cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:ks-ma
title: Graphene nanoribbons with mixed cove-cape-zigzag edge structure

description: A recently developed bottom-up synthesis strategy enables the fabrication of graphene nanoribbons with well-defined width and non-trivial edge structures from dedicated molecular precursors. Here we discuss the synthesis and properties of zigzag nanoribbons (ZGNRs) modified with periodic cove-cape-cove units along their edges. Contrary to pristine ZGNRs, which show antiferromagnetic correlation of their edge states, the edge-modified ZGNRs exhibit a finite single particle band gap without localized edge states. We report the on-surface synthesis of such edge-modified ZGNRs and discuss tunneling conductance dI/dV spectra and dI/dV spatial maps that reveal a noticeable localization of electronic states at the cape units and the
opening of a band gap without presence of edge states of magnetic origin. A thorough ab initio investigation of the electronic structure identifies the conditions under which antiferromagnetically coupled, edge-localized states reappear in the electronic structure. Further modifications of the ribbon structure are proposed that lead to an enhancement of such features, which could find application in nanoelectronics and spintronics. 
The record contains input files to reproduce the calculations discussed in the manuscript and the raw data of the experimental images discussed.
DOI: 10.24435/materialscloud:r6-zb
title: High energy barriers for edge dislocation motion in body-centered cubic high entropy alloys

description: Recent theory proposes that edge dislocations in random body-centered cubic (BCC) high entropy alloys have high barriers for motion, conveying high strengths up to high temperatures. Here, the energy barriers for edge motion are computed for two model alloys, NbTaV and MoNbTaW as represented by interatomic potentials, using the Nudged Elastic Band method and compared to theoretical predictions. The average magnitude of the barriers and the average spacing of the barriers along the glide direction agree well with the analytical theory, with no adjustable parameters. The evolution of the barriers versus applied stress is modeled, and the mean strength is in reasonable agreement with the predicted zero-temperature strength. These findings validate the analytic theory. A reduced analytic model based on solute misfit volumes is then applied to Hf-Mo-Nb-Ta-Ti-Zr and Mo-Nb-Ta-Ti-V-W alloys, rationalizing the observed significant strength increases at room temperature and 1000 ∘C upon addition of solutes with large misfit into a base alloy. The analytic theory for edge motion is thus a powerful validated tool for guiding alloy selection.
DOI: 10.24435/materialscloud:zp-ez
title: Adaptive energy reference for machine-learning models of the electronic density of states

description: The electronic density of states (DOS) provides information regarding the distribution of electronic states in a material, and can be used to approximate its optical and electronic properties and therefore guide computational material design. Given its usefulness and relative simplicity, it has been one of the first electronic properties used as target for machine-learning approaches going beyond interatomic potentials. A subtle but important point, well-appreciated in the condensed matter community but usually overlooked in the construction of data-driven models, is that for bulk configurations the absolute energy reference of single-particle energy levels is ill-defined. Only energy differences matter, and quantities derived from the DOS are typically independent on the absolute alignment. We introduce an adaptive scheme that optimizes the energy reference of each structure as part of training, and show that it consistently improves the quality of ML models compared to traditional choices of energy reference, for different classes of materials and different model architectures. On a practical level, we trace the improved performance to the ability of this self-aligning scheme to match the most prominent features in the DOS. More broadly, we believe that this work highlights the importance of incorporating insights into the nature of the physical target into the definition of the architecture and of the appropriate figures of merit for machine-learning models, that translate in better transferability and overall performance.
This record contains all the necessary data files and scripts to support the results presented in the paper with the same title.
DOI: 10.24435/materialscloud:v5-5z
title: Density of states of full and inverse Heusler magnetic alloys

description: The provided AiiDA database contains magnetic electronic structure calculations from a selection of full (L2₁) and inverse (XA) Heusler alloys. The examined crystal structures were taken from the Materials Project database. The data was obtained to examine the spin-polarization of the density of states amongst different compounds. The FLAPW code FLEUR has been combined with the GGA-based PBE functional to compute the structures. Spin-orbit interaction has been considered in second variation within the presented calculations. 
Complete list of included Heusler compounds: 
– L2₁: Co₂CrSb, Co₂GeCr, Co₂HfAl, Co₂HfGa, Co₂HfIn, Co₂ScGe, Co₂SnNb, Co₂VZn, Co₂ZnGe, Co₂ZnNb, Co₂ZnTa, Co₂ZrGa, Cu₂FeSn, Cu₂MnSb, Cu₂MnSn, Fe₂CoGa, Fe₂CoGe, Fe₂CoSi, Fe₂CrGa, Fe₂CrSi, Fe₂CrSi, Fe₂CrSn, Fe₂CrSn, Fe₂TaGe, Fe₂TiGa, Fe₂TiIn, FeMn₂P, Ir₂FeGa, Ir₂TcTl, Mn₂CrSi, Mn₂RuSi, Mn₂TaGe, Mn₂VIn, Mn₂WGa, Ni₂FeGa, Ni₂MnSb, Ni₂MnSi, Ni₂MnSn, Rh₂FeGa, Rh₂FeIn, Rh₂FeSn, Rh2MnSi, Rh2MnSn, Rh2TiMn, Rh2ZnFe, Ru2FeGe, Ru2FeSi, Ru2FeSn, Ru2ZrSb, Sc2MnSi, Sc2MnSn, ScCo2Sn, and Ti2CoIr
– XA: Fe₂CoGa, Fe₂CoSi, Fe₂NiSi, Mn₂CoGa, Mn₂CoGe, Mn₂CoIn, Mn₂CoSb, Mn₂CoSi, Mn₂CoSn, Mn₂CuGe, Mn₂CuSb, Mn₂NiSb, Mn₂NiSi, Ni₂MnSb, Sc₂MnSi, Sc₂MnSn, Ti₂CoGe, Ti₂CoIn, Ti₂CoSi,  Ti₂CuAl, Ti₂FeSi, Ti₂NiAl, Ti₂NiGa, Ti₂NiIn, Ti₂MnSi, Ti₂ZnAl, and V₂NiSb
DOI: 10.24435/materialscloud:98-53
title: A directional tensile superelasticity in ceramic crystal via reversible shuffle twinning

description: Superelasticity, being a reversible nonlinear strain response to stress stimuli beyond the linear elastic regime, is always associated with phase transformations in its host materials, mostly metals or polymers. Theoretical rationale indicates that inorganic materials with covalent/ionic bonding normally have large energy barriers for reversible structural transitions and thus host less opportunity to achieve superelasticity. Here, we demonstrate a directional tensile superelasticity in ceramic crystal GeSe through an unconventional reversible shuffle twinning mechanism instead of martensitic phase transition. We observed, with in-situ mechanical transmission electron microscopy, an evolution in stress‒strain curve from the linear elastic behavior to a nonlinear superelastic plateau, and confirmed that such superelasticity appears simultaneously together with the generation of stripy-shaped twin domains along  orientation. Theoretical calculations revealed that the shuffle twinning process from "Z-shaped" to "anti-Z-shaped" bond-configuration leads to the release of elastic potential energy, being responsible for the emergence of tensile superelasticity therein. Note that such a highly-directional superelasticity prefers to emerge at angles near the zigzag direction owing to the anisotropic Young's modulus and Poisson's ratio in GeSe, and has never been reported in superelastic materials. Our observation provides a novel strategy to exploit tensile superelasticity and nonlinear mechanics for advanced mechanical and flexible electronics.
DOI: 10.24435/materialscloud:g7-pm
title: Density of states of full and inverse Heusler magnetic alloys

description: The provided AiiDA database contains magnetic electronic structure calculations from a selection of full (L2₁) and inverse (XA) Heusler alloys. The examined crystal structures were taken from the Materials Project database. The data was obtained to examine the spin-polarization of the density of states amongst different compounds. The FLAPW code FLEUR has been combined with the GGA-based PBE functional to compute the structures. Spin-orbit interaction has been considered in second variation within the presented calculations. 
Complete list of included Heusler compounds: 
– L2₁: Co₂CrSb, Co₂GeCr, Co₂HfAl, Co₂HfGa, Co₂HfIn, Co₂ScGe, Co₂SnNb, Co₂VZn, Co₂ZnGe, Co₂ZnNb, Co₂ZnTa, Co₂ZrGa, Cu₂FeSn, Cu₂MnSb, Cu₂MnSn, Fe₂CoGa, Fe₂CoGe, Fe₂CoSi, Fe₂CrGa, Fe₂CrSi, Fe₂CrSi, Fe₂CrSn, Fe₂CrSn, Fe₂TaGe, Fe₂TiGa, Fe₂TiIn, FeMn₂P, Ir₂FeGa, Ir₂TcTl, Mn₂CrSi, Mn₂RuSi, Mn₂TaGe, Mn₂VIn, Mn₂WGa, Ni₂FeGa, Ni₂MnSb, Ni₂MnSi, Ni₂MnSn, Rh₂FeGa, Rh₂FeIn, Rh₂FeSn, Rh2MnSi, Rh2MnSn, Rh2TiMn, Rh2ZnFe, Ru2FeGe, Ru2FeSi, Ru2FeSn, Ru2ZrSb, Sc2MnSi, Sc2MnSn, ScCo2Sn, and Ti2CoIr
– XA: Fe₂CoGa, Fe₂CoSi, Fe₂NiSi, Mn₂CoGa, Mn₂CoGe, Mn₂CoIn, Mn₂CoSb, Mn₂CoSi, Mn₂CoSn, Mn₂CuGe, Mn₂CuSb, Mn₂NiSb, Mn₂NiSi, Ni₂MnSb, Sc₂MnSi, Sc₂MnSn, Ti₂CoGe, Ti₂CoIn, Ti₂CoSi,  Ti₂CuAl, Ti₂FeSi, Ti₂NiAl, Ti₂NiGa, Ti₂NiIn, Ti₂MnSi, Ti₂ZnAl, and V₂NiSb
DOI: 10.24435/materialscloud:hj-nj
title: A directional tensile superelasticity in ceramic crystal via reversible shuffle twinning

description: Superelasticity, being a reversible nonlinear strain response to stress stimuli beyond the linear elastic regime, is always associated with phase transformations in its host materials, mostly metals or polymers. Theoretical rationale indicates that inorganic materials with covalent/ionic bonding normally have large energy barriers for reversible structural transitions and thus host less opportunity to achieve superelasticity. Here, we demonstrate a directional tensile superelasticity in ceramic crystal GeSe through an unconventional reversible shuffle twinning mechanism instead of martensitic phase transition. We observed, with in-situ mechanical transmission electron microscopy, an evolution in stress‒strain curve from the linear elastic behavior to a nonlinear superelastic plateau, and confirmed that such superelasticity appears simultaneously together with the generation of stripy-shaped twin domains along  orientation. Theoretical calculations revealed that the shuffle twinning process from "Z-shaped" to "anti-Z-shaped" bond-configuration leads to the release of elastic potential energy, being responsible for the emergence of tensile superelasticity therein. Note that such a highly-directional superelasticity prefers to emerge at angles near the zigzag direction owing to the anisotropic Young's modulus and Poisson's ratio in GeSe, and has never been reported in superelastic materials. Our observation provides a novel strategy to exploit tensile superelasticity and nonlinear mechanics for advanced mechanical and flexible electronics.
DOI: 10.24435/materialscloud:s9-c5
title: Adaptive energy reference for machine-learning models of the electronic density of states

description: The electronic density of states (DOS) provides information regarding the distribution of electronic energy levels in a material, and can be used to approximate its optical and electronic properties and therefore guide computational material design. Given its usefulness and relative simplicity, it has been one of the first electronic properties used as target for machine-learning approaches going beyond interatomic potentials. A subtle but important point, well-appreciated in the condensed matter community but usually overlooked in the construction of data-driven models, is that for bulk configurations the absolute energy reference of single-particle energy levels is ill-defined. Only energy differences matter, and quantities derived from the DOS are typically independent on the absolute alignment. We introduce an adaptive scheme that optimizes the energy reference of each structure as part of the training process, and show that it consistently improves the quality of ML models compared to traditional choices of energy reference, for different classes of materials and different model architectures. On a practical level, we trace the improved performance to the ability of this self-aligning scheme to match the most prominent features in the DOS. More broadly, we believe that this work highlights the importance of incorporating insights on the nature of the physical target into the definition of the architecture and of the appropriate figures of merit for machine-learning models, that translate in better transferability and overall performance.
DOI: 10.24435/materialscloud:wh-ad
title: Adaptive energy reference for machine-learning models of the electronic density of states

description: The electronic density of states (DOS) provides information regarding the distribution of electronic energy levels in a material, and can be used to approximate its optical and electronic properties and therefore guide computational material design. Given its usefulness and relative simplicity, it has been one of the first electronic properties used as target for machine-learning approaches going beyond interatomic potentials. A subtle but important point, well-appreciated in the condensed matter community but usually overlooked in the construction of data-driven models, is that for bulk configurations the absolute energy reference of single-particle energy levels is ill-defined. Only energy differences matter, and quantities derived from the DOS are typically independent on the absolute alignment. We introduce an adaptive scheme that optimizes the energy reference of each structure as part of the training process, and show that it consistently improves the quality of ML models compared to traditional choices of energy reference, for different classes of materials and different model architectures. On a practical level, we trace the improved performance to the ability of this self-aligning scheme to match the most prominent features in the DOS. More broadly, we believe that this work highlights the importance of incorporating insights on the nature of the physical target into the definition of the architecture and of the appropriate figures of merit for machine-learning models, that translate in better transferability and overall performance.
DOI: 10.24435/materialscloud:yv-e7
title: Graph theory-based structural analysis on density anomaly of silica glass

description: Understanding the structure of glassy materials represents a tremendous challenge for both experiments and computations. Despite decades of scientific research, for instance, the structural origin of the density anomaly in silica glasses is still not well understood. Atomistic simulations based on molecular dynamics (MD) produce atomically resolved structure, but extracting insights about the role of disorder in the density anomaly is challenging. Here, we propose to quantify the topological differences between structural arrangements from MD trajectories using a graph-theoretical approach, such that structural differences in silica glasses that exhibit density anomaly can be captured. To balance the accuracy and speed of the MD simulations, we utilized force matching potentials to generate the silica glass structures. This approach involves casting all-atom glass configurations as networks, and subsequently applying a graph-similarity metric (D-measure). Calculated D-measure values are then taken as the topological distances between two configurations. By measuring the topological distances of configurations in silica glass simulated structures across a range of temperatures, distinct structural features could be observed at temperatures higher than the fictive temperature. In addition to quantifying structural changes in the full simulation box, we compared topological distances between local atomic environments in the glass and crystalline silica phases. Evidence from this approach suggests that more coesite-like local structures, which are less symmetric, emerge in silica glasses when density is at a minimum during the heating process.
DOI: 10.24435/materialscloud:5d-83
title: Graph theory-based structural analysis on density anomaly of silica glass

description: Understanding the structure of glassy materials represents a tremendous challenge for both experiments and computations. Despite decades of scientific research, for instance, the structural origin of the density anomaly in silica glasses is still not well understood. Atomistic simulations based on molecular dynamics (MD) produce atomically resolved structure, but extracting insights about the role of disorder in the density anomaly is challenging. Here, we propose to quantify the topological differences between structural arrangements from MD trajectories using a graph-theoretical approach, such that structural differences in silica glasses that exhibit density anomaly can be captured. To balance the accuracy and speed of the MD simulations, we utilized force matching potentials to generate the silica glass structures. This approach involves casting all-atom glass configurations as networks, and subsequently applying a graph-similarity metric (D-measure). Calculated D-measure values are then taken as the topological distances between two configurations. By measuring the topological distances of configurations in silica glass simulated structures across a range of temperatures, distinct structural features could be observed at temperatures higher than the fictive temperature. In addition to quantifying structural changes in the full simulation box, we compared topological distances between local atomic environments in the glass and crystalline silica phases. Evidence from this approach suggests that more coesite-like local structures, which are less symmetric, emerge in silica glasses when density is at a minimum during the heating process.
DOI: 10.24435/materialscloud:kc-0t
title: Predicting the suitability of photocatalysts for water splitting using Koopmans spectral functionals: the case of TiO₂ polymorphs

description: 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.
DOI: 10.24435/materialscloud:vk-qd
title: Finite-temperature materials modeling from the quantum nuclei to the hot electrons regime

description: Atomistic simulations provide insights into structure-property relations on an atomic size and length scale that are complementary to the macroscopic observables that can be obtained from experiments.  Quantitative predictions, however, are usually hindered by the need to strike a balance between the accuracy of the calculation of the interatomic potential and the modelling of realistic thermodynamic conditions.  Machine-learning techniques make it possible to efficiently approximate the outcome of accurate electronic-structure calculations that can, therefore, be combined with extensive thermodynamic sampling.  We take elemental nickel as a prototypical material, whose alloys have applications from cryogenic temperatures up to close to their melting point and use it to demonstrate how a combination of machine-learning models of electronic properties and statistical sampling methods makes it possible to compute accurate finite-temperature properties at an affordable cost.  We demonstrate the calculation of a broad array of bulk, interfacial, and defect properties over a temperature range from 100 to 2500 K, modelling also, when needed, the impact of nuclear quantum fluctuations and electronic entropy.  The framework we demonstrate here can be easily generalized to more complex alloys and different classes of materials.
DOI: 10.24435/materialscloud:nm-kn
title: Finite-temperature materials modeling from the quantum nuclei to the hot electrons regime

description: Atomistic simulations provide insights into structure-property relations on an atomic size and length scale that are complementary to the macroscopic observables that can be obtained from experiments.  Quantitative predictions, however, are usually hindered by the need to strike a balance between the accuracy of the calculation of the interatomic potential and the modelling of realistic thermodynamic conditions.  Machine-learning techniques make it possible to efficiently approximate the outcome of accurate electronic-structure calculations that can, therefore, be combined with extensive thermodynamic sampling.  We take elemental nickel as a prototypical material, whose alloys have applications from cryogenic temperatures up to close to their melting point and use it to demonstrate how a combination of machine-learning models of electronic properties and statistical sampling methods makes it possible to compute accurate finite-temperature properties at an affordable cost.  We demonstrate the calculation of a broad array of bulk, interfacial, and defect properties over a temperature range from 100 to 2500 K, modelling also, when needed, the impact of nuclear quantum fluctuations and electronic entropy.  The framework we demonstrate here can be easily generalized to more complex alloys and different classes of materials.
DOI: 10.24435/materialscloud:9g-k6
title: Thermodynamics and dielectric response of BaTiO₃ by data-driven modeling

description: Modeling ferroelectric materials from first principles is one of the successes of density-functional theory, and the driver of much development effort, requiring an accurate description of the electronic processes and the thermodynamic equilibrium that drive the spontaneous symmetry breaking and the emergence of macroscopic polarization. We demonstrate the development and application of an integrated machine learning (ML) model that describes on the same footing structural, energetic and functional properties of barium titanate (BaTiO₃), a prototypical ferroelectric. The model uses ab initio calculations as reference and achieves accurate yet inexpensive predictions of energy and polarization on time and length scales that are not accessible to direct ab initio modeling. The ML model allows us to thoroughly probe the static and dynamical behavior of BaTiO₃ across its phase diagram, without the need to introduce a coarse-grained description of the ferroelectric transition. Furthermore, we apply the polarization model to calculate dielectric response properties of the material in a fully ab-initio manner.
This archive provides all the relevant data and input files that were used to fit the ML interatomic potential and the polarization model used in this work, along with the relevant Density-Functional Theory calculations that were used for the training set construction and the validation of the ML model. Furthermore, it provides input files and first few snapshots of all the molecular dynamics trajectories needed to investigate the thermodynamics and dielectric properties of BaTiO₃.