AndreaBozzo/ceres-open-data-index huggingface dataset:
High Li-ion conductivity in tetragonal LGPO: a comparative first-principles study against known LISICON and LGPS phases

This work presents extensive first-principles (Car-Parrinello) molecular dynamics simulations of the solid-state electrolyte Li10GeP2O12 (LGPO) in a tetragonal phase -not synthesized so far- that is isostructural to the highly Li-ion conductive tetragonal phase of the sulfide analogue Li10GeP2S12 (LGPS). We provide comparative simulations of the experimentally known orthorhombic phase of LGPO (that we call here LISICON, from the family of superionic conductors to which LGPO belongs) and of the two experimentally known phases of LGPS, quasi-orthorhombic (called thio-LISICON) and tetragonal. We extract diffusion coefficients from fixed-cell simulations in the canonical ensemble and we study dynamical stability from variable-cell simulations in the isobaric-isothermal ensemble. The main outcome of this work is that, according to these simulations, although tetragonal LGPO is less stable than its orthorhombic allotrope, it exhibits a much higher conductivity, comparable to that presently estimated for both phases of LGPS. These results indicate that hypothetical tetragonal LGPO, if synthesized, could make a very attractive Li-ion conductor.

Tags: big-map, cscs, dynamical-stability, first-principles-molecular-dynamics, ionic-conductivity, ionic-transport, lgpo, lgps, lisicon, marvelinc1, snsf, solid-state-electrolytes
Rules of formation of H–C–N–O compounds at high pressure and the fates of planetary ices

Results of an ab initio structure search on the H+C+N+O quaternary space at 500GPa.

The solar system's outer planets, and many of their moons, are dominated by matter from the H–C–N–O chemical space, based on solar system abundances of hydrogen and the planetary ices H2O, CH4 , and NH3 . In the planetary interiors, these ices will experience extreme pressure conditions, around 5 Mbar at the Neptune mantle–core boundary, and it is expected that they undergo phase transitions, decompose, and form entirely new compounds. While temperature will dictate the formation of compounds, ground- state density functional theory allows us to probe the chemical effects resulting from pressure alone. These structural developments in turn determine the planets' interior structures, thermal evolution, and magnetic field generation, among others. Despite its importance, the H–C–N–O system has not been surveyed systematically to explore which compounds emerge at high-pressure conditions, and what governs their stability. Here, we report on and analyze an unbiased crystal structure search among H–C–N–O compounds between 1 and 5 Mbar. We demonstrate that simple chemical rules drive stability in this composition space, which explains why the simplest possible quaternary mixture HCNO—isoelectronic to diamond—emerges as a stable compound and discuss dominant decomposition products of planetary ice mixtures.

Tags: ab-initio-random-structure-searching, h-c-n-o-chemistry, high-pressure, planetary-ices
Learning local equivariant representations for large-scale atomistic dynamics

A simultaneously accurate and computationally efficient parametrization of the energy and atomic forces of molecules and materials is a long-standing goal in the natural sciences. In pursuit of this goal, neural message passing has lead to a paradigm shift by describing many-body correlations of atoms through iteratively passing messages along an atomistic graph. This propagation of information, however, makes parallel computation difficult and limits the length scales that can be studied. Strictly local descriptor-based methods, on the other hand, can scale to large systems but do not currently match the high accuracy observed with message passing approaches. This work introduces Allegro, a strictly local equivariant deep learning interatomic potential that simultaneously exhibits excellent accuracy and scalability of parallel computation. Allegro learns many-body functions of atomic coordinates using a series of tensor products of learned equivariant representations, but without relying on message passing. Allegro obtains improvements over state-of-the-art methods on the QM9 and revised MD-17 data sets. A single tensor product layer is shown to outperform existing deep message passing neural networks and transformers on the QM9 benchmark. Furthermore, Allegro displays remarkable generalization to out-of-distribution data. Molecular dynamics simulations based on Allegro recover structural and kinetic properties of an amorphous phosphate electrolyte in excellent agreement with first principles calculations. Finally, we demonstrate the parallel scaling of Allegro with a dynamics simulation of 100 million atoms.

Tags: density-functional-theory, machine-learning, molecular-dynamics
Importance of intersite Hubbard interactions in β-MnO2: A first-principles DFT+U+V study

We present a first-principles investigation of the structural, electronic, and magnetic properties of pyrolusite (β-MnO2) using conventional and extended Hubbard-corrected density-functional theory (DFT+U and DFT+U+V). The onsite U and intersite V Hubbard parameters are computed using linear-response theory in the framework of density-functional perturbation theory. We show that while the inclusion of the onsite U is crucial to describe the localized nature of the Mn(3d) states, the intersite V is key to capture accurately the strong hybridization between neighboring Mn(3d) and O(2p) states. In this framework, we stabilize the simplified collinear antiferromagnetic (AFM) ordering (suggested by the Goodenough-Kanamori rule) that is commonly used as an approximation to the experimentally-observed noncollinear screw-type spiral magnetic ordering. A detailed investigation of the ferromagnetic and of other three collinear AFM spin configurations is also presented. The findings from Hubbard-corrected DFT are discussed using two kinds of Hubbard manifolds -- nonorthogonalized and orthogonalized atomic orbitals -- showing that special attention must be given to the choice of the Hubbard projectors, with orthogonalized manifolds providing more accurate results than nonorthogonalized ones within DFT+U+V. This work paves the way for future studies of complex transition-metal compounds containing strongly localized electrons in the presence of pronounced covalent interactions.

Tags: band-gap, crystal-structure, cscs, density-functional-theory, density-of-states, dftu, dftuv, goodenough-kanamori-rule, hubbard-parameters, hubbard-projectors, magnetic-moment, marvelosp, mno2, nonorthogonalized-atomic-orbitals, orthogonalized-atomic-orbitals, self-interactions, snsf, spin-configuration
Symmetry-based computational search for novel binary and ternary 2D materials

We present a symmetry-based exhaustive approach to explore the structural and compositional richness of two-dimensional materials. We use a combinatorial engine' that constructs potential compounds by occupying all possible Wyckoff positions for a certain space group with  combinations of chemical elements. These combinations are restricted by imposing charge neutrality and the Pauling test for electronegativities. The structures are then pre-optimized with a specially crafted universal neural-network force-field, before a final step of geometry optimization using density-functional theory is performed. In this way we unveil an unprecedented variety of two-dimensional materials, covering the whole periodic table in more than 30 different stoichiometries of form AnBm or AnBmCk. Among the found structures we find examples that can be built by decorating nearly all Platonic and Archimedean tesselations as well as their dual Laves or Catalan tilings. We also obtain a rich, and unexpected, polymorphism for some specific compounds. We further accelerate the exploration of the chemical space of two-dimensional materials by employing machine-learning-accelerated prototype search, based on the structural types discovered in the exhaustive search. In total, we obtain around 6500 compounds, not present in previous available databases of 2D materials, with an energy of less than 250 meV/atom above the convex hull of thermodynamic stability.

Tags: 2d-materials, crystal-graph-neural-networks, density-functional-theory, high-throughput, machine-learning
Interplay between polarization, strain and defect-pairs in Fe-doped  SrMnO3-δ

Defect chemistry, strain, and structural, magnetic and electronic degrees of freedom constitute a rich space for the design of functional properties in transition metal oxides. Here, we show that it is possible to engineer polarity and ferroelectricity in non-polar perovskite oxides via polar defect pairs formed by anion vacancies coupled to substitutional cations. We use a self-consistent site-dependent DFT+U approach that accounts for local structural and chemical changes upon defect creation and which is crucial to reconcile predictions with the available experimental data. Our results for Fe-doped oxygen-deficient SrMnO3 show that substitutional Fe and oxygen vacancies can promote polarity due to an o -center displacement of the defect charge resulting in a net electric dipole moment, which polarizes the lattice in the defect neighborhood. The formation of these defects and the resulting polarization can be tuned by epitaxial strain, resulting in enhanced polarization also for strain values lower than the ones necessary to induce a polar phase transition in undoped SrMnO3. For high enough defect concentrations, these defect dipoles couple in a parallel fashion, thus enabling defect- and strain-based engineering of ferroelectricity in SrMnO3.

Tags: cscs, defect-pair, doping, ferroelectricity, marveldd5, point-defect, polarization, self-consistent-dftu, snsf, srmno3, strain, thin-film
Solids that are also liquids: elastic tensors of superionic materials

This work presents an application of the strain-fluctuation method, exploiting the fluctuations of the strain from extensive first-principles molecular dynamics simulations in the isobaric-isothermal ensemble, to the study of the elastic tensors of superionic materials. As the superionic materials for solid-state electrolyte applications usually do not have well-defined ground-state configurations, it is challenging to apply the static methods to calculate the elastic tensors of these materials. Instead, the strain-fluctuation method captures the dynamical nature of the elastic response of these materials and is a promising approach to studying their elastic properties. In this work: a protocol is presented and documented to extract the elastic the elastic moduli and their statistical errors from the molecular dynamics trajectories (open-source code available at https://github.com/materzanini); results for two benchmark superionic materials (Li₁₀GeP₂S₁₂ and Li₁₀GeP₂O₁₂) are given; for these superionic materials, a comparison to static methods is also provided, showing that static methods overestimate the moduli with respect to the correct dynamical treatment by ~25-50%.

Tags: big-map, cscs, elastic-constants, first-principles-molecular-dynamics, lgpo, lgps, marvelinc1, snsf, solid-state-electrolytes, statistical-sampling, stress-strain, superionics
Nuclear quantum effects on the electronic structure of water and ice

The electronic properties and optical response of ice and water are intricately shaped by their molecular structure, including the quantum mechanical nature of hydrogen atoms. Despite numerous former studies, a comprehensive understanding of nuclear quantum effects (NQE) on the electronic structure of water and ice at finite temperatures remains elusive. Here, we utilize molecular simulations that harness efficient machine-learning potentials and many-body perturbation theory to assess how NQEs impact the electronic bands of water and hexagonal ice. By comparing path-integral and classical simulations, we find that NQEs lead to a larger renormalization of the fundamental gap of ice, compared to that of water, ultimately yielding similar bandgaps in the two systems, consistent with experimental estimates. Our calculations suggest that the increased quantum mechanical delocalization of protons in ice, relative to water, is a key factor leading to the enhancement of NQEs on the electronic structure of ice.

Tags: electronic-structure, molecular-dynamics, nuclear-quantum-effects, water
Experimental and theoretical study of stable and metastable phases in sputtered CuInS2

The chalcopyrite Cu(In,Ga)S2 has gained renewed interest in recent years due to its potential application in tandem solar cells. In this contribution, a combined theoretical and experimental approach is applied to investigate stable and metastable phases forming in sputtered CuInS2 (CIS) thin films. Ab initio calculations are performed to obtain formation energies, X-ray diffraction patterns, and Raman spectra of various CIS polytypes and related compounds. Multiple low-energy CIS structures with zinc-blende and wurtzite-derived lattices are identified and their XRD/Raman patterns are shown to contain many overlapping features, which could lead to misidentification unless the techniques are duly combined and analyzed. The results are verified against experimental XRD/Raman spectra measured on a series of CIS films with different compositions and treated at different temperatures, revealing the formation of several CIS polymorphs and secondary phases. The characteristic features and the mechanisms behind the formation of different phases are discussed with the focus on the thin-film photovoltaic application of CIS. The dataset contains structures and VASP output files used to derive the discussed trends.

Tags: disorder, polymorphs, polytypes, simulated-powder-x-ray-diffraction, simulated-raman-spectra, wurtzite, zinc-blende
Probing magnetic orbitals and Berry curvature with circular dichroism in resonant inelastic X-ray scattering

Resonant inelastic X-ray scattering (RIXS) can probe localized excitations at selected atoms in materials, including particle-hole transitions from valence to the conduction bands. These transitions are governed by fundamental prop- erties of the corresponding Bloch wave-functions, including orbital and mag- netic degrees of freedom, and quantum geometric properties such as the Berry curvature. In particular, orbital angular momentum (OAM), which is closely linked to the Berry curvature, can exhibit a nontrivial momentum dependence. We demonstrate how information on such OAM textures can be extracted from the circular dichroism in RIXS. Based on accurate modeling with first-principles treatment of the key ingredient – the light-matter interaction – we simulate dichroic RIXS spectra for the prototypical transition metal dichalcogenide MoSe₂ and the two-dimensional topological insulator 1T′-MoS₂ . Guided by an in- tuitive picture for the optical selection rules, we discuss how the momentum- dependent OAM manifests itself in the dichroic RIXS signal if one controls the momentum transfer. Our calculations are performed for typical experimental geometries and parameter regimes, and demonstrate the possibility of observ- ing the predicted circular dichroism in forthcoming experiments. Thus, our work establishes a new avenue to observing Berry curvature and topological states in quantum materials.

The simulated RIXS spectra and the scripts to generate the plots in this work are available in this archive.

Tags: marvel, quantum-espresso, two-dimensional, wannier-functions
Influence of the hBN dielectric layers on the quantum transport properties of MoS2 transistors.

An ab initio  study of the coupled electrons and phonon transport properties of MoS2-hBN devices. A comparison of the device characteristics when hBN is treated as a perfectly insulating, non vibrating layer and one where it is included in the DFT together with MoS2.

Tags: ab-initio, quantum-transport, thermal-properties
Predicting hot-electron free energies from ground-state data

Machine-learning potentials are usually trained on the ground-state, Born-Oppenheimer energy surface, which depends exclusively on the atomic positions and not on the simulation temperature. This disregards the effect of thermally excited electrons, that is important in metals, and essential to the description of warm dense matter. An accurate physical description of these effects requires that the nuclei move on a temperature-dependent electronic free energy. We propose a method to obtain machine-learning predictions of this free energy at an arbitrary electron temperature using exclusively training data from ground-state calculations, avoiding the need to train temperature-dependent potentials, and benchmark it on metallic liquid hydrogen at the conditions of the core of gas giants and brown dwarfs. This Letter demonstrates the advantages of hybrid schemes that use physical consideration to combine machine-learning predictions, providing a blueprint for the development of similar approaches that extend the reach of atomistic modeling by removing the barrier between physics and data-driven methodologies.

This record contains the raw outputs of the DFT calculations done on the training set. These files are denoted by the "training set folder #*" description. The record also contains a minimal working example showing the ML workflow for training the data and how to run the MD simulations. We include the training data in the format of XYZ files for the structures and NumPy arrays for the DFT energies, forces and DOS. We also provide a Chemiscope visualisation file of the training set.

Tags: hot-electrons, liquid-hydrogen, machine-learning, marie-curie-fellowship, marvel, snsf, warm-dense-matter
Simulated sulfur K-edge X-ray absorption spectroscopy database of lithium thiophosphate solid electrolytes

We present a sulfur K-edge X-ray absorption near-edge structure (XANES) database of 18 crystalline and 48 amorphous Lithium-Phosphorous-Sulfur (LPS) compounds. The database contains a total of 2681 XANES spectra of symmetrically inequivalent absorbing S sites. Structures were taken from Materials Cloud entry 2022.17 (archive.materialscloud.org/record/2022.17) and were originally generated by systematically removing Li, P and S atoms from known crystal structures using an evolutionary algorithm and an artificial neural network based interatomic potential. The details of this procedure can be found in Guo et al. (see references below). From this data set, low-energy structures were selected for spectral simulations. The excited electron and core hole method as implemented in VASP 6.2.1 was used to compute the XANES spectra for each symmetrically inequivalent Sulfur atom. The details of the VASP simulations can be found in the associated manuscript.

Tags: amorphous-lps, lps, vasp, x-ray-absorption-spectroscopy
DFT data for giant hardening response in AlMgZn(Cu) alloys

AiiDA calculations for the publication Giant hardening response in AlMgZn(Cu) alloys. This study presents a thermomechanical processing concept which is capable of exploiting the full indus- trial application potential of recently introduced AlMgZn(Cu) alloys. The beneficial linkage of alloy design and processing allows not only to satisfy the long-standing trade-off between high mechanical strength in use and good formability during processing but also addresses the need for economically feasible processing times. After an only 3-hour short pre-aging treatment at 100 °C, the two investigated alloys, based on commercial EN AW-5182 and modified with additions of Zn and Zn + Cu respectively, show high formability due to increased work-hardening. Then, these alloys exhibit a giant hardening response of up to 184 MPa to reach a yield strength of 410 MPa after a 20-minute short final heat treatment at 185 °C, i.e. paint-baking. This rapid hardening response strongly depends on the number density, size distribution and constitution of precursors acting as preferential nucleation sites for T-phase precursor precipitation during the final high-temperature aging treatment and is significantly increased by the addition of Cu. Minor deformation (2%) after pre-aging and before final heat treatment further enhances the development of hardening precipitates additionally by activating dislocation-supported nucleation and growth. Tensile testing, quantitative and analytical electron-microscopy methods, atom probe analysis and DFT calculations were used to characterize the alloys investigated in this work over the thermomechanical processing route. The influence of pre-strain on the hardening response and the role of Cu additions in early-stage cluster nucleation are discussed in detail and supported by in-situ STEM experiments and first-principles calculations.

Tags: aluminum, marveldd2, metallurgy
Influence of carbon on the dynamic changes in Co oxidation state of Ba0.5Sr0.5Co0.8Fe0.2O3-δ perovskite catalyst during the oxygen reduction and evolution reactions

Carbon is often used as a conductive additive in catalyst layers to increase conductivity and catalytic activity. However, the effect of carbon addition to perovskites on the oxygen reduction (ORR) and oxygen evolution (OER) reactions is convoluted. In this work, composites of perovskite Ba0.5Sr0.5Co0.8Fe0.2O3-δ (BSCF) and conductive additives, carbon and indium doped tin oxide are compared. It is found that the conductive additives have differing effects on the ORR and OER activities and cobalt redox behavior, with carbon having a much more significant effect. In order to elucidate further these differences between BSCF and BSCF/Carbon, operando X-ray absorption spectroscopy (XAS) is measured simultaneously with cyclic voltammetry into the ORR and OER regions and the continuous changes in the Co oxidation state are observed with high time resolution. We theorize that carbon is enhancing the Co redox activity and as a result, the ORR and OER activities are likewise improved.

Tags: alkaline, bscf, cobalt, electrocatalysis, oer, orr, xas
Modeling peak-aged precipitate strengthening in Al-Mg-Si alloys

Strengthening by needle-shaped β′′ precipitates is critical in Al–Mg–Si alloys. Here, the strengthening is studied computationally at the peak-aged condition where precipitate shearing and Orowan looping are usually considered to have equal strengths. Pseudo-random precipitate microstructures are constructed based on experimental precipitate dimensions and volume fractions at peak aging. A Discrete Dislocation Dynamics method is then adapted to compute the Critical Resolved Shear Stress (CRSS) for Orowan looping of dislocations moving through the non-shearable precipitate field. The CRSS for Orowan looping is determined by a typical in-situ precipitate spacing that is smaller than the average spacing and by the dislocation core energy within a radius of ≈5b, a factor rarely considered. The matrix misfit stresses, volume fraction, and precipitate shape have small effects on the CRSS. With microstructure and property details introduced as faithfully as possible, the CRSS for Orowan looping using atomistically-calibrated core energies at room temperature is nonetheless ≈33% higher than experiments. This suggests that precipitate shearing controls strength, and analyses of (i) forces acting on the precipitates, (ii) misfit stresses inside the precipitates, (iii) first-principles results for the relevant precipitate fault energies, and (iv) simulations that mimic precipitate shearing indicate a shearing CRSS closer to experiments. Thus, Orowan looping only sets an upper bound for the CRSS even at peak aging, and further quantitative progress requires detailed modeling of precipitate shearing.

Tags: aluminum, discrete-dislocation-dynamics, marveldd2, metallurgy, orowan-mechanism, precipitation-strengthening