cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:7m-me
title: Interplay between ferroelectricity and metallicity in hexagonal YMnO₃

description: We use first-principles density functional theory to investigate how the polar distortion is affected by doping in multiferroic hexagonal yttrium manganite, h-YMnO₃. While the introduction of charge carriers tends to suppress the polar distortion in conventional ferroelectrics, the behavior in improper geometric ferroelectrics, of which h-YMnO₃ is the prototype, has not been studied to date. Using both background charge doping and atomic substitution, we find an approximately linear dependence of the polar distortion on doping concentration, with hole doping reducing and electron doping enhancing it. We show that this behavior is a direct consequence of the improper geometric nature of the ferroelectricity. In addition to its doping effect, atomic substitution can further suppress or enhance the polar distortion through changes in the local chemistry and geometry.
DOI: 10.24435/materialscloud:zh-q9
title: Surface segregation in high-entropy alloys from alchemical machine learning: dataset HEA25S

description: High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterogeneous catalysts, because of the large design space, and the synergistic effects between their components. In this work we use a machine-learning potential that can model simultaneously up to 25 transition metals (d-block transition metals, excluding Tc, Cd, Re, Os and Hg) to study the tendency of different elements to segregate at the surface of a HEA.
In this record, we provide a dataset HEA25S, containing 10000 bulk HEA structures (Dataset O), 2640 HEA surface slabs (Dataset A), together with 1000 bulk and 1000 surface slabs snapshots from the molecular dynamics (MD) runs (Datasets B and C), and 500 MD snapshots of the 25 elements Cantor-style alloy surface slabs.
We also provide the HEA25-4-NN and HEA25S-4-NN final models, which were used in the study. Full description of both the dataset and the models can be found the reference paper below.
DOI: 10.24435/materialscloud:r2-qc
title: A NN-Potential for phase transformations in Ge

description: In a recent preprint, entitled: "Development of a machine learning interatomic potential for exploring pressure-dependent kinetics of phase transitions in Germanium", we presented a novel Neural-Network (NN)  interatomic potential for Ge. We recall that Ge phases different from the cubic-diamond one are of particular interest for applications. Hexagonal Ge, for instance, displays superior optical properties. It is therefore important to investigate how, exploiting pressure, Ge can be transformed into different allotropes. In order to build a potential tackling kinetics of pressure-induced phase transformations, several kinetic paths (mainly sampled using the solid-state Nudged Elastic Band method) were added to the database, following a suitable active-learning procedure. Energies, forces, and stressed relative to the various configurations were computed ab initio using VASP with the PBE functional. The NN potential was trained using the Deep Potential Molecular Dynamic package (DeePMDkit). The potential greatly reproduces the relative stability of several Ge phases and yields at least a semi-quantitative description of the energetics along complex phase-transformation paths.  
In the present archive, we provide the full potential for use in LAMMPS and ASE, together with the full database produced using VASP.
DOI: 10.24435/materialscloud:dk-ft
title: A NN-Potential for phase transformations in Ge

description: In a recent preprint, entitled: "Development of a machine learning interatomic potential for exploring pressure-dependent kinetics of phase transitions in Germanium", we presented a novel Neural-Network (NN)  interatomic potential for Ge. We recall that Ge phases different from the cubic-diamond one are of particular interest for applications. Hexagonal Ge, for instance, displays superior optical properties. It is therefore important to investigate how, exploiting pressure, Ge can be transformed into different allotropes. In order to build a potential tackling kinetics of pressure-induced phase transformations, several kinetic paths (mainly sampled using the solid-state Nudged Elastic Band method) were added to the database, following a suitable active-learning procedure. Energies, forces, and stressed relative to the various configurations were computed ab initio using VASP with the PBE functional. The NN potential was trained using the Deep Potential Molecular Dynamic package (DeePMDkit). The potential greatly reproduces the relative stability of several Ge phases and yields at least a semi-quantitative description of the energetics along complex phase-transformation paths.  
In the present archive, we provide the full potential for use in LAMMPS and ASE, together with the full database produced using VASP.
DOI: 10.24435/materialscloud:54-2z
title: Computational optimization of a drafter for spunbonding polymeric filaments

description: This study presents a computational model to enhance the spunbonding drafter's performance. The existing design exhibits a significant risk of fiber breakage. To address this issue, an OpenFOAM computational fluid dynamics (CFD) solver is employed to simulate the airflow over the base geometry and its modified configurations following various design alterations. The collected data are analyzed for predefined optimization objectives: (a) maximize shear drag and thus draw on the filaments, (b) achieve maximum drawing uniformity, and (c) minimize the pressurized air consumption rate. These goals are set to produce more uniform filaments, reduce the breakage risk, and improve energy efficiency. We vary seven design parameters, ran many CFD simulations, and recommend a few enhancements for a drafter based on those. We identify a "braking effect" on the filaments and find that geometry significantly affects the internal airflow and, thus, the drawing process. Based on our findings, we propose widening the drafter, linearly diverging the walls at the lower section, linearly converging the walls at half of the upper section, and introducing an extensible length for drawing precision control.
DOI: 10.24435/materialscloud:kz-b7
title: Atomistic simulations reveal strength reductions due to short-range order in alloys

description: A theory for strengthening for multicomponent non-dilute alloys possessing short-range order (SRO) has recently been developed. The theory predicts that, in addition to a well-known athermal strengthening, there is a notable effect of SRO on the solute–dislocation interactions that can decrease or increase the strength relative to a random alloy. Here, carefully designed atomistic simulations in a model binary NbW alloy are used to demonstrate that alloy strength due to solute–dislocation interactions can be increased or decreased, depending on the SRO and consistent with theoretical predictions. Specifically, SRO is introduced using fictitious solute– solute interactions in an alloy system with very small true solute–solute interactions, and the Nudged Elastic Band (NEB) method is then used to compute the energy barriers for edge dislocation motion for various levels of SRO. Energy barriers, and hence alloy strengths, can be decreased when the Warren-Cowley SRO parameters are negative (attraction of unlike solutes). The theoretical predictions for the same system are in reasonable quantitative agreement with the simulation results. These findings demonstrate the unexpected possibility of reduced strength due to SRO and also further validate the analytical theory as a tool to guide alloy design.
DOI: 10.24435/materialscloud:x1-d9
title: Computational optimization of a drafter for spunbonding polymeric filaments

description: This study presents a computational model to enhance the spunbonding drafter's performance. The existing design exhibits a significant risk of fiber breakage. To address this issue, an OpenFOAM computational fluid dynamics (CFD) solver is employed to simulate the airflow over the base geometry and its modified configurations following various design alterations. The collected data are analyzed for predefined optimization objectives: (a) maximize shear drag and thus draw on the filaments, (b) achieve maximum drawing uniformity, and (c) minimize the pressurized air consumption rate. These goals are set to produce more uniform filaments, reduce the breakage risk, and improve energy efficiency. We vary seven design parameters, ran many CFD simulations, and recommend a few enhancements for a drafter based on those. We identify a "braking effect" on the filaments and find that geometry significantly affects the internal airflow and, thus, the drawing process. Based on our findings, we propose widening the drafter, linearly diverging the walls at the lower section, linearly converging the walls at half of the upper section, and introducing an extensible length for drawing precision control.
DOI: 10.24435/materialscloud:g9-y6
title: Atomistic simulations reveal strength reductions due to short-range order in alloys

description: A theory for strengthening for multicomponent non-dilute alloys possessing short-range order (SRO) has recently been developed. The theory predicts that, in addition to a well-known athermal strengthening, there is a notable effect of SRO on the solute–dislocation interactions that can decrease or increase the strength relative to a random alloy. Here, carefully designed atomistic simulations in a model binary NbW alloy are used to demonstrate that alloy strength due to solute–dislocation interactions can be increased or decreased, depending on the SRO and consistent with theoretical predictions. Specifically, SRO is introduced using fictitious solute– solute interactions in an alloy system with very small true solute–solute interactions, and the Nudged Elastic Band (NEB) method is then used to compute the energy barriers for edge dislocation motion for various levels of SRO. Energy barriers, and hence alloy strengths, can be decreased when the Warren-Cowley SRO parameters are negative (attraction of unlike solutes). The theoretical predictions for the same system are in reasonable quantitative agreement with the simulation results. These findings demonstrate the unexpected possibility of reduced strength due to SRO and also further validate the analytical theory as a tool to guide alloy design.
DOI: 10.24435/materialscloud:24-a9
title: Probing the Mott-insulating behavior of Ba₂MgReO₆ with DFT+DMFT

description: We investigate the interplay of spin-orbit coupling, electronic correlations, and lattice distortions in the 5d¹ double perovskite Ba₂MgReO₆. Combining density-functional theory (DFT) and dynamical mean-field theory (DMFT), we establish the Mott-insulating character of Ba₂MgReO₆ in both its cubic and tetragonal paramagnetic phases. Despite substantial spin-orbit coupling, its impact on the formation of the insulating state is minimal, consistent with theoretical expectations for d¹ systems. We further characterize the electronic properties of the cubic and tetragonal phases by analyzing spectral functions and local occupations in terms of multipole moments centered on the Re sites. Our results confirm the presence of ferroically ordered z² quadrupoles in addition to the antiferroic x²-y²-type order. We compare two equivalent but complementary descriptions in terms of either effective Re-t2g frontier orbitals or more localized atomic-like Re-d and O-p orbitals. The former maps directly on a physically intuitive picture in terms of nominal d¹ Re cations, while the latter explicitly demonstrates the role of hybridization with the ligands in the spin-orbit splitting and the formation of the charge quadrupoles around the Re sites. Finally, we compare our DFT+DMFT results with a previous DFT+U study of the tetragonal paramagnetic state. We find good qualitative agreement for the dominant charge quadrupoles, but also notable differences in the corresponding spectral functions, underscoring the need for more comparative studies between these two methods.
DOI: 10.24435/materialscloud:ps-20
title: Surface segregation in high-entropy alloys from alchemical machine learning: dataset HEA25S

description: High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterogeneous catalysts, because of the large design space, and the synergistic effects between their components. In this work we use a machine-learning potential that can model simultaneously up to 25 transition metals (d-block transition metals, excluding Tc, Cd, Re, Os and Hg) to study the tendency of different elements to segregate at the surface of a HEA.
In this record, we provide a dataset HEA25S, containing 10000 bulk HEA structures (Dataset O), 2640 HEA surface slabs (Dataset A), together with 1000 bulk and 1000 surface slabs snapshots from the molecular dynamics (MD) runs (Datasets B and C), and 500 MD snapshots of the 25 elements Cantor-style alloy surface slabs.
We also provide the HEA25-4-NN and HEA25S-4-NN final models, which were used in the study. Full description of both the dataset and the models can be found the reference paper below.
DOI: 10.24435/materialscloud:pw-az
title: Probing the Mott-insulating behavior of Ba₂MgReO₆ with DFT+DMFT

description: We investigate the interplay of spin-orbit coupling, electronic correlations, and lattice distortions in the 5d¹ double perovskite Ba₂MgReO₆. Combining density-functional theory (DFT) and dynamical mean-field theory (DMFT), we establish the Mott-insulating character of Ba₂MgReO₆ in both its cubic and tetragonal paramagnetic phases. Despite substantial spin-orbit coupling, its impact on the formation of the insulating state is minimal, consistent with theoretical expectations for d¹ systems. We further characterize the electronic properties of the cubic and tetragonal phases by analyzing spectral functions and local occupations in terms of multipole moments centered on the Re sites. Our results confirm the presence of ferroically ordered z² quadrupoles in addition to the antiferroic x²-y²-type order. We compare two equivalent but complementary descriptions in terms of either effective Re-t2g frontier orbitals or more localized atomic-like Re-d and O-p orbitals. The former maps directly on a physically intuitive picture in terms of nominal d¹ Re cations, while the latter explicitly demonstrates the role of hybridization with the ligands in the spin-orbit splitting and the formation of the charge quadrupoles around the Re sites. Finally, we compare our DFT+DMFT results with a previous DFT+U study of the tetragonal paramagnetic state. We find good qualitative agreement for the dominant charge quadrupoles, but also notable differences in the corresponding spectral functions, underscoring the need for more comparative studies between these two methods.
DOI: 10.24435/materialscloud:gf-jd
title: Oxygen vacancy induced defect dipoles in BiVO4 for photoelectrocatalytic partial oxidation of methane

description: A strong driving force for charge separation and transfer in semiconductors is essential for designing effective photoelectrodes for solar energy conversion. While defect engineering and polarization alignment can enhance this process, their potential interference within a photoelectrode remains unclear. Here we show that oxygen vacancies in bismuth vanadate (BiVO4) can create defect dipoles due to a disruption of symmetry. The modified photoelectrodes exhibit a strong correlation between charge separation and transfer capability and external electrical poling, which is not seen in unmodified samples. Applying poling at -150 Volt boosts charge separation and transfer efficiency to over 90 %. A photocurrent density of 6.3 mA cm-2 is achieved on the photoelectrode after loading with a nickel-iron oxide-based cocatalyst. Furthermore, using generated holes for methane partial oxidation can produce methanol with a Faradaic efficiency of approximately 6 %. These findings provide valuable insights into the photoelectrocatalytic conversion of greenhouse gases into valuable chemical products.
DOI: 10.24435/materialscloud:ve-r0
title: Surface segregation in high-entropy alloys from alchemical machine learning: dataset HEA25S

description: High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterogeneous catalysts, because of the large design space, and the synergistic effects between their components. In this work we use a machine-learning potential that can model simultaneously up to 25 transition metals (d-block transition metals, excluding Tc, Cd, Re, Os and Hg) to study the tendency of different elements to segregate at the surface of a HEA.
In this record, we provide a dataset HEA25S, containing 10000 bulk HEA structures (Dataset O), 2640 HEA surface slabs (Dataset A), together with 1000 bulk and 1000 surface slabs snapshots from the molecular dynamics (MD) runs (Datasets B and C), and 500 MD snapshots of the 25 elements Cantor-style alloy surface slabs.
We also provide the HEA25-4-NN and HEA25S-4-NN final models, which were used in the study. Full description of both the dataset and the models can be found the reference paper below.
DOI: 10.24435/materialscloud:1g-4c
title: Oxygen vacancy induced defect dipoles in BiVO4 for photoelectrocatalytic partial oxidation of methane

description: A strong driving force for charge separation and transfer in semiconductors is essential for designing effective photoelectrodes for solar energy conversion. While defect engineering and polarization alignment can enhance this process, their potential interference within a photoelectrode remains unclear. Here we show that oxygen vacancies in bismuth vanadate (BiVO4) can create defect dipoles due to a disruption of symmetry. The modified photoelectrodes exhibit a strong correlation between charge separation and transfer capability and external electrical poling, which is not seen in unmodified samples. Applying poling at -150 Volt boosts charge separation and transfer efficiency to over 90 %. A photocurrent density of 6.3 mA cm-2 is achieved on the photoelectrode after loading with a nickel-iron oxide-based cocatalyst. Furthermore, using generated holes for methane partial oxidation can produce methanol with a Faradaic efficiency of approximately 6 %. These findings provide valuable insights into the photoelectrocatalytic conversion of greenhouse gases into valuable chemical products.
DOI: 10.24435/materialscloud:q7-74
title: Designing bifunctional perovskite catalysts for the oxygen reduction and evolution reactions

description: The development of unified regenerative fuel cells (URFC) necessitates an active and stable bifunctional oxygen electrocatalyst. The unique challenge of possessing high activity for both the oxygen reduction (ORR) and oxygen evolution (OER) reactions, while maintaining stability over a wide potential window impedes the design of bifunctional oxygen electrocatalysts. Herein, two design strategies are explored to optimize their performance. The first incorporates active sites for ORR and OER, Mn and Co, into a single perovskite structure, which is achieved with the perovskites Ba0.5Sr0.5Co0.8Mn0.2O3-δ (BSCM) and La0.5Ba0.25Sr0.25Co0.5Mn0.5O3-δ (LBSCM). The second combines an active ORR perovskite catalyst (La0.4Sr0.6MnO3-δ (LSM)) with an OER active perovskite catalyst (Ba0.5Sr0.5Co0.8Fe0.2O3-δ (BSCF)) in a physical mixed composite (BSCF/LSM). The success of the two strategies is investigated by measuring the catalysts' catalytic performance and response to alternating reducing and oxidizing potentials to mimic the dynamic conditions experienced during operation of URFCs. Additionally, the continuous, potentiodynamic change in Mn, Co, and Fe oxidation state during ORR and OER is elucidated with operando X-ray absorption spectroscopy (XAS) measurements, revealing key insights into the nature of the active sites. The results reveal important catalyst physiochemical properties and provide a guide for future research and design principles for bifunctional oxygen electrocatalysts.
DOI: 10.24435/materialscloud:1c-m7
title: Calculation of screened Coulomb interaction parameters for the charge-disproportionated insulator CaFeO₃

description: We calculate the screened electron-electron interaction for the charge-disproportionated insulator CaFeO₃ using the constrained random-phase approximation (cRPA). While in many correlated materials, the formation of a Mott-insulating state is driven by a large local Coulomb repulsion, represented by the Hubbard U, several cases have been identified more recently where U is strongly screened and instead the Hund's interaction J dominates the physics. Our results confirm a strong screening of the local Coulomb repulsion U in CaFeO₃ whereas J is much less screened and can thus stabilize a charge-disproportionated insulating state. This is consistent with the case of the rare-earth nickelates where similar behavior has been demonstrated. In addition, we validate some common assumptions used for parametrizing the local electron-electron interaction in first-principles calculations based on density-functional theory (DFT), assess the dependence of the interaction on the choice of correlated orbitals, and discuss the use of the calculated interaction parameters in DFT+U calculations of CaFeO₃. Our work also highlights certain limitations for the direct use of cRPA results in DFT-based first-principles calculations, in particular for systems with strong entanglement between the correlated and uncorrelated bands.
DOI: 10.24435/materialscloud:mb-cz
title: Designing bifunctional perovskite catalysts for the oxygen reduction and evolution reactions

description: The development of unified regenerative fuel cells (URFC) necessitates an active and stable bifunctional oxygen electrocatalyst. The unique challenge of possessing high activity for both the oxygen reduction (ORR) and oxygen evolution (OER) reactions, while maintaining stability over a wide potential window impedes the design of bifunctional oxygen electrocatalysts. Herein, two design strategies are explored to optimize their performance. The first incorporates active sites for ORR and OER, Mn and Co, into a single perovskite structure, which is achieved with the perovskites Ba0.5Sr0.5Co0.8Mn0.2O3-δ (BSCM) and La0.5Ba0.25Sr0.25Co0.5Mn0.5O3-δ (LBSCM). The second combines an active ORR perovskite catalyst (La0.4Sr0.6MnO3-δ (LSM)) with an OER active perovskite catalyst (Ba0.5Sr0.5Co0.8Fe0.2O3-δ (BSCF)) in a physical mixed composite (BSCF/LSM). The success of the two strategies is investigated by measuring the catalysts' catalytic performance and response to alternating reducing and oxidizing potentials to mimic the dynamic conditions experienced during operation of URFCs. Additionally, the continuous, potentiodynamic change in Mn, Co, and Fe oxidation state during ORR and OER is elucidated with operando X-ray absorption spectroscopy (XAS) measurements, revealing key insights into the nature of the active sites. The results reveal important catalyst physiochemical properties and provide a guide for future research and design principles for bifunctional oxygen electrocatalysts.