cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.dadv3f
title: Occurrence Download

description: A dataset containing 613 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hyla cinerea cinerea"
 ]
}

 The dataset includes 613 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004588-150811131857512/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bd2npm
title: Occurrence Download

description: A dataset containing 28246 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Avicennia marina (Forssk.) Vierh."
 ]
}

 The dataset includes 28246 records from 185 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055533-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sgavwe
title: Occurrence Download

description: A dataset containing 220093 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cervus elaphus Linnaeus, 1758"
 ]
}

 The dataset includes 220093 records from 952 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012775-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14608361
title: Data and code for "Phase-dependent supercurrent and microwave dissipation of HgTe quantum well Josephson junctions"

description: This repository contains data, metadata, and code for "Phase-dependent supercurrent and microwave dissipation of HgTe quantum well Josephson junctions" by Wei Liu and co-workers, DOI: 10.1103/PhysRevB.111.104503. 

The data contain the measurement results of microwave admittance and I-V traces of HgTe Josephson junction from four devices, placed in separate subfolders inside the "measurements" folder, and simulation results for tight-binding spectra of superconducting Josephson junctions with varied disorder and interface transparency in the "simulations" subfolder.

Each device subfolder contains manifest.txt file with the description of corresponding data files. One manifest.txt is placed in "simulations" subfolder with a description of simulations data format.

Root folder contains figX.py files, with "X" being the figure number, which produce the corresponding figures from the paper and Supplemental Material.

File information:

data.zip:  Zip-compressed data and scripts.

Extensions:




.py
python code file


.txt
text information file


.npz
numpy compressed binary file


.png
images of simulated spectra
DOI: 10.5281/zenodo.14608362
title: Data and code for "Phase-dependent supercurrent and microwave dissipation of HgTe quantum well Josephson junctions"

description: This repository contains data, metadata, and code for "Phase-dependent supercurrent and microwave dissipation of HgTe quantum well Josephson junctions" by Wei Liu and co-workers, DOI: 10.1103/PhysRevB.111.104503. 

The data contain the measurement results of microwave admittance and I-V traces of HgTe Josephson junction from four devices, placed in separate subfolders inside the "measurements" folder, and simulation results for tight-binding spectra of superconducting Josephson junctions with varied disorder and interface transparency in the "simulations" subfolder.

Each device subfolder contains manifest.txt file with the description of corresponding data files. One manifest.txt is placed in "simulations" subfolder with a description of simulations data format.

Root folder contains figX.py files, with "X" being the figure number, which produce the corresponding figures from the paper and Supplemental Material.

File information:

data.zip:  Zip-compressed data and scripts.

Extensions:




.py
python code file


.txt
text information file


.npz
numpy compressed binary file


.png
images of simulated spectra
DOI: 10.15468/dl.zac2r3
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((100 22,104 22,104 25.6,100 25.6,100 22))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Merops philippinus Linnaeus, 1767"
 ]
}

 The dataset includes 13 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067075-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15727063
title: The four datasets used in the paper
DOI: 10.5281/zenodo.15727062
title: The four datasets used in the paper
DOI: 10.24435/materialscloud:2018.0011/v3
title: Capturing chemical intuition in synthesis of metal-organic frameworks

description: We report a methodology using machine learning to capture chemical intuition from a set of (partially) failed attempts to synthesize a metal organic framework. We define chemical intuition as the collection of unwritten guidelines used by synthetic chemists to find the right synthesis conditions. As (partially) failed experiments usually remain unreported, we have reconstructed a typical track of failed experiments in a successful search for finding the optimal synthesis conditions that yields HKUST-1 with the highest surface area reported to date. We illustrate the importance of quantifying this chemical intuition for the synthesis of novel materials.
DOI: 10.24435/materialscloud:2018.0009/v1
title: Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

description: Here we present 1,000 structures each of a water monomer, water dimer, Zundel cation and bulk water used to train tensorial machine-learning models in Phys. Rev. Lett. 120, 036002 (2018). The archive entry contains files in extended-XYZ format including the structures and several tensorial properties: for the monomer, dimer and Zundel cation, the dipole moment, polarizability and first hyperpolarizability are included, and for bulk water the dipole moment, polarizability and dielectric tensor are given.
DOI: 10.24435/materialscloud:nv-dg
title: Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

description: Here we present 1,000 structures each of a water monomer, water dimer, Zundel cation and bulk water used to train tensorial machine-learning models in Phys. Rev. Lett. 120, 036002 (2018). The archive entry contains files in extended-XYZ format including the structures and several tensorial properties: for the monomer, dimer and Zundel cation, the dipole moment, polarizability and first hyperpolarizability are included, and for bulk water the dipole moment, polarizability and dielectric tensor are given.
DOI: 10.24435/materialscloud:2019.0013/v1
title: Free-Energy Surface Prediction by Flying Gaussian Method: Numerical Proof

description: Biomolecular simulations have a great potential in protein engineering, drug discovery and many other fields. Unfortunately, this method is computationally expensive, so many interesting processes cannot be routinely studied. In order to address this problem we developed Flying Gaussian method [Journal of Chemical Theory and Computation 12, 4644-4650 (2016)]. This method simultaneously simulates multiple replicas of the studied system and disfavor replicas with similar structures by artificial bias potential. The question arises how to calculate an unbiased free energy surface from a biased simulation. This dataset demonstrates together with mathematical arguments supports application of Umbrella Sampling reweighing method, despite the fact that this method is designed for methods with a time-independent bias potential.
DOI: 10.24435/materialscloud:2018.0007/v2
title: Mail-order metal-organic frameworks (MOFs): designing isoreticular MOF-5 analogues comprising commercially available organic molecules

description: Metal–organic frameworks (MOFs), a class of porous materials, are of particular interest in gas storage and separation applications due largely to their high internal surface areas and tunable structures. MOF-5 is perhaps the archetypal MOF; in particular, many isoreticular analogues of MOF-5 have been synthesized, comprising alternative dicarboxylic acid ligands. In this contribution we introduce a new set of hypothesized MOF-5 analogues, constructed from commercially available organic molecules. We describe our automated procedure for hypothetical MOF design, comprising selection of appropriate ligands, construction of 3D structure models, and structure relaxation methods. 116 MOF-5 analogues were designed and characterized in terms of geometric properties and simulated methane uptake at conditions relevant to vehicular storage applications. A strength of the presented approach is that all of the hypothesized MOFs are designed to be synthesizable utilizing ligands purchasable online.

Version 2 includes the structures in CIF format.
DOI: 10.24435/materialscloud:vq-vf
title: Free-Energy Surface Prediction by Flying Gaussian Method: Numerical Proof

description: Biomolecular simulations have a great potential in protein engineering, drug discovery and many other fields. Unfortunately, this method is computationally expensive, so many interesting processes cannot be routinely studied. In order to address this problem we developed Flying Gaussian method [Journal of Chemical Theory and Computation 12, 4644-4650 (2016)]. This method simultaneously simulates multiple replicas of the studied system and disfavor replicas with similar structures by artificial bias potential. The question arises how to calculate an unbiased free energy surface from a biased simulation. This dataset demonstrates together with mathematical arguments supports application of Umbrella Sampling reweighing method, despite the fact that this method is designed for methods with a time-independent bias potential.
DOI: 10.24435/materialscloud:2017.0001/v1
title: Barcodes for nanoporous materials

description: In most applications of nanoporous materials the pore structure is as important as the chemical composition as a determinant of performance. For example, one can alter performance in applications like carbon capture or methane storage by orders of magnitude by only modifying the pore structure. For these applications it is therefore important to identify the optimal pore geometry and use this information to find similar materials. However, the mathematical language and tools to identify materials with similar pore structures, but different composition, has been lacking. Recently, we developed a pore recognition approach to quantify similarity of pore structures using topological data analysis. Barcodes generated with using this approach allow us to identify materials with similar pore geometries, and to screen for materials that are similar to given top-performing structures. This database has barcodes for zeolites, metal organic frameworks, and zeolitic imidazolate frameworks.
DOI: 10.24435/materialscloud:2018.0010/v1
title: Mapping uncharted territory in ice from zeolite networks to ice structures

description: We report a large-scale density-functional-theory study of the configuration space of water ice. We geometry optimise 74,963 ice structures, which are selected and constructed from over five million tetrahedral networks listed in the databases of Treacy and Deem, and the International Zeolite Association database. All prior knowledge of ice is set aside and we introduce generalised convex hulls to identify configurations stabilised by appropriate thermodynamic constraints. We thereby rediscover all known phases (I to XVII, i, 0 and the quartz phase) except the metastable ice IV. Crucially, we also find promising candidates for ices XVIII through LI. Using the sketch-map dimensionality-reduction algorithm we construct an a priori, navigable map of configuration space, which reproduces similarity relations between structures and highlights the novel candidates. By relating the known phases to the tractably small, yet structurally diverse set of synthesisable candidate structures, we provide an excellent starting point for identifying formation pathways.
DOI: 10.24435/materialscloud:en-ze
title: Barcodes for nanoporous materials

description: In most applications of nanoporous materials the pore structure is as important as the chemical composition as a determinant of performance. For example, one can alter performance in applications like carbon capture or methane storage by orders of magnitude by only modifying the pore structure. For these applications it is therefore important to identify the optimal pore geometry and use this information to find similar materials. However, the mathematical language and tools to identify materials with similar pore structures, but different composition, has been lacking. Recently, we developed a pore recognition approach to quantify similarity of pore structures using topological data analysis. Barcodes generated with using this approach allow us to identify materials with similar pore geometries, and to screen for materials that are similar to given top-performing structures. This database has barcodes for zeolites, metal organic frameworks, and zeolitic imidazolate frameworks.
DOI: 10.24435/materialscloud:71-xe
title: Mapping uncharted territory in ice from zeolite networks to ice structures

description: We report a large-scale density-functional-theory study of the configuration space of water ice. We geometry optimise 74,963 ice structures, which are selected and constructed from over five million tetrahedral networks listed in the databases of Treacy and Deem, and the International Zeolite Association database. All prior knowledge of ice is set aside and we introduce generalised convex hulls to identify configurations stabilised by appropriate thermodynamic constraints. We thereby rediscover all known phases (I to XVII, i, 0 and the quartz phase) except the metastable ice IV. Crucially, we also find promising candidates for ices XVIII through LI. Using the sketch-map dimensionality-reduction algorithm we construct an a priori, navigable map of configuration space, which reproduces similarity relations between structures and highlights the novel candidates. By relating the known phases to the tractably small, yet structurally diverse set of synthesisable candidate structures, we provide an excellent starting point for identifying formation pathways.
DOI: 10.24435/materialscloud:dz-a0
title: Simulating the ghost: quantum dynamics of the solvated electron

description: The nature of the bulk hydrated electron has been a challenge for both experiment and theory due to its short lifetime and high reactivity, and the need for a high-level of electronic structure theory to achieve predictive accuracy. The lack of a classical atomistic structural formula makes it exceedingly difficult to model the solvated electron using conventional empirical force fields, which describe the system in terms of interactions between point particles associated with atomic nuclei. Here we overcome this problem using a machine-learning model, that is sufficiently flexible to describe the effect of the excess electron on the structure of the surrounding water, without including the electron in the model explicitly. The resulting potential is not only able to reproduce the stable cavity structure but also recovers the correct localization dynamics that follow the injection of an electron in neat water. The machine learning model achieves the accuracy of the state-of-the-art correlated wave function method it is trained on. It is sufficiently inexpensive to afford a full quantum statistical and dynamical description and allows us to achieve accurate determination of the structure, diffusion mechanisms, and vibrational spectroscopy of the solvated electron.
DOI: 10.24435/materialscloud:bj-dq
title: Simulating the ghost: quantum dynamics of the solvated electron

description: The nature of the bulk hydrated electron has been a challenge for both experiment and theory due to its short lifetime and high reactivity, and the need for a high-level of electronic structure theory to achieve predictive accuracy. The lack of a classical atomistic structural formula makes it exceedingly difficult to model the solvated electron using conventional empirical force fields, which describe the system in terms of interactions between point particles associated with atomic nuclei. Here we overcome this problem using a machine-learning model, that is sufficiently flexible to describe the effect of the excess electron on the structure of the surrounding water, without including the electron in the model explicitly. The resulting potential is not only able to reproduce the stable cavity structure but also recovers the correct localization dynamics that follow the injection of an electron in neat water. The machine learning model achieves the accuracy of the state-of-the-art correlated wave function method it is trained on. It is sufficiently inexpensive to afford a full quantum statistical and dynamical description and allows us to achieve accurate determination of the structure, diffusion mechanisms, and vibrational spectroscopy of the solvated electron.
DOI: 10.24435/materialscloud:wt-98
title: A deep learning dataset for metal multiaxial fatigue life prediction

description: In this work, we present a comprehensive dataset designed to facilitate the prediction of metal fatigue life using deep learning techniques. The dataset includes detailed experimental data from 40 different metallic materials, comprising a total of 1195 data points under 48 distinct loading paths. Each data point is stored in a CSV file, capturing the loading path as a time-series with axial and tangential stress or strain values.The primary purpose of this dataset is to support the development and validation of deep learning models aimed at accurately predicting the fatigue life of metals under various loading conditions. This dataset includes stress-controlled and strain-controlled data, ensuring a broad representation of experimental scenarios. Additionally, an Excel file accompanies the dataset, providing detailed mechanical properties of each material, such as elastic modulus, tensile strength, yield strength, and Poisson's ratio, along with references to the original experimental sources.This dataset is intended for researchers in materials science and mechanical engineering, offering a robust foundation for training and testing deep learning algorithms in fatigue analysis. By making this dataset publicly available, we aim to foster collaboration and further advancements in the field of metal fatigue prediction. Researchers are encouraged to utilize and contribute to the dataset, thereby enhancing its scope and applicability.
DOI: 10.24435/materialscloud:2018.0002/v1
title: Adatom-Induced Local Melting

description: We introduce and discuss the phenomenon of adatom-induced surface local melting, using extensive first-principles molecular dynamics simulations of Al(100) taken as a paradigmatic case of a non-premelting surface that nevertheless displays facile adatom diffusion with single and multiple exchange pathways. Here, a single adatom deposited on the surface is sufficient to nucleate a localized and diffusing liquid-like region that remains confined to the surface layer, but with an area that increases with temperature; in the absence of the adatom, the surface instead remains crystalline until reaching the bulk melting temperature.
DOI: 10.24435/materialscloud:3t-va
title: Adatom-Induced Local Melting

description: We introduce and discuss the phenomenon of adatom-induced surface local melting, using extensive first-principles molecular dynamics simulations of Al(100) taken as a paradigmatic case of a non-premelting surface that nevertheless displays facile adatom diffusion with single and multiple exchange pathways. Here, a single adatom deposited on the surface is sufficient to nucleate a localized and diffusing liquid-like region that remains confined to the surface layer, but with an area that increases with temperature; in the absence of the adatom, the surface instead remains crystalline until reaching the bulk melting temperature.
DOI: 10.24435/materialscloud:ad-xk
title: A deep learning dataset for metal multiaxial fatigue life prediction

description: In this work, we present a comprehensive dataset designed to facilitate the prediction of metal fatigue life using deep learning techniques. The dataset includes detailed experimental data from 40 different metallic materials, comprising a total of 1195 data points under 48 distinct loading paths. Each data point is stored in a CSV file, capturing the loading path as a time-series with axial and tangential stress or strain values.The primary purpose of this dataset is to support the development and validation of deep learning models aimed at accurately predicting the fatigue life of metals under various loading conditions. This dataset includes stress-controlled and strain-controlled data, ensuring a broad representation of experimental scenarios. Additionally, an Excel file accompanies the dataset, providing detailed mechanical properties of each material, such as elastic modulus, tensile strength, yield strength, and Poisson's ratio, along with references to the original experimental sources.This dataset is intended for researchers in materials science and mechanical engineering, offering a robust foundation for training and testing deep learning algorithms in fatigue analysis. By making this dataset publicly available, we aim to foster collaboration and further advancements in the field of metal fatigue prediction. Researchers are encouraged to utilize and contribute to the dataset, thereby enhancing its scope and applicability.
DOI: 10.24435/materialscloud:2018.0017/v1
title: Evaluating charge equilibration methods to generate electrostatic fields in nanoporous materials

description: Charge equilibration (Qeq) methods can estimate the electrostatic potential of molecules and periodic frameworks by assigning point charges to each atom, using only a small fraction of the resources needed to compute density functional (DFT)-derived charges. This makes possible, for example, the computational screening of thousands of microporous structures to assess their performance for the adsorption of polar molecules. Recently, different variants of the original Qeq scheme were proposed to improve the quality of the computed point charges. One focus of this research was to improve the gas adsorption predictions in Metal Organic Frameworks (MOFs), for which many different structures are available. In this work, we review the evolution of the method from the original Qeq scheme, understanding the role of the different modifications on the final output. We evaluated the result of combining different protocols and set of parameters, by comparing the Qeq charges with high quality DFT-derived DDEC charges for 2338 MOF structures. We focused on the systematic errors that are attributable to specific atom types to quantify the final precision that one can expect from Qeq methods in the context of gas adsorption where the electrostatic potential plays a significant role, namely CO2 and H2S adsorption. In conclusion, both the type of algorithm and the input parameters have a large impact on the resulting charges and we draw some guidelines to help the user to choose the proper combination of the two for obtaining a meaningful set of charges. We show that, considering this set of MOFs, the accuracy of the original Qeq scheme is often still comparable with the most recent variants, even if it clearly fails in the presence of certain atom types, such as alkali metals.