This database contains cryogenic material property data.

We utilized a dataset of Machine Design materials, which includes information on their mechanical properties. The dataset was obtained from the Autodesk Material Library and comprises 15 columns, also referred to as features/attributes. This dataset is a real-world dataset, and it does not contain any random values. However, due to missing values, we only utilized seven of these columns for our ML model. You can access the related GitHub Repository here: https://github.com/purushottamnawale/material-selection-using-machine-learning

To develop a ML model, we employed several Python libraries, including NumPy, pandas, scikit-learn, and graphviz, in addition to other technologies such as Weka, MS Excel, VS Code, Kaggle, Jupyter Notebook, and GitHub. We employed Weka software to swiftly visualize the data and comprehend the relationships between the features, without requiring any programming expertise.

My Problem statement is Material Selection for EV Chassis. So, if you have any specific ideas, be sure to implement them and add the codes on Kaggle.

A Detailed Research Paper is available on https://iopscience.iop.org/article/10.1088/1742-6596/2601/1/012014

An electronic database such as Scopus using the keywords and literature searched from 1970 to 2021. Four thousand five hundred articles were screen and refined the existing/related article through different filters as per exclusion.

The NIST Chemistry WebBook provides users with easy access to chemical and physical property data for chemical species through the internet. The data provided in the site are from collections maintained by the NIST Standard Reference Data Program and outside contributors. Data in the WebBook system are organized by chemical species. The WebBook system allows users to search for chemical species by various means. Once the desired species has been identified, the system will display data for the species. Data include thermochemical properties of species and reactions, thermophysical properties of species, and optical, electronic and mass spectra.

This repository contains the code and data for the project "Beyond Training Data How Elemental Features Enhance ML-Based Formation Energy Predictions".

MADB is a bibliographic database on physico-chemical properties of selected Minor Actinide compounds and alloys. The materials and properties are selected based on their importance in the advanced nuclear fuel cycle options. This list is updated up to 2008.

Validation dataset

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.

These are material properties provided by the vendor.

Zones in this data set represent spatially contiguous areas that influence ground-water flow in the Death Valley regional ground-water flow system (DVRFS), an approximately 45,000 square-kilometer region of southern Nevada and California. The zones typically represent areas of differing material properties; however, they may also represent spatially similar areas, such as areas of similar infiltration rates. Material properties, such as horizontal hydraulic conductivity, vertical anisotropy, or storage characteristics may vary within a single hydrogeologic unit and be represented by numerous zones; or they may be the same for multiple hydrogeologic units and be represented by a single zone. The DVRFS zones were typically derived from geological or hydrological analyses by Sweetkind and others (2004) and Faunt and others (2004) and were used as input in the DVRFS transient ground-water flow model, a regional-scale model developed by the U.S. Geological Survey (USGS) for the U.S. Department of Energy (DOE) to support investigations at the Nevada Test Site (NTS) and at Yucca Mountain, Nevada (see "Larger Work Citation", Chapter A, page 8, for details).

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.

It contains the Indoor ASCII Database file for the smart home, for use in WinProp. This file is machine readable as a “.txt” file, without the use of proprietary software. It contains the RF properties of the building materials used, including those as 40 GHz, for which there is currently limited information publicly available. In addition, it also details the exact layout of the building and distribution of materials.

The dataset includes information regarding the mechanical property of magnesium (Mg) alloys. The dataset has been processed and used for machine learning (ML) operations. This database includes alloy names/compositions (individual elemental makeup in weight percentage), processing, yield strength, ultimate tensile strength, and ductility.

Property Data Summaries are collections of property values derived from surveys of published data. These collections typically focus on either one material or one particular property. Studies of specific materials typically include thermal, mechanical, structural, and chemical properties, while studies of particular properties survey one property across many materials. The property values may be typical, evaluated, or validated. Values described as typical are derived from values for nominally similar materials.

This dataset contains data of over 2,600 alloys, including their chemical composition (%wt) across 30 elements, along with two key physical properties: tensile strength and melting point.

This Data can be easily used to train the ML and Deep Learning models on Alloys property.

The datasets consist of principal fact information of gravity data and density and magnetic properties of hand samples in the Chico and Willows 1:100,000-scale quadrangles, California.

This is a companion repository for a paper published in Scientific Data with the title and authors given above, whose abstract is below:There has been an ongoing need for information-rich databases in the mechanical-engineering domain to aid in data-driven materials science. To address the lack of suitable property databases, this study employs the latest version of the chemistry-aware natural-language-processing (NLP) toolkit, ChemDataExtractor, to automatically curate a comprehensive materials database of key stress-strain properties. The database contains information about materials and their cognate properties: ultimate tensile strength, yield strength, fracture strength, Young’s modulus, and ductility values. 720,308 data records were extracted from the scientific literature and organized into machine-readable databases formats. The extracted data have an overall precision, recall and F-score of 82.03%, 92.13% and 86.79%, respectively. The resulting database has been made publicly available, aiming to facilitate data-driven research and accelerate advancements within the mechanical-engineering domain.

This is a condensed version of HTEM database downloaded via HTEM API from National Renewable Energy Laboratory. Due to network constraints, all entries without XRD entries are discarded.

Dataset Overview

The index file contains experiment conditions of 1400+ experiments performed by the high-throughput experiment platform in NREL. Each experiments contains 44 samples, whose associated data are stored in the samples folder. The 44 samples in each experiment all have different thin film thickness and composition. Depending on the experiment setup, the sample data files may contain data from X-ray Fluorescence (thin film composition), X-ray Diffraction (crystalline structure), electronic measurement (thin film conductivity), and optical spectra (light absorption).

This dataset provides a complete record of experimental condition, structural characterization, and properties measurement, making it a valuable resource for data-mining for a better understanding of complex process-structure-property relationships in thin film materials.

Please cite: A. Zakutayev, N. Wunder, M. Schwarting, J. D. Perkins, R. White, K. Munch, W. Tumas and C. Phillips, Sci Data 5, 180053 (2018).

Predicting the final properties of new materials (composite materials). Composite material is a multicomponent material made from two or more components with significantly different physical and/or chemical properties that, when combined, result in a new material with characteristics that are different from those of the individual components and are not a simple superposition of them. It is customary to distinguish a matrix and fillers in the composition of a composite, the latter performing the function of reinforcement (by analogy with reinforcement in a composite building material such as reinforced concrete). The fillers of composites are usually carbon or glass fibers, and the role of the matrix is played by the polymer. The combination of different components improves the characteristics of the material and makes it both light and durable. At the same time, the individual components remain as such in the structure of the composites, which distinguishes them from mixtures and hardened solutions. By varying the composition of the matrix and filler, their ratio, and filler orientation, a wide range of materials with the required set of properties is obtained. Many composites are superior to traditional materials and alloys in their mechanical properties and at the same time they are lighter. The use of composites usually makes it possible to reduce the weight of a structure while maintaining or improving its mechanical characteristics. Modern composites are made from different materials: polymers, ceramics, glass and carbon fibers, but the basic principle remains the same. This approach also has a drawback: even if we know the characteristics of the original components, determining the characteristics of the composite consisting of these components is quite problematic. There are two ways to solve this problem: physical testing of material samples, and the second is predicting characteristics. The essence of forecasting is to simulate a representative element of the volume of the composite, based on data on the characteristics of the incoming components (binder and reinforcing component). Therefore, the relevance of the chosen topic is due to the fact that the created predictive models will help reduce the number of tests performed, as well as replenish the materials database with possible new characteristics of materials, and digital twins of new ones. In addition, an adequately functioning prediction model can significantly reduce the time, financial and other costs of testing. Therefore, it is necessary to develop models that predict tensile modulus and tensile strength, as well as a model that recommends the matrix-filler ratio.

The relevance lies in the fact that the created predictive models will help reduce the number of tests performed, as well as replenish the materials database with possible new characteristics of materials, as well as digital twins of new composites. Initial data on the properties of composite materials, presented in two data sets X_bp and X_nup.

The X_bp data set contains:

Matrix-filler ratio. Density, kg/m3. Modulus of elasticity, GPa. Amount of hardener, m.%. Content of epoxy groups,%_2). Flash point, C_2. Surface density, g/m2 Tensile modulus of elasticity, GPa Tensile strength, MPa Resin consumption, g/m2

The X_nup dataset contains:

Patch angle, degrees. Patch pitch. Patch density.

There are available hygrothermal simulation tools that allow to model possible scenarios for optimization of thermal transmittance of historic wall, creating complex insulation systems. The accuracy of the results depends on the conformity of the input data to the specific design of existing wall. The properties of materials selected in the simulation tool should reflect as close as possible the properties of the wall under investigation. For theses tools to be widely applicable the material library has to include various materials from various regions, thus allowing architects, planners, real estate developers and homeowners of Latvia to use the simulation tool with greater reliance on the accuracy of the simulation results. In order to obtain a result in mathematical modelling software simulating the humidity transfer processes, which would reflect the situation as close to the real conditions as possible, the database built into the modelling software should be supplemented with materials specific to the Latvian construction periods and obtained in different locations. Dataset Contains hygrothermal parameters of 40 different historic brick samples from Latvia (Density, Porosity, Water vapor resistance factor, etc.). Standard measurement methods with some adjustments were used to determine hygrothermal parameters of brick samples. The material properties were determined for a specific reason, to be used as a input data for creation of hygrothermal simulation tool material file, in this case DELPHIN simulation tool. Therefore, some deviations from the testing standards were implemented. These deviation were developed by Dresden University of Technology (also the developers of DELPHIN simulation tool) and are described in file "description.docx".

This work has been supported by the European Social Fund within the Project No 8.2.2.0/20/I/008 “Strengthening of PhD students and academic personnel of Riga Technical University and BA School of Business and Finance in the strategic fields of specialization” of the Specific Objective 8.2.2 “To Strengthen Academic Staff of Higher Education Institutions in Strategic Specialization Areas” of the Operational Programme “Growth and Employment”.