrid functional methods struggle to predict dynamic properties under ambient conditions, whereas methods on the higher level of Jacob's ladder of DFT approximation perform significantly better. Intriguingly, we discovered that all D and η adhere to the established Stokes-Einstein (SE) relation for all the ab-initio water. The diversity observed among different methods can be attributed to distinct structural entropy, affirming the applicability of excess entropy scaling relations across all functionals. The correlation between D and η provides valuable insights for identifying the ideal temperature to accurately replicate liquid water's dynamic properties. Furthermore, our findings can validate the rationale behind employing artificially high temperatures in the simulation of water via AIMD. These outcomes not only pave the path toward designing better functionals for water but also underscore the significance of water's many-body characteristics.

en: The dynamic diversity and invariance of ab-initio water

aimd

display_name: aimd

density-functional-theory

display_name: density-functional-theory

diffusion

display_name: diffusion

dynamical-properties

display_name: dynamical-properties

machine-learning-potential

display_name: machine-learning-potential

water

display_name: water


en: The use of perovskite oxides as bifunctional catalysts for oxygen electrocatalysis is a promising strategy for developing high-performance unified regenerative fuel cells (URFCs), thanks to their highly tunable structure. This work explores the exsolution of well-dispersed nanoparticles containing reduced Pt species from La0.6Sr0.4Fe0.95Pt0.05O3-δ (LSFPt) as an effective approach to design bifunctional catalysts for oxygen electrocatalysis. A 10 h reduction treatment at 500 °C in 5% H2/Ar promotes the exsolution of ~1.2 nm Pt-containing nanoparticles on the surface, increasing oxygen deficiency without compromising the perovskite structure. The resulting catalyst exhibits significantly improved activity for oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) in alkaline media, with over 2.5-fold performance improvement compared to the as-prepared LSFPt. Electrochemical impedance spectroscopy (EIS), combined with distribution of relaxation times (DRT) deconvolution analysis, revealed a notable decrease in medium-frequency resistance, indicating enhanced charge transfer at the electrode surface for OER and ORR. Post-mortem O K-edge spectra obtained by soft X-ray absorption spectroscopy in total electron yield mode further suggest an increased surface restructuring in the reduced LSFPt, attributed to the synergistic presence of surface defects and exsolved nanoparticles containing reduced Pt species. This one-step reduction protocol offers a promising strategy for designing efficient bifunctional electrocatalysts.

en: Harnessing bifunctional oxygen electrocatalysis: Exsolved Pt nanoparticles from La0.6Sr0.4Fe0.95Pt0.05O3-δ

bifunctional-electrocatalyst

display_name: bifunctional-electrocatalyst

distribution-of-relaxation-times-drt

display_name: distribution-of-relaxation-times-drt

oxygen-evolution-reaction-oer

display_name: oxygen-evolution-reaction-oer

oxygen-reduction-reaction-orr

display_name: oxygen-reduction-reaction-orr

perovskite

display_name: perovskite

pt-exsolution

display_name: pt-exsolution

unified-regenerative-fuel-cells

display_name: unified-regenerative-fuel-cells


en: Materials that efficiently promote the thermodynamically uphill water-splitting reaction under solar illumination are essential for generating carbon-free ("green") hydrogen. Mapping out the combinatorial space of potential photocatalysts for this reaction can be expedited using data-intensive materials exploration. The calculated band gaps and band alignments can serve as key indicators and metrics to computationally screen photoactive materials. Ternary main-group metal sulfides containing p- and s-block elements represent a promising, albeit underexplored, class of photocatalysts. Here, we computationally screen 86 candidate ternary main-group metal sulfides containing p- and s-block elements. By validating electronic structure predictions against experimental band gaps and band edges for synthetically accessible materials, we propose eight potential photocatalysts. Using computed Pourbaix diagrams, we further narrowed the candidate pool to four materials based on the predicted aqueous stability. We then synthesized and characterized these four materials and experimentally screened them for photoresponsiveness under photocatalytically relevant conditions. We also characterized their experimental band gaps and band edge positions and compared them with computational predictions. Based on the experimental screening protocols, we identify MgIn₂S₄ and BaSn₂S₅ as photoresponsive materials with sufficient aqueous stability to be considered in greater depth as potential photocatalysts for overall water-splitting. This record contains the computational predictions for the four candidates discussed in our manuscript.

en: Data-intensive exploration of the photoelectrochemical responses of main-group metal sulfides

envi

en: Environment

photocatalysts

display_name: photocatalysts

sulfides

display_name: sulfides

water-splitting

display_name: water-splitting


en:
Phonon interactions from lattice anharmonicity govern thermal properties and heat transport in materials.These interactions are described by n-th order interatomic force constants (n-IFCs), which can be viewed as high-dimensional tensors correlating the motion of n atoms, or equivalently encoding n-phonon scattering processes in momentum space. Here, we introduce a tensor decomposition to efficiently compress n-IFCs for arbitrary order n. Using tensor learning, we find optimal low-rank approximations of n-IFCs by solving the resulting optimization problem. Our approach reveals the inherent low dimensionality of phonon-phonon interactions and allows compression of the 3 and 4-IFC tensors by factors of up to 10^3-10^4 while retaining high accuracy in calculations of phonon scattering rates and thermal conductivity. Calculations of thermal conductivity using the compressed n-IFCs achieve a speed-up by nearly three orders of magnitude with >98% accuracy relative to the reference uncompressed solution. These calculations include both 3- and 4-phonon scattering and are shown for a diverse range of materials (Si, HgTe, MgO, TiNiSn and monoclinic ZrO2).  In addition to accelerating state-of-the-art thermal transport calculations, the method shown here paves the way for modeling strongly anharmonic materials and higher-order phonon interactions.

This data contains the phonon scattering rates data with full and compressed phonon-phonon interactions. The data corresponds to Figure 3 in the paper and includes information on the phonon scattering rates for two materials: silicon and HgTe.

en: Phonon scattering rates data for silicon and HgTe

phonon-phonon-interactions

display_name: phonon-phonon-interactions

phonon-scattering-rate

display_name: phonon-scattering-rate

thermal-transport

display_name: thermal-transport


en: Charge carrier mobilities are critical parameters in halide perovskite solar cells, governing their average carrier velocity under an applied electric field and overall efficiency. Recent advances in first-principles calculations of electron-phonon interactions and carrier mobilities have enabled predictive computations for perovskite solar cells. However, the flexible octahedral frameworks and cationic displacements in these materials challenge the harmonic approximation, leading to significant difficulties in accurately calculating transport properties. To address these issues, we combine temperature-dependent effective potentials with the ab initio Boltzmann transport equations to compute carrier mobilities in a representative lead-free perovskite, CsSnBr₃. At room temperature, the electron/hole Hall mobilities in CsSnBr₃ are 106/256 cm²/Vs when neglecting anharmonic effects and 59/145 cm²/Vs when included. This overestimation of the harmonic approximation arises from the neglect of scattering coming from soft modes. We provide a workflow for performing first-principles carrier mobility calculations in anharmonic systems, advancing the predictive modeling of perovskite solar cells.

en: Impact of anharmonicity on the carrier mobility of the Pb-free CsSnBr₃ perovskite

ab-initio

display_name: ab-initio

anharmonicity

display_name: anharmonicity

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

mobility

display_name: mobility


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

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

en: GapExtractor v2.0

materialscloud

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

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

band-gap-energy

display_name: band-gap-energy

diffuse-reflectance-spectroscopy

display_name: diffuse-reflectance-spectroscopy

polyphasic-samples

display_name: polyphasic-samples

tauc-method

display_name: tauc-method


en: Gallium nitride (GaN) is a wide-bandgap semiconductor of significant interest for applications in solid-state lighting, power electronics, and radio-frequency amplifiers. An important limitation of this semiconductor is its low intrinsic hole mobility, which hinders the development of p-channel devices and the large-scale integration of GaN CMOS in next-generation electronics. Prior research has explored the use of strain to improve the hole mobility of GaN, but a systematic analysis of all possible strain conditions and their impact on the mobility is lacking. In this study, we introduce a piezomobility tensor notation to characterize the relationship between applied strain and hole mobility in GaN. To map the strain-dependence of the hole mobility, we solve the ab initio Boltzmann transport equation, accounting for electron-phonon scattering and GW quasiparticle energy corrections.

en: Design of high-mobility p-type GaN via the piezomobility tensor

materialscloud

en: Materials Cloud

hole-mobility

display_name: hole-mobility

nitrides

display_name: nitrides


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

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

defect

display_name: defect

hydrogen

display_name: hydrogen

zno

display_name: zno


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

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

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

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

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

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

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

catalytic-activity

display_name: catalytic-activity

electronic-coupling

display_name: electronic-coupling

li-s-batteries

display_name: li-s-batteries

machine-learning-prediction

display_name: machine-learning-prediction


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

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

copolymerization

display_name: copolymerization

microgels

display_name: microgels

molecular-modeling

display_name: molecular-modeling

nmr-spectroscopy

display_name: nmr-spectroscopy

selective-neutron-scattering

display_name: selective-neutron-scattering


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

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

envi

en: Environment

coarse-grained-models

display_name: coarse-grained-models

core-shell-microgels

display_name: core-shell-microgels

effective-interaction

display_name: effective-interaction

many-body-effects

display_name: many-body-effects

molecular-simulation

display_name: molecular-simulation


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

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

cuw-nanomultilayers

display_name: cuw-nanomultilayers

interface-stress

display_name: interface-stress

intermixing

display_name: intermixing

metastable-phases

display_name: metastable-phases

molecular-statics

display_name: molecular-statics


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

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

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

atomistic-machine-learning

display_name: atomistic-machine-learning

long-stride-simulations

display_name: long-stride-simulations

ml-accerelated-simulations

display_name: ml-accerelated-simulations

molecular-dynamics

display_name: molecular-dynamics


en:
Dual function materials (DFMs) enable reactive carbon capture (RCC), an intensified approach to carbon dioxide capture and utilization for cost and energy input reductions. Yet, there is a fundamental lack of understanding of mechanisms around CO2 adsorption and subsequent conversion on these materials, hindering further development. Herein, we investigated several supported alkaline metal oxides for their CO2 adsorption characteristics to find that Na/Al2O3 had the highest CO2 adsorption capacity, accompanied by a variety of CO2 adsorption geometries as identified by in situ DRIFTS and computational modeling. The addition of catalytic metals (Ru, Pd) increased the adsorption capacity of Na/Al2O3 without altering binding modes. In the subsequent reactive desorption step, acetate and formate intermediates were observed. Notably, this mechanistic investigation identified that the formation of acetate species was unique to RCC on a DFM, as these species were not observed in co-fed hydrogenation over the DFM or RCC over a Na-free catalyst.

Herein, we share the structural CONTCARs obtained using periodic PBE calculations in VASP for hydrated (110) and (100) surfaces of gamma-alumina, Na2O-modified surface models for Na/Al2O3, subsequent structures of various surface carbonate (CO3*) and bicarbonate (HCO3*) configurations formed on each of these surfaces upon CO2 adsorption and adsorbed acetate and formate radicals on the surfaces.

en: Identifying mechanistic differences between co-fed CO2 hydrogenation and reactive CO2 capture using Ru and Pd dual function materials

materialscloud

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

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

alumina

display_name: alumina

density-functional-theory

display_name: density-functional-theory

dfm

display_name: dfm

dft

display_name: dft

dual-function-material

display_name: dual-function-material

rcc

display_name: rcc

reactive-carbon-capture

display_name: reactive-carbon-capture


en: Hollow microgels are elastic polymer shells easily realizable in experiments. Recent works have shown the emergence of buckling events in dilute hollow microgels under the effect of an added osmotic pressure. Here, we perform large-scale simulations to show that these microgels at high enough packing fractions undergo spontaneous symmetry-breaking deformations ranging from single large dents to multiple indentations, even in the absence of any externally applied stress. This self-induced buckling phenomenon is thus solely driven by interparticle crowding. We construct a phase diagram inspired by vesicle shape theories, mapping local curvature metrics as a function of the reduced volume, to quantify these findings, and we also propose ways to observe the occurrence of buckling in experiments. The present results thus rationalize the deformations occurring in suspensions of micro- and nanoscale elastic shells, offering a synthetic analogue to biological ones and allowing direct control on buckling instabilities for potential applications. Beyond materials design, these insights may also help to describe shape regulation in natural systems such as cells and vesicles, where similar deformations are observed.

en: Self-induced buckling in hollow microgels

materialscloud

en: Materials Cloud

buckling-instability

display_name: buckling-instability

microgels

display_name: microgels

monomer-resolved-simulations

display_name: monomer-resolved-simulations


en:
The past few years have seen the development of ``universal'' machine-learning interatomic potentials (uMLIPs) capable of approximating the ground-state potential energy surface across a wide range of chemical structures and compositions with reasonable accuracy. While these models differ in the architecture and the dataset used, they share the ability to compress a staggering amount of chemical information into descriptive latent features. Herein, we systematically analyze what the different uMLIPs have learned by quantitatively assessing the relative information content of their latent features with feature reconstruction errors as metrics, and observing how the trends are affected by the choice of training set and training protocol. We find that the uMLIPs encode chemical space in significantly distinct ways, with substantial cross-model feature reconstruction errors. When variants of the same model architecture are considered, trends become dependent on the dataset, target, and training protocol of choice. We also observe that fine-tuning of a uMLIP retains a strong pre-training bias in the latent features. Finally, we discuss how atom-level features, which are directly output by MLIPs, can be compressed into global structure-level features via concatenation of progressive cumulants, each adding significantly new information about the variability across the atomic environments within a given system.

This repository contains the scripts to reproduce results presented in the associated paper/preprint.

en: Comparing the latent features of universal machine-learning interatomic potentials

feature-comparison

display_name: feature-comparison

interatomic-potentials

display_name: interatomic-potentials

machine-learning

display_name: machine-learning


en: Topological effects arising from the Berry curvature lead to intriguing transport signatures in quantum materials. Two such phenomena are the chiral anomaly and nonlinear Hall effect (NLHE). A unified description of these transport regimes requires a quantitative treatment of both band topology and electron scattering. Here, we show accurate predictions of the magnetoresistance in topological semimetals and NLHE in noncentrosymmetric materials by solving the Boltzmann transport equation (BTE) with electron-phonon (e-ph) scattering and Berry curvature computed from first principles. We apply our method to study magnetotransport in a prototypical Weyl semimetal, TaAs, and the NLHE in strained monolayer WSe2, bilayer WTe2 and bulk BaMnSb2. In TaAs, we find a chiral contribution to the magnetoconductance which is positive and increases with magnetic field, consistent with experiments. We show that e-ph interactions can significantly modify the Berry curvature dipole and its dependence on temperature and Fermi level, highlighting the interplay of band topology and electronic interactions in nonlinear transport. The computed nonlinear Hall response in BaMnSb2 is in agreement with experiments. By adding the Berry curvature to first-principles transport calculations, our work advances the quantitative analysis of a wide range of linear and nonlinear transport phenomena in quantum materials.

en: Magnetotransport in topological materials and nonlinear Hall effect via first-principles electronic interactions and band topology

berry-curvature

display_name: berry-curvature

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

magnetotransport

display_name: magnetotransport

nonlinear-hall-effect

display_name: nonlinear-hall-effect

topological-materials

display_name: topological-materials


en:
We use the self-consistent harmonic approximation (SSCHA) with machine learning interatomic potentials to calculate the effect of 18O substitution on the properties of quantum paraelectric SrTiO3 (STO). We find that calculations including both quantum and anharmonic effects are able to reproduce the experimentally observed isotope effect, in which the replacement of 16O by 18O induces the ferroelectric state, and we demonstrate that the ferroelectric phase transition in ST18O can be reproduced in a purely displacive description. We calculate the ferroelectric soft mode frequency as a function of volume, lattice parameters, and temperature for ST16O and ST18O and find that the phase space in which ST16O shows quantumparaelectric behavior, while ST18O becomes ferroelectric, is narrow. Our Letter shows that machine learning interatomic potentials enable temperature-dependent simulations that include quantum and anharmonic phonon effects and that quantitative prediction of the phase diagram in the case of STO is limited by the accuracy of the underlying electronic structure method.

This dataset contains the inputs/and outputs from the SSCHA calculations.

en: Machine-learning-enabled ab initio study of quantum phase transitions in SrTiO3

ferroelectric

display_name: ferroelectric

isotope-effect

display_name: isotope-effect

quantum-paraelectric

display_name: quantum-paraelectric


en:
The nudged elastic band (NEB) method is the standard approach for finding minimum energy paths and transition states on potential energy surfaces. Practical NEB calculations require several pre-processing steps: endpoint minimization, structural alignment, and initial path generation. These steps are typically handled by ad-hoc scripts or manual intervention, introducing errors and hindering reproducibility. We present a fully automated, open-source Snakemake workflow for small gas phase molecules that couples modern machine learning potentials (PET-MAD) to the eOn saddle point search software. Each step of the calculation lifecycle is encoded as an explicit dependency graph, from model retrieval and endpoint preparation through path initialization and band optimization. The workflow resolves all software dependencies from conda-forge, ensuring identical execution across platforms. Validation on the HCN to HNC isomerization demonstrates that the automated pipeline recovers the known single-barrier energy profile and product energy without manual intervention.

This record contains the data needed to "rehydrate" the Github repository to the point of submission including the models and results.

en: Reproducible orchestration of best practices for reaction path optimization with the nudged elastic band

envi

en: Environment

computational-chemistry

display_name: computational-chemistry

nudged-elastic-band

display_name: nudged-elastic-band

transition-state

display_name: transition-state


en: This record contains the computational data associated with the manuscript “Distinct O2 activation regimes and an activation–removal trade-off at 3d-metal-doped MoS2 sulfur vacancies” by Park and Kim. The dataset includes optimized atomic structures, representative VASP input templates, and curated tabulated data supporting the reported adsorption energies, structural parameters, electronic descriptors, and kinetic benchmarks for Fe-, Co-, Ni-, and Cu-doped MoS2 sulfur vacancies. The data are intended to support reproducibility of the figures and tables in the manuscript and the Supporting Information. Detailed descriptions of the individual files and folders are provided in the accompanying README.txt file.

en: Computational dataset for distinct O2 activation regimes and an activation–removal trade-off at 3d-metal-doped MoS2 sulfur vacancies

cohp

display_name: cohp

density-functional-theory

display_name: density-functional-theory

electronic-structure-descriptors

display_name: electronic-structure-descriptors

oxygen-activation

display_name: oxygen-activation

single-atom-catalyst

display_name: single-atom-catalyst

strain-engineering

display_name: strain-engineering

sulfur-vacancy

display_name: sulfur-vacancy

transition-metal-doping

display_name: transition-metal-doping


en: These data characterize the activation processes of CoSn(OH)6-based electrocatalysts for the oxygen evolution reaction in alkaline conditions. Crystalline (CoSn25), crystalline defective (CoSn150) and amorphous (CoSn300) catalysts with identical metal compositions from the same precursor batch were considered. The operando X-ray diffraciton and X-ray abosorption spectroscopy indicate that crystalline and amorphous catalysts take different reconstruction pathways under the reaction conditions. Co L-edge total electron yield data indicate that a metal-ligand charge transfer surface state is formed during electrochemical activation that correlates well with the observed electrochemical activity.

en: Characteristic data of the CoSn(OH)6 alkaline electrochemical activation processes.

electrocatalysis

display_name: electrocatalysis

oer

display_name: oer

operando

display_name: operando

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction

synchrotron

display_name: synchrotron

x-ray-absorption-spectroscopy

display_name: x-ray-absorption-spectroscopy

x-ray-diffraction

display_name: x-ray-diffraction