cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:sg-2a
title: Symmetry-based computational search for novel binary and ternary 2D materials

description: We present a symmetry-based exhaustive approach to explore the structural and compositional richness of two-dimensional materials. We use a combinatorial engine' that constructs potential compounds by occupying all possible Wyckoff positions for a certain space group with  combinations of chemical elements. These combinations are restricted by imposing charge neutrality and the Pauling test for electronegativities. The structures are then pre-optimized with a specially crafted universal neural-network force-field, before a final step of geometry optimization using density-functional theory is performed. In this way we unveil an unprecedented variety of two-dimensional materials, covering the whole periodic table in more than 30 different stoichiometries of form AnBm or AnBmCk. Among the found structures we find examples that can be built by decorating nearly all Platonic and Archimedean tesselations as well as their dual Laves or Catalan tilings. We also obtain a rich, and unexpected, polymorphism for some specific compounds. We further accelerate the exploration of the chemical space of two-dimensional materials by employing machine-learning-accelerated prototype search, based on the structural types discovered in the exhaustive search. In total, we obtain around 6500 compounds, not present in previous available databases of 2D materials, with an energy of less than 250 meV/atom above the convex hull of thermodynamic stability.
DOI: 10.24435/materialscloud:qa-yp
title: Interplay between polarization, strain and defect-pairs in Fe-doped  SrMnO3-δ

description: Defect chemistry, strain, and structural, magnetic and electronic degrees of freedom constitute a rich space for the design of functional properties in transition metal oxides. Here, we show that it is possible to engineer polarity and ferroelectricity in non-polar perovskite oxides via polar defect pairs formed by anion vacancies coupled to substitutional cations. We use a self-consistent site-dependent DFT+U approach that accounts for local structural and chemical changes upon defect creation and which is crucial to reconcile predictions with the available experimental data. Our results for Fe-doped oxygen-deficient SrMnO3 show that substitutional Fe and oxygen vacancies can promote polarity due to an o -center displacement of the defect charge resulting in a net electric dipole moment, which polarizes the lattice in the defect neighborhood. The formation of these defects and the resulting polarization can be tuned by epitaxial strain, resulting in enhanced polarization also for strain values lower than the ones necessary to induce a polar phase transition in undoped SrMnO3. For high enough defect concentrations, these defect dipoles couple in a parallel fashion, thus enabling defect- and strain-based engineering of ferroelectricity in SrMnO3.
DOI: 10.24435/materialscloud:sq-wv
title: Reverse dark current in organic photodetectors and the major role of traps as source of noise

description: Organic photodetectors have promising applications in low-cost imaging, health monitoring and near infrared sensing. Recent research on organic photodetectors based on donor-acceptor systems has resulted in narrow-band, flexible and biocompatible devices, of which the best reach external photovoltaic quantum efficiencies approaching 100%. However, the high noise spectral density of these devices limits their specific detectivity to around 10^13 Jones in the visible and several orders of magnitude lower in the near-infrared, severely reducing performance. Here, we show that the shot noise, proportional to the dark current, dominates the noise spectral density, demanding a comprehensive understanding of the dark current. We demonstrate that, in addition to the intrinsic saturation current generated via charge-transfer states, dark current contains a major contribution from trap-assisted generated charges and decreases systematically with decreasing concentration of traps. By modeling the dark current of several donor-acceptor systems, we reveal the interplay between traps and charge-transfer states as source of dark current and show that traps dominate the generation processes, thus being the main limiting factor of organic photodetectors detectivity.
DOI: 10.24435/materialscloud:ds-4f
title: Reverse dark current in organic photodetectors and the major role of traps as source of noise

description: Organic photodetectors have promising applications in low-cost imaging, health monitoring and near infrared sensing. Recent research on organic photodetectors based on donor-acceptor systems has resulted in narrow-band, flexible and biocompatible devices, of which the best reach external photovoltaic quantum efficiencies approaching 100%. However, the high noise spectral density of these devices limits their specific detectivity to around 10^13 Jones in the visible and several orders of magnitude lower in the near-infrared, severely reducing performance. Here, we show that the shot noise, proportional to the dark current, dominates the noise spectral density, demanding a comprehensive understanding of the dark current. We demonstrate that, in addition to the intrinsic saturation current generated via charge-transfer states, dark current contains a major contribution from trap-assisted generated charges and decreases systematically with decreasing concentration of traps. By modeling the dark current of several donor-acceptor systems, we reveal the interplay between traps and charge-transfer states as source of dark current and show that traps dominate the generation processes, thus being the main limiting factor of organic photodetectors detectivity.
DOI: 10.24435/materialscloud:j9-bf
title: Crystal-graph attention networks for the prediction of stable materials

description: Graph neural networks have enjoyed great success in the prediction of material properties for both molecules and crystals. These networks typically use the atomic positions (usually expanded in a Gaussian basis) and the atomic species as input. Unfortunately, this information is in general not available when predicting new materials, for which the precise geometrical information is unknown. In this work, we circumvent this problem by predicting the thermodynamic stability of crystal structures without using the knowledge of the precise bond distances. We replace this information with embeddings of graph distances, allowing our networks to be used directly in high-throughput studies based on both composition and crystal structure prototype. Using these embeddings, we combine the newest developments in graph neural networks and apply them to the prediction of the distances to the convex hull. To train these networks, we curate a dataset of over 2 million density-functional calculations of crystals with consistent calculation parameters from various sources. The new dataset allows for the creation of a high quality convex hull and a large scale transfer learning approach. We apply the resulting model to the high-throughput search of 15 million tetragonal perovskites of composition ABCD2. As a result, we identify several thousand potentially stable compounds and demonstrate that transfer learning from the newly curated dataset reduces the required training data by 50%.
DOI: 10.24435/materialscloud:qt-cj
title: Gas adsorption and process performance data for MOFs

description: Reticular chemistry provides materials designers with a practically infinite playground on different length scales. However, the space of all plausible materials for a given application is so large that it cannot be explored using a brute-force approach. One promising approach to guide the design and discovery of materials is machine learning, which typically involves learning a mapping of structures onto properties from data. 
To advance the data-driven materials discovery of metal-organic frameworks (MOFs) for gas storage and separation applications we provide a dataset of diverse gas separation properties (CO2, CH4, H2, N2, O2 isotherms); H2S, H2O, Kr, Xe Henry coefficients (computed using grand canonical Monte-Carlo with classical force fields) as well as parasitic energy for carbon capture from natural gas and a coal-fired power plant (computed using a simple process model) for the relaxed structures in the QMOF dataset with their DDEC charges.
DOI: 10.24435/materialscloud:vr-ak
title: Crystal-graph attention networks for the prediction of stable materials

description: Graph neural networks have enjoyed great success in the prediction of material properties for both molecules and crystals. These networks typically use the atomic positions (usually expanded in a Gaussian basis) and the atomic species as input. Unfortunately, this information is in general not available when predicting new materials, for which the precise geometrical information is unknown. In this work, we circumvent this problem by predicting the thermodynamic stability of crystal structures without using the knowledge of the precise bond distances. We replace this information with embeddings of graph distances, allowing our networks to be used directly in high-throughput studies based on both composition and crystal structure prototype. Using these embeddings, we combine the newest developments in graph neural networks and apply them to the prediction of the distances to the convex hull. To train these networks, we curate a dataset of over 2 million density-functional calculations of crystals with consistent calculation parameters from various sources. The new dataset allows for the creation of a high quality convex hull and a large scale transfer learning approach. We apply the resulting model to the high-throughput search of 15 million tetragonal perovskites of composition ABCD2. As a result, we identify several thousand potentially stable compounds and demonstrate that transfer learning from the newly curated dataset reduces the required training data by 50%.
DOI: 10.24435/materialscloud:yr-mp
title: Gas adsorption and process performance data for MOFs

description: Reticular chemistry provides materials designers with a practically infinite playground on different length scales. However, the space of all plausible materials for a given application is so large that it cannot be explored using a brute-force approach. One promising approach to guide the design and discovery of materials is machine learning, which typically involves learning a mapping of structures onto properties from data. 
To advance the data-driven materials discovery of metal-organic frameworks (MOFs) for gas storage and separation applications we provide a dataset of diverse gas separation properties (CO2, CH4, H2, N2, O2 isotherms); H2S, H2O, Kr, Xe Henry coefficients (computed using grand canonical Monte-Carlo with classical force fields) as well as parasitic energy for carbon capture from natural gas and a coal-fired power plant (computed using a simple process model) for the relaxed structures in the QMOF dataset with their DDEC charges.
DOI: 10.24435/materialscloud:pr-fw
title: Controlling the TiN electrode work function at the atomistic level: a first principles investigation

description: The paper reports on a theoretical description of work function of TiN, which is one of the most used materials for the realization of electrodes and gates in CMOS devices. Indeed, although the work function is a fundamental quantity in quantum mechanics and also in device physics, as it allows the understanding of band alignment at heterostructures and gap states formation at the metal/semiconductor interface, the role of defects and contaminants is rarely taken into account. Here, by using first principles simulations, we present an extensive study of the work function dependence on nitrogen vacancies and surface oxidation for different TiN surface orientations. The results complement and explain a number of existent experimental data, and provide a useful tool to tailoring transport properties of TiN electrodes in device simulations.
DOI: 10.24435/materialscloud:gr-1f
title: Radial spin texture of the Weyl fermions in chiral tellurium

description: In the present record we provide the theoretical calculations used in the article: G. Gatti et al., Radial Spin Texture of the Weyl Fermions in Chiral Tellurium, Phys. Rev. Lett. 125, 216402. It consist of a detailed analysis of the electronic structure and spin textures at different points of the Brillouin zone. We provide the band structure and spin expectation values along different high-symmetry lines, Fermi surface contour plots in the ΓMLA plane, and spin texture in small sphere around two points of the Brillouin zone. The data is provided as the output format of the Quantum Espresso package. We also include the pseudopotential used in these calculations.
DOI: 10.24435/materialscloud:nf-sj
title: Controlling the TiN electrode work function at the atomistic level: a first principles investigation

description: The paper reports on a theoretical description of work function of TiN, which is one of the most used materials for the realization of electrodes and gates in CMOS devices. Indeed, although the work function is a fundamental quantity in quantum mechanics and also in device physics, as it allows the understanding of band alignment at heterostructures and gap states formation at the metal/semiconductor interface, the role of defects and contaminants is rarely taken into account. Here, by using first principles simulations, we present an extensive study of the work function dependence on nitrogen vacancies and surface oxidation for different TiN surface orientations. The results complement and explain a number of existent experimental data, and provide a useful tool to tailoring transport properties of TiN electrodes in device simulations.
DOI: 10.24435/materialscloud:m8-d0
title: Radial spin texture of the Weyl fermions in chiral tellurium

description: In the present record we provide the theoretical calculations used in the article: G. Gatti et al., Radial Spin Texture of the Weyl Fermions in Chiral Tellurium, Phys. Rev. Lett. 125, 216402. It consist of a detailed analysis of the electronic structure and spin textures at different points of the Brillouin zone. We provide the band structure and spin expectation values along different high-symmetry lines, Fermi surface contour plots in the ΓMLA plane, and spin texture in small sphere around two points of the Brillouin zone. The data is provided as the output format of the Quantum Espresso package. We also include the pseudopotential used in these calculations.
DOI: 10.24435/materialscloud:93-j5
title: Is there a polaron signature in angle-resolved photoemission of CsPbBr₃?

description: The formation of large polarons has been proposed as reason for the high defect tolerance, low mobility, low charge carrier trapping and low nonradiative recombination rates of lead halide perovskites. Recently, direct evidence for large-polaron formation has been reported from a 50% effective mass enhancement in angle-resolved photoemission of CsPbBr₃ over theory for the orthorhombic structure. We present in-depth band dispersion measurements of CsPbBr₃ and GW calculations which lead to almost identical effective masses at the valence band maximum of 0.203+/-0.016 m₀ in experiment and 0.217 m₀ in orthorhombic theory. We argue that the effective mass can be explained solely on the basis of electron-electron correlation and large polaron formation cannot be concluded from photoemission data.
DOI: 10.24435/materialscloud:6e-1d
title: Is there a polaron signature in angle-resolved photoemission of CsPbBr₃?

description: The formation of large polarons has been proposed as reason for the high defect tolerance, low mobility, low charge carrier trapping and low nonradiative recombination rates of lead halide perovskites. Recently, direct evidence for large-polaron formation has been reported from a 50% effective mass enhancement in angle-resolved photoemission of CsPbBr₃ over theory for the orthorhombic structure. We present in-depth band dispersion measurements of CsPbBr₃ and GW calculations which lead to almost identical effective masses at the valence band maximum of 0.203+/-0.016 m₀ in experiment and 0.217 m₀ in orthorhombic theory. We argue that the effective mass can be explained solely on the basis of electron-electron correlation and large polaron formation cannot be concluded from photoemission data.
DOI: 10.24435/materialscloud:v6-zd
title: HP - A code for the calculation of Hubbard parameters using density-functional perturbation theory

description: We introduce HP, an implementation of density-functional perturbation theory, designed to compute Hubbard parameters (on-site U and inter-site V) in the framework of DFT+U and DFT+U+V. The code does not require the use of computationally expensive supercells of the traditional linear-response approach; instead, unit cells are used with monochromatic perturbations that significantly reduce the computational cost of determining Hubbard parameters. HP is an open-source software distributed under the terms of the GPL as a component of Quantum ESPRESSO. As with other components, HP is optimized to run on a variety of different platforms, from laptops to massively parallel architectures, using native mathematical libraries (LAPACK and FFTW) and a hierarchy of custom parallelization layers built on top of MPI. The effectiveness of the code is showcased by computing Hubbard parameters self-consistently for the phospho-olivine LixMn0.5Fe0.5PO4 (x=0, 0.5, 1) and by highlighting the accuracy of predictions of the geometry and Li intercalation voltages.
DOI: 10.24435/materialscloud:b1-qn
title: HP - A code for the calculation of Hubbard parameters using density-functional perturbation theory

description: We introduce HP, an implementation of density-functional perturbation theory, designed to compute Hubbard parameters (on-site U and inter-site V) in the framework of DFT+U and DFT+U+V. The code does not require the use of computationally expensive supercells of the traditional linear-response approach; instead, unit cells are used with monochromatic perturbations that significantly reduce the computational cost of determining Hubbard parameters. HP is an open-source software distributed under the terms of the GPL as a component of Quantum ESPRESSO. As with other components, HP is optimized to run on a variety of different platforms, from laptops to massively parallel architectures, using native mathematical libraries (LAPACK and FFTW) and a hierarchy of custom parallelization layers built on top of MPI. The effectiveness of the code is showcased by computing Hubbard parameters self-consistently for the phospho-olivine LixMn0.5Fe0.5PO4 (x=0, 0.5, 1) and by highlighting the accuracy of predictions of the geometry and Li intercalation voltages.
DOI: 10.3886/e222321v2
title: ECIN Replication package for "Gender Differences in Cooperation in the U.S. Congress? Replicating Gagliarducci and Paserman (2022)""

description: Gagliarducci and Paserman (2022) study gender differences in cooperative behavioramong politicians using information from the U.S. House of Representatives between 1988 and 2010 on (i) the number of co-sponsors on bills and (ii) the share of co-sponsors from the rival party. Through different empirical strategies, they show that women-sponsored bills tend to have more co-sponsors, but the gap is only statistically significant among Republicans. Moreover, Republican women recruit a significantly larger share of co-sponsors from the rival party than Republican men, whereas the opposite is true among Democrats. GP argue that the observed pattern is consistent with a commonality of interest driving cooperation, rather than gender per se, since during this period Republican women were ideologically closer to the rival party than their male colleagues, while female Democrats were further away.We examine the robustness of these findings to (i) the correction of some errors in twocontrol variables of the dataset used by GP and (ii) clustering the standard errors at theindividual level, instead of individual-term. These changes have a relatively minor impact on results: most coefficients are still statistically significant and the main conclusions from the analysis are confirmed. Furthermore, we extend the analysis to the 2011-2020 period. The analysis of gender differences in bipartisan cooperation confirms GP’s hypothesis that ideological distance plays an important role. However, results are slightly different when we analyze overall cooperation. The gender gap in favor of women is larger in magnitude than in GP and it is statistically significant in several specifications, providing support for the hypothesis that gender also matters for cooperation.
DOI: 10.24435/materialscloud:js-fx
title: Scaling properties of RNA as a randomly branching polymer

description: Formation of base pairs between the nucleotides of a ribonucleic acid (RNA) sequence gives rise to a complex and often highly branched RNA structure. While numerous studies have demonstrated the functional importance of the high degree of RNA branching—for instance, for its spatial compactness or interaction with other biological macromolecules—RNA branching topology remains largely unexplored. Here, we use the theory of randomly branching polymers to explore the scaling properties of RNAs by mapping their secondary structures onto planar tree graphs. Focusing on random RNA sequences of varying lengths, we determine the two scaling exponents related to their topology of branching. Our results indicate that ensembles of RNA secondary structures are characterized by annealed random branching and scale similarly to self-avoiding trees in three dimensions. We further show that the obtained scaling exponents are robust upon changes in nucleotide composition, tree topology, and folding energy parameters. Finally, in order to apply the theory of branching polymers to biological RNAs, whose length cannot be arbitrarily varied, we demonstrate how both scaling exponents can be obtained from distributions of the related topological quantities of individual RNA molecules with fixed length. In this way, we establish a framework to study the branching properties of RNA and compare them to other known classes of branched polymers. By understanding the scaling properties of RNA related to its branching structure, we aim to improve our understanding of the underlying principles and open up the possibility to design RNA sequences with desired topological properties.