AndreaBozzo/ceres-open-data-index huggingface dataset:
Revising known concepts for novel applications: Fe incorporation into Ni-MOF-74 derived oxygen evolution electrocatalysts for anion exchange membrane water electrolysis

The performance of Ni-based oxygen evolution reaction (OER) electrocatalysts is enhanced upon Fe incorporation into the structure or Fe uptake from the electrolyte. In light of the promising potential of metal-organic framework (MOF) electrocatalysts for water splitting, Ni-MOF-74 is used as a model catalyst to study the effect of Fe incorporation from KOH electrolyte on the electrocatalyst's OER activity and stability. The insights obtained from X-ray diffraction and operando X-ray absorption spectroscopy characterizations of Ni-MOF-74 and an amorphous Ni metal organic compound (Ni-MOC*)  reveal that Fe uptake enhances OER by two processes: higher Ni oxidation states and enhanced flexibility of the electronic state and local structure when cycling the potential below and above the OER onset. To demonstrate the impressive OER activity and stability in Fe containing KOH, an Ni-MOC* anode was implemented in an anion exchange membrane water electrolyzer (AEM-WE) with 3 ppm Fe containing 1 M KOH electrolyte resulting in an outstanding cell voltage of 1.7 V (at an anode potential of 1.51 V) at 60 °C and 0.5 A cm-2 exceeding 130 h of stable continuous operation.

Tags: alkalineaem-water-electrolysis, electrocatalysis, fe-effect-on-ni-based-oer-catalysts, marvel, ni-mof-74, oer, operando-xasxrd
Spectral operator representations

Materials are often represented in machine learning applications by (chemical-)geometric descriptions of their atomic structure. In this work, we propose an alternative framework for representing materials using descriptions of their electronic structure called Spectral Operator Representations (SOREPs). This record contains the code and data used to study carbon nanotubes (CNTs), barium titanate polymorphs, and the accelerated screening of transparent conducting materials with SOREPs. A data set for each application is provided: pz tight binding band structures for the three CNT configurations studied; the structures, band dispersions, and SOREP features of 127 BaTiO₃ polymorphs; and the SOREP features and ML targets for the MC3D materials considered in the accelerated screening. Additionally, code including patch files for Quantum ESPRESSO, the "sorep" python package, and the set of scripts used to prepare these data, train ML models, and plot results is provided.

Tags: electronic-bands, electronic-structure, epfl, machine-learning, marvel
Explaining the effect of in-plane strain on thermal degradation kinetics of Cu/W nano-multilayers

Thermal annealing experiments evidence opposite effect on the degradation kinetics of Cu/W nano-multilayers from compressive to tensile in-plane strain.  Besides higher activation energy, nano-multilayers with tensile strains degrade to nanocomposites faster than those with compressive strains. By assuming a vacancy-driven diffusion mechanism of degradation, we applied ab initio calculations to quantify different contributions to the corresponding diffusion coefficients in relation to in-plane strain. The average vacancy formation energy increases as the strain changes from compressive to tensile, which explains the higher experimental activation energy. The bulk in-plane and out-of-plane vacancy migration energies and corresponding diffusion prefactors highlight that enhanced transformation rate under tension can be explained by the segregation of non-equilibrium W vacancies to Cu/W interfaces. Our thermodynamic evaluation of grain boundary wetting and grooving by hybrid molecular dynamics/Monte Carlo method further supports this point, as both stress states enhance W grain separation to the same level.

Tags: ab-initio-calculations, cuw, degradation-kinetics, in-plane-strain, nano-multilayers, vacancy-driven-diffusion
Atomic-level description of thermal fluctuations in inorganic lead halide perovskites

The potential of lead-halide perovskites for realistic applications is currently hindered by their limited long-term stability under functional activation. While the role of lattice flexibility in the thermal response of perovskites has become increasingly evident, the description of thermally-induced distortions is still unclear. In this work, we provide a unified picture of thermal activation in CsPbBr₃ across length scales, showing that lattice symmetry does not increase at high temperatures. We combine temperature-dependent XRD, Br K-edge XANES, ab initio MD simulations, and calculations of the XANES spectra by first-principles, accounting for both thermal fluctuations and core hole final state effects. We find that the octahedral tilting of the Pb-Br inorganic framework statistically adopts multiple local configurations over time - in the short-range. In turn, the stochastic nature of the local thermal fluctuations uplifts the longer-range periodic octahedral tilting characterizing the low temperature structure, with the statistical mean of the local configurations resulting in a cubic-like time-averaged lattice. These observations can be rationalized in terms of displacive thermal phase transitions through the soft mode model, in which the phonon anharmonicity of the flexible inorganic framework causes the excess free energy surface to change as a function of temperature. Our work demonstrates that the effect of thermal dynamics on the XANES spectra can be effectively described for largely anharmonic systems, provided ab initio MD simulations are performed to determine the dynamically fluctuating structures, and core hole final state effects are included in order to retrieve an accurate XANES line shape. Moreover, it shows that the soft mode model, previously invoked to describe displacive thermal phase transitions in oxide perovskites, carries a more general validity.

Tags: halide-perovskites, marvel, molecular-dynamics-simulation, xas-simulation
An anisotropic lattice Boltzmann - phase field model for dendrite growth and movement in rapid solidification of binary alloys

In this paper, we proposed a model coupling the lattice Boltzmann and the phase field methods with anisotropic effects is proposed, which is used to numerically describe the growth and movement of dendrites in rapid solidification of alloys. The model was applied to investigate the effects of dendrite movement and interfacial non-equilibrium on evolution of dendritic patterns for Si-9.0at%As and the CET for Al-3.0wt%Cu alloys. Both the growth and remelt processes of isolated dendrites are studied, and the result reveals the remelting influences on dendrite growth and solute micro-segregation in the condition of directional solidification. This dataset contains the underlying data for the above. This work demonstrates that the proposed model has a wide range of applicability and great potential to simulate the microstructure evolution with various solidification conditions.

Tags: dendrite-growth, lattice-boltzmann, phase-field, rapid-solidification, solid-sedimentation
The three-dimensional atomic-level structure of an amorphous glucagon-like peptide-1 receptor agonist

Amorphous formulations are increasingly used in the pharmaceutical industry due to their increased solubility, but their structural characterization at atomic-level resolution remains extremely challenging. Here, we characterize the complete atomic-level structure of an amorphous glucagon-like peptide-1 receptor (GLP-1R) agonist using chemical shift driven NMR crystallography. The structure is determined from measured chemical shift distributions for 17 of the 32 carbon atoms and 16 of the 31 hydrogen atoms in the molecule. The chemical shifts are able to provide a detailed picture of the atomic-level conformations and interactions, and we identify the structural motifs that play a major role in stabilization of the amorphous form. In particular, hydrogen bonding of the carboxylic acid proton is strongly promoted although no carboxylic acid dimer is formed. Two orientations of the benzodioxole ring are promoted in the NMR structure, corresponding to a significant stabilization mechanism. Our observation that inclusion of water leads to stabilization of the carboxylic acid group might be used as a strategy in future formulations where hydrogen bonding between neighboring molecules may otherwise be hindered by sterics.

Tags: glp-1r, machine-learning, marvel, molecular-dynamics, nmr, nmr-crystallography
Optical materials discovery and design with federated databases and machine learning

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

Tags: electronic-structure, modnet, optical-materials, optimade, refractive-index
Interfacial fluid rheology of soft particles

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.

Tags: elastic-properties, hertzian-model, liquid-liquid-interfaces, microgels, softness-and-elasticity
Neural network potential for Zr-H

The introduction of Hydrogen (H) into Zirconium (Zr) influences many mechanical properties, especially due to low H solubility and easy formation of Zirconium hydride phases. Understanding the various effects of H requires studies with atomistic resolution but at scales that incorporate defects such as cracks, interfaces, and dislocations. Such studies thus demand accurate interatomic potentials. Here, a neural network potential (NNP) for the Zr-H system is developed within the  Behler-Parrinello framework. The Zr-H NNP retains the accuracy of a recent NNP for hcp Zr and exhibits excellent agreement with first-principles density functional theory (DFT) for (i) H interstitials and their diffusion in hcp Zr, (ii) formation energies, elastic constants, and surface energies of relevant Zr hydrides, and (iii) energetics of a common Zr/Zr-H interface. The Zr-H NNP shows physical behavior for many different crack orientations in the most-stable ε-hydride and structures and reasonable relative energetics for the ⟨a⟩ screw dislocation in pure Zr. This Zr-H NNP should thus be very powerful for future study of many phenomena driving H degradation in Zr that require atomistic detail at scales far above those accessible by first-principles

Tags: molecular-dynamics-simulation, neural-network-potentials, zirconium-hydrides
Solvation Structure and UV-Visible Absorption Spectra of the Nitrate Anion at the Air-Ice Interface by First Principles Molecular Simulations

Nitrate is a significant contaminant in Polar snow. Its photolysis in environmental sunlight generates reactive nitrogen, which impacts the oxidative capacity of the atmosphere, influencing the fate and lifetimes of pollutants. The photolysis of nitrate can produce either NO2 or NO2- , with recent experiments suggesting that the process is accelerated at the air-ice interface compared to the bulk solution. In this study, we employed multiscale modeling approaches to investigate the enhanced photoreactivity of nitrate at the ice surface in the presence of two different cations. We characterized the solvation shell of NO3- and explored its pairing with cations in water and ice using ab initio molecular dynamics and enhanced sampling. Molecular trajectories were used to calculate light absorption spectra at different solvation conditions and finite temperature. Our analysis revealed that the pairing of nitrate with cations may alter the molar absorption coefficient of nitrate at the air-ice interface affecting the rate of photolysis observed in experiments of ammonium nitrate deposited on snow.

Tags: dft, ice-surface, molecular-dynamics, nitrate, solvation, tddft, uv-vis-absorption
Effect of residual stress and microstructure on mechanical properties of sputter-grown Cu/W nanomultilayers

The combination of the high wear resistance and mechanical strength of W with the high thermal conductivity of Cu makes the Cu/W system an attractive candidate material for heat sink plasma and radiation tolerance applications. However, the resulting mechanical properties of multilayers and coatings strongly depend on the microstructure of the layers. 
In this work, the mechanical properties of Cu/W nanomultilayers with different densities of internal interfaces are systematically investigated for two opposite in-plane stress states and critically discussed in comparison with literature. Atomistic simulations with the state-of-the-art neural network potential are used to explain the experimental findings. The results suggest that the microstructure, specifically the excess free volume associated with porosity and interface disorder interconnected with the stress state, has a great impact on the mechanical properties, notably Young's modulus of Cu/W nanomultilayers.

Tags: atomistic-simulations, cuw, hardness, marveldd1, nanoindentation, nanomultilayers, youngs-modulus
FINALES - Electrolyte optimization for maximum conductivity and for maximum cycle life

This study investigates an electrolyte system composed of lithium hexafluorophosphate (LiPF6), ethylene carbonate (EC) and ethyl methyl carbonate (EMC). For the assembly of full cells, electrodes based on graphite and lithium nickel dioxide (LNO) are used. This work provides insight into the similarity of formulations of an electrolyte optimized for maximum conductivity and another one optimized for maximum cycle life are expected to be in this chemical system. The goal is to assess whether it is promising to target research efforts on finding an electrolyte formulation within this chemical space which can fulfill both requirements.
A campaign utilizing the latest version of FINALES is designed to determine conductivity values and predict end of life for various electrolyte formulations containing the aforementioned chemicals. The campaigns were able to reproducibly identify regions of high ionic conductivity of the aforementioned chemical composition. The ML methodology applied for the EOL optimisation allowed accelerated early life-time predictions.

Tags: battery, big-map, electrolyte-formulation, electronic-structure, end-of-lifetime, experimental, ionic-conductivity, machine-learning, map, marvelp3, material-acceleration-platform, polis
Quasiparticle self-consistent GW with effective vertex corrections in the polarizability and the self-energy applied to MnO, FeO, CoO, and NiO

Through quasiparticle self-consistent GW, we investigate the electronic structure of the antiferromagnetic ground state of four transition-metal monoxides: MnO, FeO, CoO, and NiO. In addition to the random-phase approximation, we consider two different schemes incorporating effective vertex corrections. The first scheme includes in the polarizability a vertex function derived from the solution of the Bethe-Salpeter equation (BSE), whereas the second scheme includes in both the polarizability and self-energy a vertex function, which carries a long-range part satisfying the Ward identity and a short-range part derived from the adiabatic local density approximation. Our results include fundamental band gaps, macroscopic dielectric constants, and local magnetic moments, emphasizing the role of vertex corrections in the description of these key electronic properties. We provide quasiparticle band structures and projected densities of states allowing us to establish a connection with the Mott insulator picture and to highlight the overall good agreement with experimental photoemission spectra. The inclusion of the vertex in the self-energy preserves the overall shape of the band structures but generally produces larger rigid energy shifts for d bands than for sp bands. Through the solution of the Bethe-Salpeter equation, we determine excitonic effects and obtain dielectric response functions. Despite the experimental uncertainties, which appear sizable in the case of FeO, it is possible to discern a good general correspondence between the calculated and measured spectra, particularly for the absorption onsets and the peak positions. Furthermore, deep excitons are found to occur in the absorption spectra of FeO, CoO, and NiO, in qualitative agreement with the available characterization. Our work indicates that many-body perturbation theory including effective vertex corrections is able to provide a quantitatively accurate description of key electronic-structure features of transition-metal monoxides.

Tags: electronic-structure, gw, many-body-perturbation-theory, transition-metal-oxides, vertex-corrections
Experimental and Theoretical Insights on Gas Trapping in MOFs: A Case Study with Noble Gases and MFU-4 Type MOFs

Isostructural metal-organic frameworks (MOFs), namely MFU-4 and MFU-4-Br, in which the pore apertures are defined by anionic side ligands (Cl− and Br−, respectively), were synthesized and loaded with noble gases. By selecting the type of side ligand, one can fine-tune the pore aperture size, allowing for precise regulation of the entry and release of gas guests. In this study, we conducted experiments to examine gas loading and release using krypton and xenon as model gases, and we complemented our findings with computational modeling. Remarkably, the loaded gas guests remained trapped inside the pores even after being exposed to air under ambient conditions for extended periods, in some cases for up to several weeks. Therefore, we focused on determining the energy barrier preventing gas release using both theoretical and experimental methods. The results were compared in relation to the types of hosts and guests, providing valuable insights into the gas trapping process in MOFs, as well as programmed gas release in air under ambient conditions. Furthermore, the crystal structure of MFU-4-Br was elucidated using the three-dimensional electron diffraction (3DED) technique, and the bulk purity of the sample was subsequently verified through Rietveld refinement. The data in this record are supplementary data to the manuscript with DOI 10.26434/chemrxiv-2024-vb8pj.

Tags: computational-modeling, crystal-engineering, electron-diffraction, gas-storage, metal-organic-framework
Simulation of the crystallization process of Ge₂Sb₂Te₅ nanoconfined in superlattice geometries for phase change memories

Phase change materials are the most promising candidates for the realization of artificial synapsis for neuromorphic computing. Different resistance levels corresponding to analogic values of the
synapsis conductance can be achieved by modulating the size of an amorphous region embedded in its crystalline matrix. Recently, it has been proposed that a superlattice made of alternating layers of the phase change compound Sb₂Te₃ and of the TiTe₂ confining material allows for a better control of multiple intermediate resistance states and for a lower drift with time of the electrical resistance of the amorphous phase. In this work, we consider to substitute Sb₂Te₃ with the Ge₂Sb₂Te₅ prototypical phase change compound that should feature better data retention. By exploiting molecular dynamics simulations with a machine learning interatomic potential, we have investigated the crystallization kinetics of Ge₂Sb₂Te₅ nanoconfined in geometries mimicking Ge₂Sb₂Te₅/TiTe₂ superlattices. It turns out that nanoconfinement induces a slight reduction in the crystal growth velocities with respect to the bulk, but also an enhancement of the nucleation rate due to heterogeneous nucleation. The results support the idea of investigating Ge₂Sb₂Te₅/TiTe₂ superlattices for applications in neuromorphic devices with improved data retention. The effect on the crystallization kinetics of the addition of van der Waals interaction to the interatomic potential is also discussed.

Tags: crystallization, molecular-dynamics-simulation, neural-network-potential, neuromophic-computing, phase-change-materials
Local magnetoelectric effects as predictors of surface magnetic order

We use symmetry analysis and density functional theory to show that changes in magnetic order at a surface with respect to magnetic order in the bulk can be generically determined by considering local magnetoelectric responses of the crystal. Specifically, analysis of the atomic-site magnetoelectric responses, or equivalently the corresponding local magnetic multipoles, can be used to predict all surface magnetic modifications arising purely from symmetry lowering via termination of the bulk magnetic order. This analysis applies even in materials with no bulk magnetoelectric response or surface magnetization. We then demonstrate our arguments for two example antiferromagnets, metallic CuMnAs and rock-salt NiO. We find that the (010) and (1-10) surfaces of CuMnAs and NiO respectively exhibit a series of antiferroically, as well as roughness-sensitive, ferroically ordered, modifications of the surface magnetic dipole moments, via canting or changes in sublattice magnitude, consistent with the bulk ordering of the magnetic multipoles. Our findings demonstrate a universal bulk-boundary correspondance allowing the general prediction of minimal possible surface and interface magnetic modifications, even in non-magnetoelectric materials. Furthermore, it paves the way for more accurate interpretations of a wide variety of surface-sensitive measurements.

Tags: dft, magnetism, surface-science, symmetry-analysis
Charged adsorbates on metallic surfaces from periodic to open boundary conditions

Understanding the thermodynamics of adsorbates on surfaces is central to many (electro)catalysis applications. In first-principles calculations, additional challenges arise when considering charged adsorbates owing to long-range electrostatic interactions in the in-plane and normal directions. Here, we derive an analytical correction to obtain the energy profiles of individual charged adsorbates on metallic surfaces from finite-cell calculations in periodic boundary conditions. The method is illustrated by calculating the adsorption energy profiles of Li+, Na+, and K+ on graphite from first-principles, highlighting the very slow convergence with system size of the periodic calculations and the need to correctly recover the infinite limit.
In this record, we provide the generic input file used to generate the calculated energies of Fig. 4.

Tags: charged-defects, dft, electrochemistry, surface--interface