Low-temperature alloys are important for a wide spectrum of modern technologies ranging from liquid hydrogen, superconductivity to the quantum technology. These applications push the limit of material performance into extreme coldness, often demanding a combination of strength and toughness to address various challenges.Steel is one of the most widely used materials in cryogenic applications. With the deployment in aerospace liquid hydrogen storage and transportation, aluminum and titanium alloys are also gaining increasing attention. Emerging medium-entropy alloys (MEAs) and high-entropy alloys (HEAs) demonstrate excellent low-temperature mechanical performance with a much-expanded space of material design. A database of low-temperature metallic alloys is reported here by collecting the literature data published from 1991 to 2023, which is hosted in an open repository. The workflow of data collection includes automated extraction based on machine learning and natural language processing, supplemented by manual inspection and correction, to enhance data extraction efficiency and database quality. The product datasets cover key performance parameters including yield strength, tensile strength, elongation at fracture, Charpy impact energy, as well as detailed information on materials such as their types, chemical compositions, processing and testing conditions. Data statistics are analyzed to elucidate the research and development patterns and clarify the challenges in both scientific exploration and engineering deployment.

NIST has accumulated a valuable and comprehensive collection of thermal conductivity data from measurements performed with a 200-mm square guarded-hot-plate apparatus. The guarded-hot-plate test method is arguably the most accurate and popular method for determination of thermal transmission properties of flat, homogeneous specimens under steady state conditions. Several organizations, including ASTM and ISO, have standardized the method. Version 1.0 of the database includes data for over 2000 measurements, covering several categories of materials including concrete, fiberboard, plastics, thermal insulation, and rubber. The data cover a temperature range corresponding to most building applications; however, the majority of the measurements were conducted at 24° C (75° F). Web version 1.0

This Database has been created from the NLP code delivered during the project, which has extracted compositions and properties from different journal papers. The headings in the database are the doi of the paper, the line the data is extracted from, the composition of the material, the units of the property and then the value of the property

Dataset Overview

This dataset provides the Young's Modulus values (in GPa) for 50 metals, covering a wide range of categories such as alkali metals, alkaline earth metals, transition metals, and rare earth elements. Young's Modulus is a fundamental mechanical property that measures a material's stiffness under tensile or compressive stress. It is critical for applications in materials science, physics, and engineering.

The dataset includes: - 50 metals with their chemical symbols and Young's Modulus values. - A wide range of stiffness values, from soft metals like cesium (1.7 GPa) to very stiff metals like ruthenium (447 GPa). - Clean and complete data with no missing or duplicate entries.

Data Science Applications

This dataset can be utilized in various data science and engineering applications, such as: 1. Material Property Prediction: Train machine learning models to predict mechanical properties based on elemental features. 2. Cluster Analysis: Group metals based on their mechanical properties or periodic trends. 3. Correlation Studies: Explore relationships between Young's Modulus and other physical/chemical properties (e.g., density, atomic radius). 4. Engineering Simulations: Use the data for simulations in structural analysis or material selection for design purposes. 5. Visualization and Education: Create visualizations to teach periodic trends and material property variations.

Column Descriptors

Ethically Mined Data

The dataset was ethically sourced from publicly available scientific references and academic resources. The data was verified for accuracy using multiple authoritative sources, ensuring reliability for research and educational purposes. No proprietary or sensitive information was included.

Key checks performed: - No missing values: The dataset contains complete entries for all 50 metals. - No duplicates: Each metal appears only once in the dataset. - Statistical analysis: The mean Young's Modulus is ~98.93 GPa, with a wide range from 1.7 GPa to 447 GPa.

Acknowledgments

We would like to thank the following sources for their contributions to this dataset: - Academic references such as WebElements, Byju's Chemistry Resources, and Wikipedia for cross-verifying the data. - Scientific databases like MatWeb and ASM International for providing accurate material property data. - Special thanks to DALL·E 3 for generating the accompanying dataset image.

A complete copy of the Materials Project database as of 10/18/2018. Mp_all files contain structure data for each material while mp_nostruct does not.Available as Monty Encoder encoded JSON and as CSV. Recommended access method for these particular files is with the matminer Python package using the datasets module. Access to the current Materials Project is recommended through their API (good), pymatgen (better), or matminer (best).Note on citations: If you found this dataset useful and would like to cite it in your work, please be sure to cite its original sources below rather than or in addition to this page.Dataset discussed in:A. Jain*, S.P. Ong*, G. Hautier, W. Chen, W.D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, K.A. Persson (*=equal contributions) The Materials Project: A materials genome approach to accelerating materials innovation APL Materials, 2013, 1(1), 011002.Dataset sourced from:https://materialsproject.org/Citations for specific material properties available here:https://materialsproject.org/citing

Data created at the European Commission JRC in the scope of the EERA JPNM pilot project NINA on the topic of nanoindentation for nuclear applications.

Two-dimensional (2D) materials are among the most promising candidates for beyond silicon electronic and optoelectronic applications. Recently, their recognized importance, sparked a race to discover and characterize new 2D materials. Within few years the number of experimentally exfoliated or synthesized 2D materials went from a couple of dozens to few hundreds while the number theoretically predicted compounds reached a few thousands. In 2018 we first contributed to this effort with the identification of 1825 compounds that are either easily (1036) or potentially (789) exfoliable from experimentally known 3D compounds. In the present work we report on the new materials recently added to the 2D-portfolio thanks to the extension of the screening to an additional experimental database (MPDS) as well as the most up-to-date versions of the two databases (ICSD and COD) used in our previous work. This expansion led to the discovery of an additional 1252 unique monolayers bringing the total to 3077 compounds and, notably, almost doubling the number of easily exfoliable materials (2004). Moreover, we optimized the structural properties of all the materials (regardless of their binding energy or number of atoms in the unit cell) as isolated mono-layer and explored their electronic band structure. This archive entry contains the database of 2D materials in particular it contains the structural parameters for all the 3077 structures of the global Material Cloud 2D database as extracted from their bulk 3D parent, 2710 optimized 2D structures and 2345 electronic band structure together with the provenance of all data and calculations as stored by AiiDA.

The prediction of material properties through electronic-structure simulations based on density-functional theory has become routinely common, thanks, in part, to the steady increase in the number and robustness of available simulation packages. This plurality of codes and methods aiming to solve similar problems is both a boon and a burden. While providing great opportunities for cross-verification, these packages adopt different methods, algorithms, and paradigms, making it challenging to choose, master, and efficiently use any one for a given task. Leveraging recent advances in managing reproducible scientific workflows, we demonstrate how developing common interfaces for workflows that automatically compute material properties can tackle the challenge mentioned above, greatly simplifying interoperability and cross-verification. We introduce design rules for reproducible and reusable code-agnostic workflow interfaces to compute well-defined material properties, which we implement for eleven different quantum engines and use to compute three different material properties. Each implementation encodes carefully selected simulation parameters and workflow logic, making the implementer's expertise of the quantum engine directly available to non-experts. Full provenance and reproducibility of the workflows is guaranteed through the use of the AiiDA infrastructure. All workflows are made available as open-source and come pre-installed with the Quantum Mobile virtual machine, making their use straightforward. This entry contains all data and scripts to reproduce the figures of the corresponding scientific paper.

Selected material properties

Data created at the European Commission JRC during the H2020 project on multiscale modeling for fusion and fission materials (M4F), funded from the Euratom research and training programme 2014-2018 under grant agreement No. 755039.

An in-house developed finite-difference time-domain (FDTD) code has been used to simulate certain patterned defects as found in the semiconductor industry. Intrinsic to FDTD is the establishment of a simulation domain, a 3-D matrix of some arbitrary size (X, Y, Z) comprised of smaller cells (in our case, cubic with side length x), with each cell indexed to a material (including the vacuum) to form the geometry. Although the specific text files used as inputs to the in-house FDTD engine are provided, such files are likely incompatible with external FDTD solutions for the replication of our results. Therefore, entire 3-D matrices for our simulations have been reduced to single-vector, readable ASCII data files indexing the geometry and materials of the system, accompanied by text files that supply the optical constants used in the simulation as well as cross-sectional images that allow verification by others of their reconstruction of the 3-D matrix from the supplied 1-D ASCII data files.

The dataset provides records of tensile properties of nuclear structural materials. The focus is on studying the influence of specimen dimensions and geometry on mechanical properties such as yield strength, ultimate tensile strength, uniform elongation, and total elongation. The dataset was created through an extensive literature review of scientific articles and databases. The search inclusion criteria targeted peer-reviewed studies on tensile testing of sub-sized specimens, providing quantitative data on tensile properties relative to specimen size. The extracted data points from the literature review were organized into a tabular format database containing 1,070 tensile testing records with 54 parameters, including material type and composition, manufacturing information, irradiation conditions, specimen size and dimensions, and tensile properties. Materials science experts conducted systematic checks to validate the collected data, ensuring accuracy in the material type, manufacturing processes and treatment methods, and testing conditions, as well as verifying the chemical composition and other pertinent information. Our team performed statistical analyses to identify and address data outliers, ensuring the reliability of the dataset.

The dataset provided in this repository comprises data obtained from a series of characterization tests performed to a sheet of typical S355 (material number: 1.0577) structural steel (designation of steel according to DIN EN 10025-2:2019). The tests include methods for the determination of mechanical properties such as, e.g., tensile test, Charpy test and sonic resonance test. This dataset is intended to be extended by the inclusion of data obtained from further test methods. Therefore, the entire dataset (concept DOI) comprises several parts (versions), each of which is addressed by a unique version DOI.

The data were generated in the frame of the digitization project Innovationplatform MaterialDigital (PMD) which, amongst other activities, aims to store data in a semantically and machine understandable way. Therefore, data structuring and data formats are focused in addition to aspects in the field of material science and engineering (MSE). Hence, this data is supposed to provide reference data as basis for experimental data inclusion, conversion and structuring (data management and processing) that leads to semantical expressivity as well as for MSE experts being generally interested in the material properties and knowledge.

V2DB: A two-dimensional (2D) materials database, created by the Autonomous Energy Materials Discovery [AMD] research group, consists of 316,505 likely to be stable materials with AI predicted key properties (energy, electronic, and magnetic).

This dataset consists of

1) Excel sheets with material properties for a) a collection of historic building materials and b) insulation materials suited for internal insulation. Material properties were collected from all RIBuild partner countries. The main purpose was to locate data set not included in the DELPHIN database that contained the needed data for DELPHIN simulations.

2) Excel sheets with material properties for specific historic building materials used in Italy and Switzerland. As these materials were supposed to be included in hygrothermal simulations with DELPHIN performed in RIBuild, a full material characterization was required as described in RIBuild deliverable D2.1 and in the DELPHIN specifications. Material characterization was performed at RTU.

Requirements concerning input need for DELPHIN simulations are described in RIBuild deliverable D2.1.

Overview of data files to be found in 'RIBuild data WP2 Mat Prop' as part of this dataset.

Data created at the European Commission JRC in the scope of the EERA JPNM pilot project NINA on the topic of nanoindentation for nuclear applications.

The PoroTomo team has completed inverse modeling of the three data sets (seismology, geodesy, and hydrology) individually, as described previously. The estimated values of the material properties are registered on a three-dimensional grid with a spacing of 25 meters between nodes. The material properties are listed an Excel file. Figures show planar slices in three sets: horizontal slices in a planes normal to the vertical Z axis (Z normal), vertical slices in planes perpendicular to the dominant strike of the fault system (X normal), and vertical slices in planes parallel to the dominant strike of the fault system (Y normal). The results agree on the following points. The material is unconsolidated and/or fractured, especially in the shallow layers. The structural trends follow the fault system in strike and dip. The geodetic measurements favor the hypothesis of thermal contraction. Temporal changes in pressure, subsidence rate, and seismic amplitude are associated with changes in pumping rates during the four stages of the deployment in 2016. The modeled hydraulic conductivity is high in fault damage zones. All the observations are consistent with the conceptual model: highly permeable conduits along faults channel fluids from shallow aquifers to the deep geothermal reservoir tapped by the production wells.

The database includes mechanical data for structure-properties relationships and mechanical modeling of elastic impact protection foams from a variety of imaging (micro-computed tomography, digital image correlation) and force-sensing instruments (dynamic mechanical analysis, universal test system) under a wide range of experimental conditions and modes. The data repository includes directories for: dynamic mechanical analysis raw data, results, and analysis tools; intermediate rate (servo-hydraulic UTS based) raw data including 2D digital image correlation (DIC) images, results, and analysis tools; quasi-static rate (electro-mechanical UTS based) raw data including 2D digital image correlation (DIC), results, and analysis tools; micro-computed tomography data including raw volume images, filtered images, binarized images, other results, and analysis tools; and, instrumented drop tower data including backface force, high speed video, and results and analyzed data, Fourier Transform Infrared (FTIR) spectra, and differential scanning calorimetry (DSC) data.For more information see the readme and data documentation in each respective directory. A paper describing data collection, analysis, and database documentation is available here: https://doi.org/10.1038/s41597-023-02092-4. A repository containing example usage code is available at: https://github.com/materials-data-facility/foam_db. File formats for data include .txt, .xls, .tri, .tprc, .rcp, .py, .m, .csv, .mat, .vtk, .spa, .exp, .stl, and .tif.

The graph representation of complex materials plays a crucial role in the field of inorganic and organic materials investigations for developing data-centric materials science, such as those using graph neural networks (GNNs). However, the currently prevalent GNN models are primarily employed for investigating periodic crystals and organic small molecule data, yet they still encounter challenges in terms of interpretability and computational efficiency when applied to polymer monomers and organic macromolecules data. There is still a lack of graph representation of organic polymers and macromolecules specifically tailored for GNN models to explore the structural characteristics. The Polymer-unit Graph, a novel coarse-grained graph representation method introduced in study, is dedicated to expressing and analyzing polymers and macromolecules. By incorporating the Polymer-unit Graph into the GNN models and analyzing the organic semiconductor (OSC) materials database, it becomes possible to uncover intricate structure–property relationships involving branched-chain engineering, fluoridation substitution, and donor–acceptor combination effects on the elementary structure of OSC polymers. Furthermore, the Polymer-unit Graph enables visualizing the relationship between target properties and polymer units while reducing training time by an impressive 98% and minimizing molecular graph representation models. In conclusion, the Polymer-unit Graph successfully integrates the concept of Polymer-unit into the field of GNNs, enabling more accurate analysis and understanding of organic polymers and macromolecules.

The creep rupture data in this collection were determined for the high temperature reactor projects (the direct cycle helium turbine project, the nuclear process heat project and the district heating project) of the Federal Republic of Germany and the State of North Rhine Westphalia, the aim being to qualify the candidate constructional alloys for service at temperatures up to 950°C. Several batches of each alloy were investigated and special attention was given to the effects of the service environments (helium containing different impurity gases, such as hydrogen, water vapour, carbon monoxide and methane, all at microbar levels, and methane reforming gas mixture) on the properties. In the test machines, these test environments led to carburisation of test pieces. However, analysis of the data showed that the stress rupture data for tests in air, in impure helium and in methane reforming gas lay in the same scatterband at each temperatures tested.

The data were used for the formulation of draft nuclear design rules for the high temperature reactor systems, which are summarised in 'Proceedings of the Workshop on Structural Design Criteria for HTR', G Breitbach, F Schubert, H Nickel, Jül-Conf-71, April 1989, Berichte der Kernforschungsanlage Jülich GmbH, ISSN 0344-5798.