cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:j6-rw
title: Dislocation-grain boundary interaction dataset for FCC Cu

description: Dislocation-grain boundary play a major role in the strength and ductility of structural materials. An understanding of governing parameters such as grain boundary local atomic structures on the outcome of this interaction can accelerate new alloy design strategies that tailors materials' microstructures. Previous studies have focused only on the subset of minimum-energy grain boundary structures. We present a comprehensive database of dislocation-grain boundary interaction for edge, screw, and 60o mixed dislocation with 587 < 110 > and < 112 > symmetric tilt grain boundary in FCC Cu consisting of 73 minimum-energy grain boundary structures and 514 metastable ones. The dataset contains the outcomes for 5234 unique interactions with a particular dislocation type, grain boundary structure, and applied shear stress.
DOI: 10.24435/materialscloud:qt-1s
title: FINALES - Electrolyte optimization for maximum conductivity and for maximum cycle life

description: This study investigates an electrolyte system composed of lithium hexafluorophosphate (LiPF6), ethylene carbonate (EC) and ethyl methyl carbonate (EMC). For the assembly of full cells, electrodes based on graphite and lithium nickel dioxide (LNO) are used. This work provides insight into the similarity of formulations of an electrolyte optimized for maximum conductivity and another one optimized for maximum cycle life are expected to be in this chemical system. The goal is to assess whether it is promising to target research efforts on finding an electrolyte formulation within this chemical space which can fulfill both requirements.
A campaign utilizing the latest version of FINALES is designed to determine conductivity values and predict end of life for various electrolyte formulations containing the aforementioned chemicals. The campaigns were able to reproducibly identify regions of high ionic conductivity of the aforementioned chemical composition. The ML methodology applied for the EOL optimisation allowed accelerated early life-time predictions.
DOI: 10.24435/materialscloud:nn-03
title: Effect of residual stress and microstructure on mechanical properties of sputter-grown Cu/W nanomultilayers

description: The combination of the high wear resistance and mechanical strength of W with the high thermal conductivity of Cu makes the Cu/W system an attractive candidate material for heat sink plasma and radiation tolerance applications. However, the resulting mechanical properties of multilayers and coatings strongly depend on the microstructure of the layers. 
In this work, the mechanical properties of Cu/W nanomultilayers with different densities of internal interfaces are systematically investigated for two opposite in-plane stress states and critically discussed in comparison with literature. Atomistic simulations with the state-of-the-art neural network potential are used to explain the experimental findings. The results suggest that the microstructure, specifically the excess free volume associated with porosity and interface disorder interconnected with the stress state, has a great impact on the mechanical properties, notably Young's modulus of Cu/W nanomultilayers.
DOI: 10.24435/materialscloud:qt-x9
title: FINALES - Electrolyte optimization for maximum conductivity and for maximum cycle life

description: This study investigates an electrolyte system composed of lithium hexafluorophosphate (LiPF6), ethylene carbonate (EC) and ethyl methyl carbonate (EMC). For the assembly of full cells, electrodes based on graphite and lithium nickel dioxide (LNO) are used. This work provides insight into the similarity of formulations of an electrolyte optimized for maximum conductivity and another one optimized for maximum cycle life are expected to be in this chemical system. The goal is to assess whether it is promising to target research efforts on finding an electrolyte formulation within this chemical space which can fulfill both requirements.
A campaign utilizing the latest version of FINALES is designed to determine conductivity values and predict end of life for various electrolyte formulations containing the aforementioned chemicals. The campaigns were able to reproducibly identify regions of high ionic conductivity of the aforementioned chemical composition. The ML methodology applied for the EOL optimisation allowed accelerated early life-time predictions.
DOI: 10.24435/materialscloud:bv-ge
title: Effect of residual stress and microstructure on mechanical properties of sputter-grown Cu/W nanomultilayers

description: The combination of the high wear resistance and mechanical strength of W with the high thermal conductivity of Cu makes the Cu/W system an attractive candidate material for heat sink plasma and radiation tolerance applications. However, the resulting mechanical properties of multilayers and coatings strongly depend on the microstructure of the layers. 
In this work, the mechanical properties of Cu/W nanomultilayers with different densities of internal interfaces are systematically investigated for two opposite in-plane stress states and critically discussed in comparison with literature. Atomistic simulations with the state-of-the-art neural network potential are used to explain the experimental findings. The results suggest that the microstructure, specifically the excess free volume associated with porosity and interface disorder interconnected with the stress state, has a great impact on the mechanical properties, notably Young's modulus of Cu/W nanomultilayers.
DOI: 10.24435/materialscloud:wb-sf
title: An anisotropic lattice Boltzmann - phase field model for dendrite growth and movement in rapid solidification of binary alloys

description: In this paper, we proposed a model coupling the lattice Boltzmann and the phase field methods with anisotropic effects is proposed, which is used to numerically describe the growth and movement of dendrites in rapid solidification of alloys. The model was applied to investigate the effects of dendrite movement and interfacial non-equilibrium on evolution of dendritic patterns for Si-9.0at%As and the CET for Al-3.0wt%Cu alloys. Both the growth and remelt processes of isolated dendrites are studied, and the result reveals the remelting influences on dendrite growth and solute micro-segregation in the condition of directional solidification. This dataset contains the underlying data for the above. This work demonstrates that the proposed model has a wide range of applicability and great potential to simulate the microstructure evolution with various solidification conditions.
DOI: 10.5281/zenodo.15728902
title: In situ XAS data in fluorescence mode during the photodeposition of H2PdCl4 on TiO2 support

description: The XAS data were collected in situ. TiO2 was deposited on the window of the photocatalytic cell. H2PdCl4 was dissolved in solution. The data were collected after specific irradiation times, indicated in the first line of the data file.
DOI: 10.5281/zenodo.15728903
title: In situ XAS data in fluorescence mode during the photodeposition of H2PdCl4 on TiO2 support

description: The XAS data were collected in situ. TiO2 was deposited on the window of the photocatalytic cell. H2PdCl4 was dissolved in solution. The data were collected after specific irradiation times, indicated in the first line of the data file.
DOI: 10.24435/materialscloud:s1-mx
title: An anisotropic lattice Boltzmann - phase field model for dendrite growth and movement in rapid solidification of binary alloys

description: In this paper, we proposed a model coupling the lattice Boltzmann and the phase field methods with anisotropic effects is proposed, which is used to numerically describe the growth and movement of dendrites in rapid solidification of alloys. The model was applied to investigate the effects of dendrite movement and interfacial non-equilibrium on evolution of dendritic patterns for Si-9.0at%As and the CET for Al-3.0wt%Cu alloys. Both the growth and remelt processes of isolated dendrites are studied, and the result reveals the remelting influences on dendrite growth and solute micro-segregation in the condition of directional solidification. This dataset contains the underlying data for the above. This work demonstrates that the proposed model has a wide range of applicability and great potential to simulate the microstructure evolution with various solidification conditions.
DOI: 10.24435/materialscloud:4d-p4
title: Solvation Structure and UV-Visible Absorption Spectra of the Nitrate Anion at the Air-Ice Interface by First Principles Molecular Simulations

description: Nitrate is a significant contaminant in Polar snow. Its photolysis in environmental sunlight generates reactive nitrogen, which impacts the oxidative capacity of the atmosphere, influencing the fate and lifetimes of pollutants. The photolysis of nitrate can produce either NO2 or NO2- , with recent experiments suggesting that the process is accelerated at the air-ice interface compared to the bulk solution. In this study, we employed multiscale modeling approaches to investigate the enhanced photoreactivity of nitrate at the ice surface in the presence of two different cations. We characterized the solvation shell of NO3- and explored its pairing with cations in water and ice using ab initio molecular dynamics and enhanced sampling. Molecular trajectories were used to calculate light absorption spectra at different solvation conditions and finite temperature. Our analysis revealed that the pairing of nitrate with cations may alter the molar absorption coefficient of nitrate at the air-ice interface affecting the rate of photolysis observed in experiments of ammonium nitrate deposited on snow.
DOI: 10.24435/materialscloud:kg-vy
title: Solvation Structure and UV-Visible Absorption Spectra of the Nitrate Anion at the Air-Ice Interface by First Principles Molecular Simulations

description: Nitrate is a significant contaminant in Polar snow. Its photolysis in environmental sunlight generates reactive nitrogen, which impacts the oxidative capacity of the atmosphere, influencing the fate and lifetimes of pollutants. The photolysis of nitrate can produce either NO2 or NO2- , with recent experiments suggesting that the process is accelerated at the air-ice interface compared to the bulk solution. In this study, we employed multiscale modeling approaches to investigate the enhanced photoreactivity of nitrate at the ice surface in the presence of two different cations. We characterized the solvation shell of NO3- and explored its pairing with cations in water and ice using ab initio molecular dynamics and enhanced sampling. Molecular trajectories were used to calculate light absorption spectra at different solvation conditions and finite temperature. Our analysis revealed that the pairing of nitrate with cations may alter the molar absorption coefficient of nitrate at the air-ice interface affecting the rate of photolysis observed in experiments of ammonium nitrate deposited on snow.
DOI: 10.24435/materialscloud:qv-xn
title: Neural network potential for Zr-H

description: The introduction of Hydrogen (H) into Zirconium (Zr) influences many mechanical properties, especially due to low H solubility and easy formation of Zirconium hydride phases. Understanding the various effects of H requires studies with atomistic resolution but at scales that incorporate defects such as cracks, interfaces, and dislocations. Such studies thus demand accurate interatomic potentials. Here, a neural network potential (NNP) for the Zr-H system is developed within the  Behler-Parrinello framework. The Zr-H NNP retains the accuracy of a recent NNP for hcp Zr and exhibits excellent agreement with first-principles density functional theory (DFT) for (i) H interstitials and their diffusion in hcp Zr, (ii) formation energies, elastic constants, and surface energies of relevant Zr hydrides, and (iii) energetics of a common Zr/Zr-H interface. The Zr-H NNP shows physical behavior for many different crack orientations in the most-stable ε-hydride and structures and reasonable relative energetics for the ⟨a⟩ screw dislocation in pure Zr. This Zr-H NNP should thus be very powerful for future study of many phenomena driving H degradation in Zr that require atomistic detail at scales far above those accessible by first-principles
DOI: 10.24435/materialscloud:b6-w0
title: Neural network potential for Zr-H

description: The introduction of Hydrogen (H) into Zirconium (Zr) influences many mechanical properties, especially due to low H solubility and easy formation of Zirconium hydride phases. Understanding the various effects of H requires studies with atomistic resolution but at scales that incorporate defects such as cracks, interfaces, and dislocations. Such studies thus demand accurate interatomic potentials. Here, a neural network potential (NNP) for the Zr-H system is developed within the  Behler-Parrinello framework. The Zr-H NNP retains the accuracy of a recent NNP for hcp Zr and exhibits excellent agreement with first-principles density functional theory (DFT) for (i) H interstitials and their diffusion in hcp Zr, (ii) formation energies, elastic constants, and surface energies of relevant Zr hydrides, and (iii) energetics of a common Zr/Zr-H interface. The Zr-H NNP shows physical behavior for many different crack orientations in the most-stable ε-hydride and structures and reasonable relative energetics for the ⟨a⟩ screw dislocation in pure Zr. This Zr-H NNP should thus be very powerful for future study of many phenomena driving H degradation in Zr that require atomistic detail at scales far above those accessible by first-principles
DOI: 10.24435/materialscloud:df-x5
title: Interfacial fluid rheology of soft particles

description: In situ interfacial rheology and numerical simulations are used to investigate microgel monolayers in a wide range of packing fractions, ζ2D. The heterogeneous particle compressibility determines two flow regimes characterized by distinct master curves. To mimic the microgel architecture and reproduce experiments, an interaction potential combining a soft shoulder with the Hertzian model is introduced. In contrast to bulk conditions, the elastic moduli vary nonmonotonically with ζ2D at the interface, confirming long-sought predictions of reentrant behavior for Hertzian-like systems.
DOI: 10.24435/materialscloud:gh-0x
title: Interfacial fluid rheology of soft particles

description: In situ interfacial rheology and numerical simulations are used to investigate microgel monolayers in a wide range of packing fractions, ζ2D. The heterogeneous particle compressibility determines two flow regimes characterized by distinct master curves. To mimic the microgel architecture and reproduce experiments, an interaction potential combining a soft shoulder with the Hertzian model is introduced. In contrast to bulk conditions, the elastic moduli vary nonmonotonically with ζ2D at the interface, confirming long-sought predictions of reentrant behavior for Hertzian-like systems.
DOI: 10.24435/materialscloud:5p-vq
title: Optical materials discovery and design with federated databases and machine learning

description: Combinatorial and guided screening of materials space with density-functional theory and related approaches has provided a wealth of hypothetical inorganic materials, which are increasingly tabulated in open databases. The OPTIMADE API is a standardised format for representing crystal structures, their measured and computed properties, and the methods for querying and filtering them from remote resources. Currently, the OPTIMADE federation spans over 20 data providers, rendering over 30 million structures accessible in this way, many of which are novel and have only recently been suggested by machine learning-based approaches. In this work, we outline our approach to non-exhaustively screen this dynamic trove of structures for the next-generation of optical materials. By applying MODNet, a neural network-based model for property prediction that has been shown to perform especially well for small materials datasets, within a combined active learning and high-throughput computation framework, we isolate particular structures and chemistries that should be most fruitful for further theoretical calculations and for experimental study as high-refractive-index materials. By making explicit use of automated calculations, federated dataset curation and machine learning, and by releasing these publicly, the workflows presented here can be periodically re-assessed as new databases implement OPTIMADE, and new hypothetical materials are suggested.
DOI: 10.24435/materialscloud:bd-fp
title: The three-dimensional atomic-level structure of an amorphous glucagon-like peptide-1 receptor agonist

description: Amorphous formulations are increasingly used in the pharmaceutical industry due to their increased solubility, but their structural characterization at atomic-level resolution remains extremely challenging. Here, we characterize the complete atomic-level structure of an amorphous glucagon-like peptide-1 receptor (GLP-1R) agonist using chemical shift driven NMR crystallography. The structure is determined from measured chemical shift distributions for 17 of the 32 carbon atoms and 16 of the 31 hydrogen atoms in the molecule. The chemical shifts are able to provide a detailed picture of the atomic-level conformations and interactions, and we identify the structural motifs that play a major role in stabilization of the amorphous form. In particular, hydrogen bonding of the carboxylic acid proton is strongly promoted although no carboxylic acid dimer is formed. Two orientations of the benzodioxole ring are promoted in the NMR structure, corresponding to a significant stabilization mechanism. Our observation that inclusion of water leads to stabilization of the carboxylic acid group might be used as a strategy in future formulations where hydrogen bonding between neighboring molecules may otherwise be hindered by sterics.
DOI: 10.24435/materialscloud:ed-g2
title: Optical materials discovery and design with federated databases and machine learning

description: Combinatorial and guided screening of materials space with density-functional theory and related approaches has provided a wealth of hypothetical inorganic materials, which are increasingly tabulated in open databases. The OPTIMADE API is a standardised format for representing crystal structures, their measured and computed properties, and the methods for querying and filtering them from remote resources. Currently, the OPTIMADE federation spans over 20 data providers, rendering over 30 million structures accessible in this way, many of which are novel and have only recently been suggested by machine learning-based approaches. In this work, we outline our approach to non-exhaustively screen this dynamic trove of structures for the next-generation of optical materials. By applying MODNet, a neural network-based model for property prediction that has been shown to perform especially well for small materials datasets, within a combined active learning and high-throughput computation framework, we isolate particular structures and chemistries that should be most fruitful for further theoretical calculations and for experimental study as high-refractive-index materials. By making explicit use of automated calculations, federated dataset curation and machine learning, and by releasing these publicly, the workflows presented here can be periodically re-assessed as new databases implement OPTIMADE, and new hypothetical materials are suggested.
DOI: 10.24435/materialscloud:ex-sh
title: The three-dimensional atomic-level structure of an amorphous glucagon-like peptide-1 receptor agonist

description: Amorphous formulations are increasingly used in the pharmaceutical industry due to their increased solubility, but their structural characterization at atomic-level resolution remains extremely challenging. Here, we characterize the complete atomic-level structure of an amorphous glucagon-like peptide-1 receptor (GLP-1R) agonist using chemical shift driven NMR crystallography. The structure is determined from measured chemical shift distributions for 17 of the 32 carbon atoms and 16 of the 31 hydrogen atoms in the molecule. The chemical shifts are able to provide a detailed picture of the atomic-level conformations and interactions, and we identify the structural motifs that play a major role in stabilization of the amorphous form. In particular, hydrogen bonding of the carboxylic acid proton is strongly promoted although no carboxylic acid dimer is formed. Two orientations of the benzodioxole ring are promoted in the NMR structure, corresponding to a significant stabilization mechanism. Our observation that inclusion of water leads to stabilization of the carboxylic acid group might be used as a strategy in future formulations where hydrogen bonding between neighboring molecules may otherwise be hindered by sterics.
DOI: 10.24435/materialscloud:qa-5v
title: Disorder-resilient transition of Helical to Conical ground states in M1/3NbS2, M=Cr,Mn

description: The discovery of chiral helical magnetism (CHM) in Cr1/3NbS2 and the stabilization of a chiral soliton lattice (CSL) has attracted considerable interest in view of their potential technological applications. However, there is an ongoing debate regarding whether the sister compound, Mn1/3NbS2, which shares the same crystal structure, exhibits similar nontrivial properties, which rely on the stabilization of the lack of inversion symmetry at the magnetic ion. In this study, we conduct a comprehensive investigation of the magnetically ordered states of both compounds, using 53Cr, 55Mn and 93Nb nuclear magnetic resonance.  Our results, supported by density functional  calculations, detect in a high quality single crystal of Cr1/3NbS2 all the signatures of the monoaxial CHM in  a magnetic field, identifying it as a textbook NMR case. The detailed understanding of this prototypic  behavior provides a reference for Mn1/3NbS2.
Despite the much larger density of specific defects in this second single crystal, we confirm the presence of a CHM phase in the Mn compound, characterized by a very large critical field for the forced ferromagnetic phase (~ 10 T for H || c).