cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:r5-42
title: Dataset of self-consistent Hubbard parameters for Ni, Mn and Fe from linear-response

description: Density-functional theory with extended Hubbard functionals (DFT+U+V) provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements. It does so by mitigating self-interaction errors inherent to semi-local functionals which are particularly pronounced in systems with partially-filled d and f electronic states. However, achieving accuracy in this approach hinges upon the accurate determination of the on-site U and inter-site V Hubbard parameters. In practice, these are obtained either by semi-empirical tuning, requiring prior knowledge, or, more correctly, by using predictive but expensive first-principles calculations.  This archive entry contains Hubbard parameters, occupation matrices and other data calculated for 28 materials and covers all steps in a self-consistent procedure where, at each step new Hubbard parameters are obtained via linear-response, a process that is repeated until the parameters no longer change. The primary purpose of this dataset is to support the development and validation of machine learning models that can be used to predict Hubbard parameters, sidestepping the need for expensive ab-initio density functional perturbation theory calculations.
DOI: 10.24435/materialscloud:y6-p8
title: Inter-layer hydrogen recombination from hydrogen boride nanosheets elucidated by isotope labeling

description: Deuterium boride (DB) nanosheets were synthesized through ion exchange, and their Fourier-transform infrared absorption spectra showed isotope effects with a shift in the B-H stretching mode. Temperature-programmed desorption (TPD) experiments indicated that hydrogen release from DB and HB nanosheets primarily results from inter-layer hydrogen recombination at lower temperatures, with intra-layer recombination occurring at higher temperatures. This repository contains supplementary data related to the computational findings presented in the accompanying publication. The goal of sharing these data files is to enhance result transparency and support better reproducibility.
DOI: 10.24435/materialscloud:34-1s
title: Inter-layer hydrogen recombination from hydrogen boride nanosheets elucidated by isotope labeling

description: Deuterium boride (DB) nanosheets were synthesized through ion exchange, and their Fourier-transform infrared absorption spectra showed isotope effects with a shift in the B-H stretching mode. Temperature-programmed desorption (TPD) experiments indicated that hydrogen release from DB and HB nanosheets primarily results from inter-layer hydrogen recombination at lower temperatures, with intra-layer recombination occurring at higher temperatures. This repository contains supplementary data related to the computational findings presented in the accompanying publication. The goal of sharing these data files is to enhance result transparency and support better reproducibility.
DOI: 10.24435/materialscloud:xd-ef
title: 3DReact: geometric deep learning for chemical reactions

description: Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficiency of predictions of molecular properties. Building on this success, we introduce 3DREACT, a geometric deep learning model to predict reaction properties from three-dimensional structures of reactants and products. We demonstrate that the invariant version of the model is sufficient for existing reaction data sets. We illustrate its competitive performance on the prediction of activation barriers on the GDB7-22-TS, Cyclo-23-TS, and Proparg-21-TS data sets in different atom-mapping regimes. We show that, compared to existing models for reaction property prediction, 3DREACT offers a flexible framework that exploits atom- mapping information, if available, as well as geometries of reactants and products (in an invariant or equivariant fashion).  Accordingly, it performs systematically well across different data sets, atom-mapping regimes, as well as both interpolation and extrapolation tasks.
DOI: 10.24435/materialscloud:hs-73
title: 3DReact: geometric deep learning for chemical reactions

description: Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficiency of predictions of molecular properties. Building on this success, we introduce 3DREACT, a geometric deep learning model to predict reaction properties from three-dimensional structures of reactants and products. We demonstrate that the invariant version of the model is sufficient for existing reaction data sets. We illustrate its competitive performance on the prediction of activation barriers on the GDB7-22-TS, Cyclo-23-TS, and Proparg-21-TS data sets in different atom-mapping regimes. We show that, compared to existing models for reaction property prediction, 3DREACT offers a flexible framework that exploits atom- mapping information, if available, as well as geometries of reactants and products (in an invariant or equivariant fashion).  Accordingly, it performs systematically well across different data sets, atom-mapping regimes, as well as both interpolation and extrapolation tasks.
DOI: 10.24435/materialscloud:ph-3c
title: Prediction of a novel type-I antiferromagnetic Weyl semimetal

description: Topological materials have been a main focus of studies in the past decade due to their protected properties that can be exploited for the fabrication of new devices. Among them, Weyl semimetals are a class of topological semimetals with non-trivial linear band crossing close to the Fermi level. The existence of such crossings requires the breaking of either time-reversal or inversion symmetry and is responsible for the exotic physical properties. In this work we identify the full-Heusler compound InMnTi₂, as a promising, easy to synthesize, T- and I-breaking Weyl semimetal. This material exhibits several features that are comparatively more intriguing with respect to other known Weyl semimetals: the distance between two neighboring nodes is large enough to observe a wide range of linear dispersions in the bands, and only one kind of such node's pairs is present in the Brillouin zone. We also show the presence of Fermi arcs stable across a wide range of chemical potentials. Finally, the lack of contributions from trivial points to the low-energy properties makes the materials a promising candidate for practical devices.
DOI: 10.24435/materialscloud:qb-sb
title: Prediction of a novel type-I antiferromagnetic Weyl semimetal

description: Topological materials have been a main focus of studies in the past decade due to their protected properties that can be exploited for the fabrication of new devices. Among them, Weyl semimetals are a class of topological semimetals with non-trivial linear band crossing close to the Fermi level. The existence of such crossings requires the breaking of either time-reversal or inversion symmetry and is responsible for the exotic physical properties. In this work we identify the full-Heusler compound InMnTi₂, as a promising, easy to synthesize, T- and I-breaking Weyl semimetal. This material exhibits several features that are comparatively more intriguing with respect to other known Weyl semimetals: the distance between two neighboring nodes is large enough to observe a wide range of linear dispersions in the bands, and only one kind of such node's pairs is present in the Brillouin zone. We also show the presence of Fermi arcs stable across a wide range of chemical potentials. Finally, the lack of contributions from trivial points to the low-energy properties makes the materials a promising candidate for practical devices.
DOI: 10.24435/materialscloud:gq-6x
title: Electric field tunable bandgap in twisted double trilayer graphene

description: Twisted van der Waals heterostructures have recently emerged as a versatile platform for engineering interaction-driven, topological phenomena with a high degree of control and tunability. Since the initial discovery of correlated phases in twisted bilayer graphene, a wide range of moiré materials have emerged with fascinating electronic properties. While the field of twistronics has rapidly evolved and now includes a range of multi-layered systems, moiré systems comprised of double trilayer graphene remain elusive. Here, we report electrical transport measurements combined with tight-binding calculations in twisted double trilayer graphene (TDTLG). We demonstrate that small-angle TDTLG (~1.7−2.0ᵒ) exhibits an intrinsic bandgap at the charge neutrality point. Moreover, by tuning the displacement field, we observe a continuous insulator-semimetal-insulator transition at the CNP, which is also captured by tight-binding calculations. These results establish TDTLG systems as a highly tunable platform for further exploration of magneto-transport and optoelectronic properties.
DOI: 10.24435/materialscloud:qq-aq
title: Electric field tunable bandgap in twisted double trilayer graphene

description: Twisted van der Waals heterostructures have recently emerged as a versatile platform for engineering interaction-driven, topological phenomena with a high degree of control and tunability. Since the initial discovery of correlated phases in twisted bilayer graphene, a wide range of moiré materials have emerged with fascinating electronic properties. While the field of twistronics has rapidly evolved and now includes a range of multi-layered systems, moiré systems comprised of double trilayer graphene remain elusive. Here, we report electrical transport measurements combined with tight-binding calculations in twisted double trilayer graphene (TDTLG). We demonstrate that small-angle TDTLG (~1.7−2.0ᵒ) exhibits an intrinsic bandgap at the charge neutrality point. Moreover, by tuning the displacement field, we observe a continuous insulator-semimetal-insulator transition at the CNP, which is also captured by tight-binding calculations. These results establish TDTLG systems as a highly tunable platform for further exploration of magneto-transport and optoelectronic properties.
DOI: 10.24435/materialscloud:xd-10
title: Benchmarking machine-readable vectors of chemical reactions on computed activation barriers

description: In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks. Consequently, various predictive approaches have emerged, ranging from graph-based models to methods based on the three-dimensional structure of reactants and products. In tandem, many representations have been developed to predict experimental targets, which may hold promise for barrier prediction as well. Here, we bring together all of these efforts and benchmark various methods (Morgan fingerprints, the DRFP, the CGR representation-based Chemprop, SLATMd, B²Rl², EquiReact and language model BERT + RXNFP) for the prediction of computed activation barriers on three diverse datasets.
This record includes data to support the article "Benchmarking machine-readable vectors of chemical reactions on computed activation barriers". This supports the github repository https://github.com/lcmd-epfl/benchmark-barrier-learning which contains the codes and duplicates the data.
DOI: 10.24435/materialscloud:49-vz
title: Benchmarking machine-readable vectors of chemical reactions on computed activation barriers

description: In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks. Consequently, various predictive approaches have emerged, ranging from graph-based models to methods based on the three-dimensional structure of reactants and products. In tandem, many representations have been developed to predict experimental targets, which may hold promise for barrier prediction as well. Here, we bring together all of these efforts and benchmark various methods (Morgan fingerprints, the DRFP, the CGR representation-based Chemprop, SLATMd, B²Rl², EquiReact and language model BERT + RXNFP) for the prediction of computed activation barriers on three diverse datasets.
This record includes data to support the article "Benchmarking machine-readable vectors of chemical reactions on computed activation barriers". This supports the github repository https://github.com/lcmd-epfl/benchmark-barrier-learning which contains the codes and duplicates the data.
DOI: 10.24435/materialscloud:t7-7p
title: Exploring strong electronic correlations in the breathing kagome metal Fe₃Sn

description: Kagome metals have emerged as pivotal materials in condensed matter physics due to their unique geometric arrangement and intriguing electronic properties. Understanding the origin of magnetism in these materials, particularly in iron rich Fe-Sn binary compounds like Fe₃Sn, holds a significant importance, as they represent potential candidates for permanent magnets with a high Curie temperature and a strong magnetic anisotropy. In the present study, we employ density-functional theory and dynamical mean-field theory to analyze the electronic structure and magnetic properties of Fe₃Sn. Our investigation reveals the presence of several nearly-flat bands and Weyl nodes at low excitation energies. The inclusion of local correlation effects is shown to push these features even closer to the Fermi energy, which may be important for their manipulation via external stimuli. Regarding magnetism, the Hubbard-like interaction leads to an increase of orbital polarization at the expenses of a minor reduction of the spin moment. The magnetic anisotropy energy exhibits a strong dependence on the particular choice of the Coulomb interaction parameters. Additionally, our detailed analysis of the interatomic exchange interactions indicates a significant contribution from the antisymmetric exchange, i.e. the Dzyaloshinskii-Moriya interaction, which showcases the existence of magnetic chirality in the system. Overall, our investigation highlights a strong interplay between the flat bands near the Fermi level, the local Coulomb interaction and the triangular geometry of the lattice, which plays a crucial role in driving the magnetic properties of this material.
DOI: 10.24435/materialscloud:vb-g4
title: Dataset of self-consistent Hubbard parameters for Ni, Mn and Fe from linear-response

description: Density-functional theory with extended Hubbard functionals (DFT+U+V) provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements. It does so by mitigating self-interaction errors inherent to semi-local functionals which are particularly pronounced in systems with partially-filled d and f electronic states. However, achieving accuracy in this approach hinges upon the accurate determination of the on-site U and inter-site V Hubbard parameters. In practice, these are obtained either by semi-empirical tuning, requiring prior knowledge, or, more correctly, by using predictive but expensive first-principles calculations.  This archive entry contains Hubbard parameters, occupation matrices and other data calculated for 28 materials and covers all steps in a self-consistent procedure where, at each step new Hubbard parameters are obtained via linear-response, a process that is repeated until the parameters no longer change. The primary purpose of this dataset is to support the development and validation of machine learning models that can be used to predict Hubbard parameters, sidestepping the need for expensive ab-initio density functional perturbation theory calculations.
DOI: 10.24435/materialscloud:xe-qk
title: Exploring strong electronic correlations in the breathing kagome metal Fe₃Sn

description: Kagome metals have emerged as pivotal materials in condensed matter physics due to their unique geometric arrangement and intriguing electronic properties. Understanding the origin of magnetism in these materials, particularly in iron rich Fe-Sn binary compounds like Fe₃Sn, holds a significant importance, as they represent potential candidates for permanent magnets with a high Curie temperature and a strong magnetic anisotropy. In the present study, we employ density-functional theory and dynamical mean-field theory to analyze the electronic structure and magnetic properties of Fe₃Sn. Our investigation reveals the presence of several nearly-flat bands and Weyl nodes at low excitation energies. The inclusion of local correlation effects is shown to push these features even closer to the Fermi energy, which may be important for their manipulation via external stimuli. Regarding magnetism, the Hubbard-like interaction leads to an increase of orbital polarization at the expenses of a minor reduction of the spin moment. The magnetic anisotropy energy exhibits a strong dependence on the particular choice of the Coulomb interaction parameters. Additionally, our detailed analysis of the interatomic exchange interactions indicates a significant contribution from the antisymmetric exchange, i.e. the Dzyaloshinskii-Moriya interaction, which showcases the existence of magnetic chirality in the system. Overall, our investigation highlights a strong interplay between the flat bands near the Fermi level, the local Coulomb interaction and the triangular geometry of the lattice, which plays a crucial role in driving the magnetic properties of this material.
DOI: 10.24435/materialscloud:zg-7j
title: Dataset of self-consistent Hubbard parameters for Ni, Mn and Fe from linear-response

description: Density-functional theory with extended Hubbard functionals (DFT+U+V) provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements. It does so by mitigating self-interaction errors inherent to semi-local functionals which are particularly pronounced in systems with partially-filled d and f electronic states. However, achieving accuracy in this approach hinges upon the accurate determination of the on-site U and inter-site V Hubbard parameters. In practice, these are obtained either by semi-empirical tuning, requiring prior knowledge, or, more correctly, by using predictive but expensive first-principles calculations.  This archive entry contains Hubbard parameters, occupation matrices and other data calculated for 28 materials and covers all steps in a self-consistent procedure where, at each step new Hubbard parameters are obtained via linear-response, a process that is repeated until the parameters no longer change. The primary purpose of this dataset is to support the development and validation of machine learning models that can be used to predict Hubbard parameters, sidestepping the need for expensive ab-initio density functional perturbation theory calculations.
DOI: 10.24435/materialscloud:w7-k1
title: Teaching oxidation states to neural networks

description: The accurate description of redox reactions remains a challenge for first-principles calculations, but it has been shown that extended Hubbard functionals (DFT+U+V) can provide a reliable approach, mitigating self-interaction errors, in materials with strongly localized d or f electrons. Here, we first show that DFT+U+V molecular dynamics is capable to follow the adiabatic evolution of oxidation states over time, using representative Li-ion cathode materials.  In turn, this allows to develop redox-aware machine-learned potentials. We show that considering atoms with different oxidation states (as accurately predicted by DFT+U+V) as distinct species in the training leads to potentials that are able to identify the correct ground state and pattern of oxidation states for redox elements present. This is achieved, e.g., trough a combinatorial search for the lowest energy configuration. This brings the advantages of machine-learned potential to key technological applications (e.g., rechargeable batteries), which require an accurate description of the evolution of redox states.
DOI: 10.24435/materialscloud:c6-c2
title: Teaching oxidation states to neural networks

description: The accurate description of redox reactions remains a challenge for first-principles calculations, but it has been shown that extended Hubbard functionals (DFT+U+V) can provide a reliable approach, mitigating self-interaction errors, in materials with strongly localized d or f electrons. Here, we first show that DFT+U+V molecular dynamics is capable to follow the adiabatic evolution of oxidation states over time, using representative Li-ion cathode materials.  In turn, this allows to develop redox-aware machine-learned potentials. We show that considering atoms with different oxidation states (as accurately predicted by DFT+U+V) as distinct species in the training leads to potentials that are able to identify the correct ground state and pattern of oxidation states for redox elements present. This is achieved, e.g., trough a combinatorial search for the lowest energy configuration. This brings the advantages of machine-learned potential to key technological applications (e.g., rechargeable batteries), which require an accurate description of the evolution of redox states.
DOI: 10.24435/materialscloud:sm-90
title: Influence of carrier-carrier interactions on the sub-threshold swing of band-to-band tunnelling transistors

description: Band-to-band tunnelling field-effect transistors (TFETs) have long been considered as promising candidates for future low-power logic applications. However, fabricated TFETs rarely reach sub-60 mV/dec sub-threshold swings (SS) at room temperature. Previous theoretical studies identified Auger processes as possible mechanisms for the observed degradation of SS. Through first-principles quantum transport simulations incorporating carrier-carrier interactions within the Non-equilibrium Green's Function formalism through self-consistent GW approximation, we confirm here that Auger processes are indeed at least partly responsible for the poor performance of TFETs. Using a carbon nanotube TFET as testbed, we show that carrier-carrier scattering alone significantly increases the OFF-state current of these devices, thus worsening their sub-threshold behavior. The results are in the folder uploaded.
DOI: 10.24435/materialscloud:8b-0s
title: Influence of carrier-carrier interactions on the sub-threshold swing of band-to-band tunnelling transistors

description: Band-to-band tunnelling field-effect transistors (TFETs) have long been considered as promising candidates for future low-power logic applications. However, fabricated TFETs rarely reach sub-60 mV/dec sub-threshold swings (SS) at room temperature. Previous theoretical studies identified Auger processes as possible mechanisms for the observed degradation of SS. Through first-principles quantum transport simulations incorporating carrier-carrier interactions within the Non-equilibrium Green's Function formalism through self-consistent GW approximation, we confirm here that Auger processes are indeed at least partly responsible for the poor performance of TFETs. Using a carbon nanotube TFET as testbed, we show that carrier-carrier scattering alone significantly increases the OFF-state current of these devices, thus worsening their sub-threshold behavior. The results are in the folder uploaded.