AndreaBozzo/ceres-open-data-index huggingface dataset:
Incorporating static intersite correlation effects in vanadium dioxide through DFT+V

We analyze the effects on the structural and electronic properties of vanadium dioxide (VO₂) of adding an empirical inter-atomic potential within the density-functional theory+V (DFT+V) framework. We use the DFT+V machinery founded on the extended Hubbard model to apply an empirical self-energy correction between nearest-neighbor vanadium atoms in both rutile and monoclinic phases, and for a set of structures interpolating between these two cases. We observe that imposing an explicit intersite interaction V along the vanadium-vanadium chains enhances the characteristic bonding-antibonding splitting of the relevant bands in the monoclinic phase, thus favoring electronic dimerization and the formation of a band gap. We then explore the effect of V on the structural properties and the relative energies of the two phases, finding an insulating global energy minimum for the monoclinic phase, consistent with experimental observations. With increasing V, this minimum becomes deeper relative to the rutile structure, and the transition from the metallic to the insulating state becomes sharper. We also analyze the effect of applying the +V correction either to all or only to selected vanadium-vanadium pairs, and both in the monoclinic as well as in the metallic rutile phase. Our results suggest that  DFT+V can indeed serve as a computationally inexpensive unbiased way of modeling VO₂ which is well suited for studies that, e.g., require large system sizes.

Tags: dft, dftuv, vo2
Dataset for characterization of fracture and elastic properties of commercially available polyurethane foam and short fiber filled epoxy for bone models

In the field of orthopedic biomechanics, synthetic bone surrogates are essential for evaluating surgical techniques, implant stability, and fracture risks due to their reproducible geometry and consistent mechanical behavior compared to human cadaveric tissue. Fourth-generation (4th Gen) Sawbones(R) composite models, which utilize a short fiber-filled epoxy (SFFE) cortical shell and a polyurethane foam (PUF) cancellous core, represent the industry standard. However, a significant gap remains in the literature regarding the full anisotropic characterization of these individual constituents, which is critical for high-fidelity numerical simulations. This study presents a comprehensive experimental dataset covering uniaxial tension (ASTM D638), compression (ASTM D695), and fracture toughness (ASTM E399) tests, alongside specialized shear characterization using a modified Single Cube Apparatus (SCA). The dataset includes 93 successful mechanical tests: 56 for the SFFE cortical surrogate and 37 for the PUF cancellous surrogate. The results validate the transverse isotropy of the SFFE and the isotropic behavior of the PUF. These findings provide the required mechanical properties for accurate finite element studies, facilitating the rigorous validation of synthetic bone surrogates as representations of biological human tissue.

Tags: anisotropy, bone-substitutes, characterization, experimental, fracture-toughness, sawbones
Inverse design of heterodeformations for strain soliton networks in bilayer 2D materials

Strain soliton networks strongly influence the structural and electronic properties of heterodeformed bilayer systems, yet their design remains challenging due to the high dimensionality of heterodeformation space and the absence of a direct map between deformation and network geometry. In this work, we introduce a geometric framework that establishes a one-to-one mapping between heterodeformations and the geometry of the strain soliton network expressed as line vector-Burgers vector pairs. The admissible networks are constrained by topology dictated by the generalized stacking fault energy landscape. We show that the moiré Bravais lattice, corresponding to a uniform heterodeformation, alone is insufficient to characterize the interface: distinct heterodeformations can share identical moiré Bravais lattices while producing different soliton networks, reflecting an inherent many-to-one mapping when only translational symmetry is considered. In contrast, the soliton network encodes the full multilattice geometry of the interface, including topology and connectivity, which are not captured by the moiré Bravais lattice alone. The proposed framework enables the direct construction of heterodeformations from target networks, providing a systematic route for inverse design of moiré interfaces beyond conventional twist-based approaches.

Tags: heterodeformations, inverse-design, strain-soliton-networks
A kinetic Monte Carlo dataset of monoatomic step roughening for deep generative models approximation of stochastic dynamics

Deep Generative models have shown impressive capabilities in several applications, e.g., image, video, and audio synthesis. Importantly, they can infer probability distributions from data implicitly. For this reason, they have promising applications in learning stochastic dynamics, with potential applications in computational condensed matter physics and materials science. Indeed, random thermal fluctuations have a key role in several phenomena, such as surface roughening, nucleation, crystal growth, and phase transitions. Here we provide a comprehensive dataset of trajectories of one of such systems, i.e., the progressive roughening of monoatomic steps on top of a simple cubic (100) surface. The system has been simulated with a Kinetic Monte Carlo approach, implementing the edge diffusion conditions. Among other characteristics, this system is convenient because its dynamics can be represented as a series of binary images, with white and black pixels corresponding to occupied and unoccupied surface sites, respectively. The repository contains 3 KMC datasets composed of 300 independent trajectories each. Every trajectory contains 1000 subsequent states. The datasets differ in the initial condition (one flat stripe and one undulated one on a 64x64 computational cell) and the domain size (one undulated stripe on a 72x64 computational cell). Evolutions predicted by a Generative Adversarial Network approach trained on the flat stripe dataset are also provided, serving as a benchmark for comparing alternative implementations on the same problem. These comprise the evolution of flat profiles on domains of different lengths (32, 48, 64, 72, 80, 96, and 128) to the equilibrium value and relaxation dynamics from undulated profiles conformal to those used in the KMC datasets.

Tags: deep-learning, edge-diffusion, generative-adversarial-network, kinetic-monte-carlo, neural-networks, roughness, stochastic-dynamics, surface-science
A FEM dataset of Ge film profiles and elastic energies for machine learning approximation of strain state and morphological evolution

Machine Learning (ML) can be conveniently applied to continuum materials simulations, allowing for the investigation of larger systems and longer timescales, pushing the limits of tractable systems. Here we provide a comprehensive dataset of strained Ge films on Si and their corresponding strain states, which can be used to train a ML model capable of such acceleration. Approximately 80k 2D cases are included, reporting the profiles h(x) and the corresponding elastic energy densities and strain fields. The profiles are conveniently sampled using Perlin-noise and pure-sine waves. A 100nm-large computational domain is considered. The mechanical equilibrium problem is solved using Finite Element Method (FEM). Ge is modeled as an isotropic material and an eigenstrain of 3.99% is used, as in Ge/Si(001).
The database has been exploited for training a (fully) Convolutional Neural Network (CNN) which maps the free surface profile h(x) to the corresponding energy density. If plugged into the proper time-dependent Partial Differential Equation, this term can be used to accelerate continuum simulations of the morphological evolution of strained films while retaining FEM-level accuracy. Tests of the reliability of such CNN model are also provided in the repository, together with the output of surface morphology minimization procedures and morphological evolution simulations during coarsening and growth. In the latter, evolution by surface diffusion has been considered as an important case, but applications to other mechanisms are possible. Generalization examples to larger computational cells with respect to those in the dataset are also available.

Tags: continuum-models, convolutional-neural-network, elastic-energy, finite-element-method, germanium, machine-learning, strained-films, surface-evolution, thin-films
Towards a unified first-principles-based description of VO2 using DFT+DMFT with bond-centered orbitals

We present a combined density-functional theory and dynamical mean-field theory (DFT+DMFT) study of the full structural phase space of rutile-based vanadium dioxide (VO2), including also the less studied M2 and T phases, using an unconventional bond-centered orbital basis. The use of bond-centered orbitals allows us to treat all main phases of VO2, and the structural transitions between them, using one consistent approach with moderate computational cost and without pre-pattering of the structure into dimerized and undimerized V--V pairs. We obtain two distinct insulating states on the two different types of vanadium chains in the M2 phase, a singlet-insulator on the dimerized chains and a Mott-insulator on the zigzag-distorted chains, which, however, are strongly coupled in the M2 phase and thus the metal-insulator transition always occurs concomitantly for both types of sites. We also demonstrate that the M2 phase corresponds to a local energy minimum in the structural phase space of VO2, the stability of which, apart from the internal structural distortion, depends crucially on the unit cell strain relative to the undistorted rutile phase. Our calculations further indicate that the symmetry-distinct triclinic T phase corresponds electronically to either an M1 or an M2-type insulator with an abrupt transition as a function of distortion. Finally, we disentangle the effect of the dimerization and zigzag distortions by constructing hypothetical structures that contain only one site type, finding that the zigzag distortion strongly favors emergence of the Mott-insulating state, both as function of distortion and on-site interaction.

Tags: dft, dftdmft, mit, vo2
Molecular dynamics insights into the shear-induced orientation and interfacial enhancement in functionalized CNTs and graphene/PVA composite fibers

This is a summary of previous work, presenting a simulation dataset for the interactions between functionalized/unfunctionalized carbon nanomaterials and PVA. The primary objective is to investigate flow-assisted orientation and surface properties. Unreliable LAMMPS stress-strain simulations were excluded, and the rationality of the pull-out, nanoscratch, and cooling simulations was validated.

The synergistic reinforcement of polyvinyl alcohol (PVA) fibers using carbon nanomaterials is heavily constrained by interfacial compatibility and dynamic spatial orientation during the wet-spinning process. In this study, confined-shear molecular dynamics (MD) simulations were employed to systematically investigate the interfacial mechanics, structural evolution, and shear-induced orientation of PVA composites reinforced with one-dimensional (SWNT) and two-dimensional (graphene/graphene oxide) carbon nanofillers. The results reveal that surface functionalization (-COOH, -OH) significantly enhances interfacial adhesion via dense hydrogen bond networks, shifting the failure mechanism to highly dissipative plastic deformation and improving wear resistance. Interestingly, under static equilibrium, the functionalized 1D/2D hybrid system (GO+SWNT-COOH/PVA) exhibits an anomalous antagonistic effect in mechanical moduli due to spatial crowding and conformational frustration. However, the application of a dynamic shear flow field effectively overcomes this static steric hindrance, inducing a highly ordered axial alignment of both polymer chains and nanofillers. Consequently, the post-shear hybrid system unlocks its multi-scale synergistic reinforcement potential, achieving a remarkable axial Young's modulus of 8.10 GPa. These atomistic insights elucidate the critical transition from static interfacial chemistry to dynamic macroscopic orientation, providing robust theoretical guidance for the fabrication of high-performance PVA composite fibers.

Tags: carbon-nanotubes, graphene, md, pva
Dataset for Energy level alignment of vacancy-ordered halide double perovskites

Vacancy-ordered double perovskites have emerged as lead-free alternatives, offering remarkable stability and compositional tunability for optoelectronic applications. In this study, we provide first-principles insights into their electronic properties, surface stability, and energy level alignment using a non-empirical dielectric-dependent hybrid functional. For representative family of the Cs2MX6, with M = Zr, Sn, Te, and X= Cl, Br, I, the predicted electronic band gaps are comparable to those obtained with the state-of-the-art GW method. We investigate the stability of these materials under simulated experimental conditions, considering both the rich and poor chemical potentials of their precursor salts. Our results indicate distinct regions of surface energy stability that favor CsX terminations. In contrast, MX4 terminations show in-gap surface states, which can act as trap states and reduce carrier lifetime. Finally, based solely on the intrinsic absolute energy levels, we identify promising candidates as charge transport/injection layers for typical photovoltaic and light-emitting applications.

Tags: absolute-energy-levels, charge-transport-layer, density-functional-theory, dielectric-dependent-hybrid-functional, surface, vacancy-ordered-double-perovskites
Ab initio study of strain and quantum confinement shaping the valence-band structure of Ge quantum wells

Germanium–silicon–germanium (Ge/SixGe1-x) heterostructures have emerged as a prominent platform for high-mobility electronic devices and hole-spin–based quantum technologies. In this work, we present an ab initio study of strained germanium, demonstrating that biaxial compressive strain in a Ge quantum well strongly alters the valence-band structure. Specifically, the strain lifts the heavy-hole/light-hole degeneracy and modifies the effective masses (significantly enhances hole mobility). Our findings offer a comprehensive theoretical description of the combined effects of strain and quantum confinement on the valence bands of Ge quantum wells, providing a solid foundation for predictive modeling of Ge-based high-mobility electronics and hole-spin qubits.

Tags: aiida, dft, quantum-confinement, quantum-espresso, strained-germanium, tight-binding, wannier-interpolation, wannier90
Exploring charge density waves in two-dimensional NbSe2 with machine learning

Niobium diselenide (NbSe2) has garnered significant attention due to the coexistence of superconductivity and charge density waves (CDWs) down to the monolayer limit. However, realistic modeling of CDWs—capturing effects such as layer number, twist angle, and strain—remains challenging due to the high computational cost of first-principles methods. Here, we develop a physically informed workflow for training machine-learning interatomic potentials (MLIPs) based on the E(3)-equivariant Allegro architecture, tailored to capture the subtle structural and dynamical signatures of CDWs in mono- and bilayer NbSe2. We find that while CDW lattice distortions are relatively easy to learn, modeling vibrational properties remains more challenging. It requires targeted dataset design and careful hyperparameter tuning, pushing the boundaries and testing the extensibility of current MLIP frameworks. Our MLIPs enable reliable simulations of commensurate and incommensurate CDW phases, including their sensitivity to dimensionality and stacking, as well as CDW dynamics, phonons, and transition temperatures estimated via the stochastic self-consistent harmonic approximation. This work opens new possibilities for studying and tuning CDWs in NbSe2 and other two-dimensional systems, with implications for electron-phonon coupling, superconductivity, and advanced materials design.

Tags: 2d-materials, charge-density-waves, machine-learned-interatomic-potentials
One dimensional edge localized YSR states in CrCl₃ on NbSe₂

Magnet/superconductor hybrid systems have been put forward as a platform for realizing topological superconductivity. We investigated the heterostructure of ferromagnetic monolayer CrCl₃ and superconducting NbSe₂. Using low-temperature scanning tunneling microscopy, we observe topologically trivial Yu-Shiba-Rusinov (YSR) states localized at the edge of CrCl₃ islands. DFT simulations reveal that the Cr atoms at the edge have an enhanced d-orbital DOS close to the Fermi energy. This leads to an exchange coupling between these atoms and the substrate that rationalizes the edge-localization of the YSR states.

This dataset contains the first-principles calculations performed on a nanoribbon of CrCl₃ on NbSe₂ and the associated notebooks used to generate figures from this data.

Tags: aiida, aiida-siesta, crcl3, density-functional-theory, h2020, heterostructure, horizon-europe, magnetism, nbse2, prace, siesta, superconductivity, van-der-waals, yu-shiba-rusinov
Bayesian optimization with Gaussian processes to accelerate stationary point searches

Accelerating the explorations of stationary points on potential energy surfaces building local surrogates spans decades of effort. Done correctly, surrogates reduce required evaluations by an order of magnitude while preserving the accuracy of the underlying theory. We present a unified Bayesian Optimization view of minimization, single point saddle searches, and double ended saddle searches through a unified six-step surrogate loop, differing only in the inner optimization target and acquisition criterion. The framework uses Gaussian process regression with derivative observations, inverse-distance kernels, and active learning. The Optimal Transport GP extensions of farthest point sampling with Earth mover's distance, MAP regularization via variance barrier and oscillation detection, and adaptive trust radius form concrete extensions of the same basic methodology, improving accuracy and efficiency. We also demonstrate random Fourier features decouple hyperparameter training from predictions enabling favorable scaling for high-dimensional systems. Accompanying pedagogical Rust code demonstrates that all applications use the exact same Bayesian optimization loop, bridging the gap between theoretical formulation and practical execution.

 

This dataset contains the raw data and models for generating the results in the manuscript.

Tags: bayesian, gaussian-process-regression, minimization, saddle-search
Reproducible orchestration of best practices for reaction path optimization with the nudged elastic band

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.

Tags: computational-chemistry, nudged-elastic-band, transition-state
Comparing Hubbard parameters from linear-response theory and a Hartree-Fock-based approach

Density-functional theory with on-site U and inter-site V Hubbard corrections (DFT+U+V) is widely used to predict properties of transition-metal and rare-earth compounds, but its accuracy depends critically on how these parameters are obtained. While they can be determined empirically, first-principles approaches offer better consistency, though their results can differ and a systematic comparison is lacking. Here, we compare two widely used approaches, linear-response theory (LRT) and the Hartree–Fock-based pseudohybrid functional formalism, applied to representative oxides (MnO, NiO, CoO, FeO, BaTiO3, ZnO, and ZrO2). For partially occupied transition-metal d states, both methods yield comparable U values, but they differ significantly for nearly empty or fully filled d shells. For O-2p states, LRT systematically predicts large U values (~10 eV), whereas the pseudohybrid approach gives system-dependent values. Even larger differences appear for the inter-site V: the former yields small values (<1 eV), while the latter gives larger values (~3 eV) due to its dependence on charge redistribution. We further show that the pseudopotential choice, which defines Hubbard projectors, affects the parameters and their agreement. Overall, while parallels between these two methods exist, they rely on different assumptions, leading to variations in predictions of material properties.

Tags: batio3, coo, dftu, dftuv, feo, mno, nio, quantum-espresso, zno, zro2
Rototranslational sum rules for nuclear dynamics via traveling pseudopotentials

We establish a set of exact sum rules that relate the interatomic force constants to the frequency-dependent electromagnetic susceptibility of a solid or molecule, thereby generalizing the long-established principles of rototranslational symmetry to the nonadiabatic regime. Crucially, we show that in practical numerical implementations these sum rules are violated, unless special precautions are taken in the treatment of the atomic pseudopotentials. We solve these issues once and for all by correctly adapting the pseudopotential to the motion of the corresponding nucleus, with a velocity dependence of the nonlocal operator. This prescription restores the correct Galilean covariance of the Schr"odinger equation, and the expected identity between mechanical rototranslations and electromagnetic perturbations. These results conclusively fix a number of worrisome inconsistencies that were pointed out over the years in the context of linear-response theory restoring, e.g., the validity of the Larmor theorem, and the equivalence between the inertial and electrical definitions of the Drude weight in metals.

This dataset contains the input and output files used to produce the results presented in the manuscript.

Tags: density-functional-theory, nonadiabatic-regime, pseudopotentials
Oxygen vacancies in vanadium dioxide: A DFT+V study

We present a density-functional theory study of the effects of oxygen vacancies on the structural and electronic properties of vanadium dioxide (VO2). Our motivation is the reported suppression of the metal-insulator transition by oxygen vacancies and the lack of a clear consensus on its origin. We use the DFT+V method with a static intersite vanadium-vanadium interaction term, V, to calculate the properties of the oxygen-deficient metallic rutile and insulating monoclinic M1 phases of VO2 on the same footing. We find that oxygen vacancies induce local distortions in the M1 phase, but do not destroy the dimerization usually associated with the insulating behavior. In spite of this, we find that the M1 phase becomes metallic as a result of the partial filling of the conduction band due to a rigid-band-like doping effect.

Tags: dft, dftuv, vacancy, vo2