AndreaBozzo/ceres-open-data-index huggingface dataset:
Predicting the suitability of photocatalysts for water splitting using Koopmans spectral functionals: the case of TiO₂ polymorphs

Photocatalytic water splitting has attracted considerable attention for renewable energy production. Since the first reported photocatalytic water splitting by titanium dioxide, this material remains one of the most promising photocatalysts, due to its suitable band gap and band-edge positions. However, predicting both of these properties is a challenging task for existing computational methods. Here we show how Koopmans spectral functionals can accurately predict the band structure and level alignment of rutile, anatase, and brookite TiO₂ using a computationally efficient workflow that only requires (a) a DFT calculation of the photocatalyst/vacuum interface and (b) a Koopmans spectral functional calculation of the bulk photocatalyst. The success of this approach for TiO₂ suggests that this strategy could be deployed for assessing the suitability of novel photocatalyst candidates. This record contains for each polymorph: (a) a calculation of a slab; (b) KI and pKIPZ band structure calculation of the bulk system.

Tags: electronic-structure, koopmans-spectral--functionals, marvelp4, orbital-density-dependent-functionals, photocatalysis, water-splitting
Viscous heat backflow and temperature resonances in extreme thermal conductors

We demonstrate that non-diffusive, fluid-like heat transport, such as heat backflowing from cooler to warmer regions, can be induced, controlled, and amplified in extreme thermal conductors such as graphite and hexagonal boron nitride. We employ the viscous heat equations, i.e., the thermal counterpart of the Navier-Stokes equations in the laminar regime, to show with first-principles quantitative accuracy that a finite thermal viscosity yields steady-state heat vortices, and governs the magnitude of transient temperature waves. Finally, we devise strategies that exploit devices' boundaries and resonance to amplify and control heat hydrodynamics, paving the way for novel experiments and applications in next-generation electronic and phononic technologies.

Tags: graphite, heat-hydrodynamics, temperature-resonances, thermal-viscosity
Magnetotransport in topological materials and nonlinear Hall effect via first-principles electronic interactions and band topology

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.

Tags: berry-curvature, electron-phonon-coupling, first-principles, magnetotransport, nonlinear-hall-effect, topological-materials
Electric-field-reinforced affinitive electrolytes for highly reversible aqueous zinc metal batteries

We propose an affinitive additive strategy featuring high donor number and dipole moment, exemplified by N,N-dimethylurea (DMU), to dynamically modulate Zn2+ solvation and the structure of the electric double layer under operational electric fields.  And this repository contains all computational data underlying the study, including: DFT-optimized molecular structures; initial and equilibrated configurations from MD simulations of bulk electrolyte and Zn/electrolyte interfaces; RDF and MSD analysis files; high-throughput xTB results (AIE, AEA, ion-binding energies); and machine learning scripts for descriptor screening. Data are organized by module with detailed READMEs to ensure full reproducibility.

Tags: batteries, descriptor, molecular-dynamics-simulation
Source files for microstructure and electromagnetic property data used in the paper "Influence of Ho³⁺ Substitution on the Microstructure and Electromagnetic Properties of NiZnCo Ferrites"

This document contains all raw data for microstructure and electromagnetic parameters from the unpublished thesis titled: Influence of Ho3+ Substitution on the Microstructure and Electromagnetic Properties of NiZnCo Ferrites. Its primary purpose is to provide empirical evidence demonstrating the reliability of the thesis's data.

Tags: electromagnetic-performance, ho3-substitution, niznco-ferrites, solid-phase-reaction-method
Score-based diffusion models for accurate crystal-structure inpainting and reconstruction of hydrogen positions

Generative AI models, such as score-based diffusion models, have recently advanced the field of computational materials science by enabling the generation of new materials with desired properties. In addition, these models can also be leveraged to reconstruct crystal structures for which partial information is available. One relevant example is the reliable determination of atomic positions occupied by hydrogen atoms in hydrogen-containing crystalline materials. While crucial to the analysis and prediction of many materials properties, the identification of hydrogen positions can however be difficult and expensive, as it is challenging in X-ray scattering experiments and often requires dedicated neutron scattering measurements. As a consequence, inorganic crystallographic databases frequently report lattice structures where hydrogen atoms have been either omitted or inserted with heuristics or by chemical intuition. Here, we combine diffusion models from the field of materials science with techniques originally developed in computer vision for image inpainting. We present how this knowledge transfer across domains enables a much faster and more accurate completion of host structures, compared to unconditioned diffusion models or previous approaches solely based on DFT. Overall, our approach exceeds a success rate of 97% in terms of finding a structural match or predicting a more stable configuration than the initial reference, when starting both from structures that were already relaxed with DFT, or directly from the experimentally determined host structures.

Tags: aiida, dft, generative-ai, machine-learning, mlip
Cost-effective multi-channel MolOrbImage for machine-learned excited-state properties of practical photofunctional materials

Leveraging our recent development, which incorporates hole and particle information into the multi-channel molecular orbital image (MolOrbImage), to generate exceptional accuracy (mean absolute error, MAE < 0.1 eV) in predicting excited-state energies of practical photofunctional materials containing several hundred atoms, we have advanced the implementation of a new approach to overcome the high computational cost of mean-field ground-state calculations that limits its application in high-throughput materials discovery. In this work, low-cost approaches for generating approximate orbitals, including the superposition of atomic densities technique and the semi-empirical tight-binding method, have been employed to construct cost-effective multi-channel MolOrbImages. By connecting with a convolutional neural network, the performance of our model is evaluated for both small organic molecules (MAE < 0.1 eV) and practical photofunctional materials (MAE < 0.14 eV). Perturbation analysis of MolOrbImages highlights the importance of frontier orbital energies, which further motivates the adoption of transfer learning techniques to reduce prediction errors in excited-state energies.

Tags: excited-state, machine-learning, photofunctional-materials
Long-range van der Waals forces between graphene and copperene: first principles calculations

Van der Waals (vdW) interactions play a crucial role in the formation of various systems. Two-dimensional (2D) metals have attracted considerable attention due to their unique properties, particularly in applications where vdW interactions play a significant role.  In particular, a graphene template facilitates the self-assembly of 2D metal nanosheets. A study of vdW interactions between copperene and graphene has been carried out. The behavior of the binding energy of copperene/graphene is studied within the framework of DFT-D3.Collection of data, input and output files. Calculations were performed using Quantum ESPRESSO.

Tags: input-files, output-files, quantum-espresso
Skeletal editing of electrolyte molecule by oxygen-distal fluorination for 500 Wh kg–1, cycling stable Li metal battery

Enhancing cycling reversibility in high-energy-density lithium metal batteries (LMBs) necessitate precise management of electrolyte-derived electrochemical reactions at electrodes and interphases, yet the recently developed and most commonly used localized high-concentration electrolytes (LHCEs) technology suffers from limited tunability on these reactions due to the non-solvation-participating nature of oxygen-proximal fluorinated diluents. Here we address this issue by designing and synthesizing a new oxygen-distal fluorinated diluent. Unlike traditional oxygen-proximal fluorinated analogs, the molecule skeleton was strategically edited to position fluorine atoms distal to oxygen centers that attenuates electron-withdrawing effects at these Li+-coordination sites, enabling: enhanced diluent/anion participation and reduced volatile solvents in solvation shells. This work establishes oxygen-distal fluorination as a new electrolyte molecular skeleton editing principle for stable energy-dense LMBs.

Tags: lithium-metal-battery, molecule-design, theoretical-calculation
Two-dimensional RMSD projections for reaction path visualization and validation

This record contains the complete optimization trajectories, potential model, and analysis data for the Nudged Elastic Band (NEB) calculation of the ethylene + N$_2$O cycloaddition reaction. This dataset supports the accompanying manuscript "Two-dimensional RMSD projections for reaction path visualization and validation" and enables the full reproduction of the reported 2D RMSD landscapes and 1D energy profiles.

The provided data encompasses the entire computational workflow: from the pre-trained PET-MAD machine-learned potential and initial endpoint geometries to the intermediate NEB optimization steps and the final converged reaction path.

Tags: nudged-elastic-band, saddle-search-methods, transition-states, visualization
Beyond-quasiparticle transport with vertex correction: self-consistent ladder formalism for electron-phonon interactions

This file contains all the data, as well as the code necessary to reproduce the results of Jae-Mo Lihm and Samuel Ponce, "Beyond-quasiparticle transport with vertex correction: self-consistent ladder formalism for electron-phonon interactions." We present a self-consistent many-body framework for computing phonon-limited electronic transport from first principles, incorporating both beyond-quasiparticle effects and vertex corrections. Using the recently developed first-principles scGD0 method, we calculate spectral functions with nonperturbative effects such as broadening, satellites, and energy-dependent renormalization. We show that the scGD0 spectral functions outperform one-shot G0D0 and cumulant approximations in model Hamiltonians and real materials, eliminating unphysical spectral kinks and correctly predicting the phonon emission continuum. Building on this, we introduce the self-consistent ladder formalism for transport, which captures vertex corrections due to electron-phonon interactions. This approach unifies and improves upon the two state-of-the-art approaches for first-principles phonon-limited transport: the bubble approximation and the Boltzmann transport equation. Moreover, as a charge-conserving approximation, it enables consistent calculations of the optical conductivity and dielectric function. We validate the developed method against numerically exact results for model Hamiltonians and apply it to real materials. Our results show quantitative agreement with the experimental dc conductivities in intrinsic semiconductors Si and ZnO and the SrVO3 metal, as well as excellent agreement with the experimental THz optical and dielectric properties of Si and ZnO. This work unifies first-principles and many-body approaches for studying transport, opening new directions for applying many-body theory to materials with strong electron-phonon interactions.

Tags: electron-phonon-coupling, first-principles, transport
Physics-inspired equivariant descriptors of non-bonded interactions

One essential ingredient in many machine learning (ML) based methods for atomistic modeling of materials and molecules is the use of locality. While allowing better system-size scaling, this systematically neglects long-range (LR) effects, such as electrostatics or dispersion interaction. We present an extension of the long distance equivariant (LODE) framework that can handle diverse LR interactions in a consistent way, and seamlessly integrates with preexisting methods by building new sets of atom centered features. We provide a direct physical interpretation of these using the multipole expansion, which allows for simpler and more efficient implementations. The framework is applied to simple toy systems as proof of concept, and a heterogeneous set of molecular dimers to push the method to its limits. By generalizing LODE to arbitrary asymptotic behaviors, we provide a coherent approach to treat arbitrary two- and many-body non-bonded interactions in the data-driven modeling of matter.

Tags: dispersion, electrostatics, erc, long-range-interactions, machine-learning
Enhanced Climbing Image Nudged Elastic Band method with Hessian Eigenmode Alignment

Accurate determination of transition states is central to an understanding of reaction kinetics. Double-endpoint methods where both initial and final states are specified, such as the climbing image nudged elastic band (CI-NEB), identify the minimum energy path between the two and thereby the saddle point on the energy surface that is relevant for the given transition, thus providing an estimate of the transition state within the harmonic approximation of transition state theory. Such calculations can, however, incur high computational costs and may suffer stagnation on exceptionally flat or rough energy surfaces. Conversely, methods that only require specification of an initial set of atomic coordinates, such as the minimum mode following (MMF) method, offer efficiency but can converge on saddle points that are not relevant for transition of interest. Here, we present an adaptive hybrid algorithm that dynamically switches between the CI-NEB with the MMF method ("off-path" CI-NEB) so as to get faster convergence to the relevant saddle point. The method is benchmarked for the Baker-Chan (BC) saddle point test set using the PET-MAD machine-learned potential as well as 59 transitions of a heptamer island on Pt(111) from the OptBench benchmark set. A Bayesian analysis of the performance shows a median change in energy and force calculations of -46% [95% CrI: -55%, -37%] relative to CI-NEB for the BC set, while a 28% reduction is found for the transitions of the heptamer island. A fixed cutoff static switch mechanism for the BC leads to a median increase of 31% calculations compared to the OCI-NEB algorithm.  These results establish the OCI-NEB method as a highly effective tool for high-throughput automated chemical discovery of atomic rearrangements.

 

This record provides the reproduction data, transition state search logs, and statistical models for the Off-path Climbing Image Nudged Elastic Band (OCI-NEB) algorithm. It includes the complete outputs to accompany the Github repository of scripts which support the manuscript.

Tags: machine-learning, nudged-elastic-band, saddle-search, transition-state
Discovery of thermochemical energy storage materials via a hybrid data-mining approach

A key technical challenge inhibiting fossil-free energy generation, which is especially pronounced  in solar plants, is the inherent intermittency. Since thermochemical energy storage represents a  solution that has yet to see large-scale utilization, the goal of the study at hand has been to find  suitable materials using a hybrid high-throughput approach, encompassing both data mining and  first principles calculations. Through these efforts, thermal properties have been estimated for more  than 50 000 mono-, bi-, and trimetallic oxides, as well as alloys, retrieved from the Materials Project  database.Thereafter, close to 18 000 convex hulls were created, each representing a unique metal ratio, in order  to determine the most stable phases at different oxygen chemical potentials. In turn, this allowed  almost 300 000 potential transitions to be identified. After filtering out potentially toxic and rare  compositions, a little over 51 000 remained, which were further reduced, to about 3000, by requiring  that the phase transformation must occur at a temperature between 400 °C and 1300 °C.A thorough analysis of the results led to the discovery of several promising energy storage materials.  In addition, Chromium; manganese; calcium; and magnesium were found to be associated with high  reaction enthalpies. When the sensible heat was taken into account, however, light elements such as  lithium; boron; iron; and sodium dominated among the top-ranking candidates. This study therefore  demonstrates how high-throughput data mining can be efficiently enhanced through first-principles  calculations, enabled by machine-learning interatomic potentials, to facilitate material discovery.

Tags: data-mining, first-principles, high-throughput, machine-learning-potentials, metal-oxides, thermochemical-energy-storage, thermodynamics
Supporting data for the paper "Algebraic loop liquid in the pyrochlore CsNiCrF6"

The 𝛽-pyrochlore is a charge ice – each tetrahedron of the pyrochlore lattice is occupied by two Ni2+ and two Cr3+ according to an ice rule, resulting in fully packed same-species loops with power-law length distribution. Using inelastic neutron scattering and numerical simulations, we show that the spin dynamics can be well described by a model with three antiferromagnetic interaction strengths, one for Ni-Ni, Cr-Ni, and Cr-Cr interactions. Because the strengths of the two intra-loop interactions are quite different, the fluctuations of each type of loop can be identified in the spectrum. therefore forms an algebraic loop liquid in which power-law cation correlations control the spin dynamics. Our parameters predict a transition to spin nematic order with associated arrest of dynamics at 𝑇𝑛≈0.55 K, while we observe a freezing transition at 𝑇𝑓=2.3K. Measurements of magnetisation, ac and non-linear susceptibility show that this is not a canonical spin glass transition so we associate it with the development of slow dynamics due to inter-loop nematic correlations.

Tags: correlated-disorder, frustrated-magnetism, inelastic-neutron-scattering
Resolving the body-order paradox of machine learning interatomic potentials

This dataset contains the model training scripts, training sets, evaluation results, and analysis notebooks associated with the work "Resolving the Body-Order Paradox of Machine Learning Interatomic Potentials" by Chong et al. Abstract of the work is shared below:

 

"In many cases, the predictions of machine learning interatomic potentials (MLIPs) can be interpreted as a sum of body-ordered contributions, which is explicit when the model is directly built on neighbor density correlation descriptors, and implicit when the model captures the correlations through non-linear functions of low body-order terms. In both cases, the "effective body-orderedness" of MLIPs remains largely unexplained: how do the models decompose the total energy into body-ordered contributions, and how does their body-orderedness affect the accuracy and learning behavior? In answering these questions, we first discuss the complexities in imposing the many-body expansion on ab initio calculations at the atomic limit. Next, we train a curated set of MLIPs on datasets of hydrogen clusters and reveal the inherent tendency of the ML models to deduce their own, effective body-order trends, which are dependent on the model type and dataset makeup. Finally, we present different trends in the convergence of the body-orders and generalizability of the models, providing useful insights for the development of future MLIPs."

Tags: atomic-cluster-expansion, body-orderedness, machine-learning-interatomic-potentials, many-body-expansion
Refining interface stress measurement in nanomultilayers through layer corrugation and interface roughness corrections

This study introduces new models that incorporate layer corrugation and interface roughness into standard approaches for measuring interface stress in nanomultilayers (NMLs). Applied to Cu/W NMLs, these models show that ignoring such features can inflate measured interface stress by up to 0.4 J/m^2. However, corrugation and roughness alone cannot account for the extreme stresses reported, suggesting that atomic-scale phenomena (e.g., intermixing and metastable phase formation at the interfaces) dominate. These findings highlight the importance of balancing bilayer counts and thickness-to-roughness ratios for reliable stress quantification, providing a practical pathway to designing and characterizing advanced nanocomposite coatings with improved accuracy.

Tags: corrugations, interface-stress, marvelp1, model, nanomultilayers, roughness
Symmetry-protected topological polarons

Emergent quasiparticles in solids often exhibit unique topological properties as a result of the complex interplay between charge, orbital, spin and lattice degrees of freedom. Among these quasiparticles, the polaron occupies a special place as the first known manifestation of the interaction between a fermion and a boson field. While polarons have been investigated for almost a century, whether these quasiparticles exhibit topological properties and why remain open questions. Here, we establish the universal symmetry principles governing the topology of polar textures in large polarons. Using a group-theoretic analysis, we identify four distinct classes of polar textures in time-reversal-invariant systems, and we show that they carry integer topological charges. We validate this classification by performing state-of-the-art first-principles calculations of materials representative of each class. For these materials, we compute the fingerprints of polaron topology in Huang diffuse scattering, and propose ultrafast electron and X-ray scattering experiments to detect these quasiparticles.

This archive contains the data necessary to reproduce the results of the "Symmetry-protected topological polarons" article by Kaifa Luo, Jon Lafuente-Bartolome, and Feliciano Giustino.

Tags: diffuse-scattering, polarons, topology