This repository has properties for different groups of material. The main idea is to provide accesible properties for comparison.

Materials Project is an open database of computed materials properties aiming to accelerate materials science research. The resources in this OpenData dataset contain the raw, parsed, and build data products.

The document contains the comprehensive ‘one-stop’ material properties database developed by the GEOLAB consortium for the typical soils and constitutive models used in the GEOLAB facilities. The said database was developed to support the use and re-use of the quality experimental data from the GEOLAB Transnational Access projects.

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

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 deliverable is a database of exfoliable three-dimensional (3D) layered materials available for 2D-PRINTABLE, and the corresponding two-dimensional (2D) materials produced by project partners by means of various exfoliation methods in liquid media, including liquid-phase exfoliation method (LPE), electrochemical exfoliation (EE) and chemical exfoliation (CE). Exfoliable 3D layered materials are those synthesized and currently available at VSCHT facilities, while LPE-produced 2D materials are those produced by BeD, UKa, TCD TUD and VSCHT. The database includes the main specifications for exfoliable 3D layered materials, including their (physical) form (e.g., powder/crystal and corresponding dimension), stoichiometry and doping, as well as the material amount that can be supplied within the consortium. For 2D materials, the database reports the references to public documents (e.g., paper in international peer-reviewed journal or public repositories) showing material characterizations.

This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101135196. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.

This dataset contains key characteristics about the data described in the Data Descriptor A database of battery materials auto-generated using ChemDataExtractor. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

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).

This database contains cryogenic material property data.

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 database has been developed in the context of the Horizon Europe project TRANSIENCE to support the development of MIC3, a consistent, fully open-source model ecosystem to assess industrial circularity, decarbonisation, and sustainability. The database contains P&S characteristics, specifications, material compositions, and supply chain-related information. It assesses material compositions (bulk materials) of various building types, passenger vehicles, batteries, wind turbines, solar PV systems, and electronic devices, using the data from the prospective life cycle assessment framework premise building on ecoinvent 3.10 (system model: 'allocation, cutoff by classification') and literature. Additional data on critical materials for a large set of low-carbon energy technologies has also been included. Moreover, important translations of socioeconomic indicators into physical demand are provided for selected products, end-uses, and energy services.

The NIST WWW High Temperature Superconductors database (WebHTS) provides evaluated thermal, mechanical, and superconducting property data for oxide superconductors. The range of materials covers the major series of compounds derived from the Y-Ba-Cu-O, Bi-Sr-Ca-Cu-O, Tl-Sr-Ca-Cu-O, and La-Cu-O chemical families, along with numerous other variants of the cuprate and bismuthate materials that are known to have superconducting phases. The materials are described by specification and characterization information that includes processing details and chemical compositions. Physical characteristics such as density and crystal structure are given in numeric tables. All measured values are evaluated and supported by descriptions of the measurement methods, procedures, and conditions. In all cases, the sources of the data are fully documented in a comprehensive bibliography.

This repository contains two datasets for our recent work "Learning Properties of Ordered and Disordered Materials from Multi-fidelity Data". The first data set is a multi-fidelity band gap data for crystals, and the second data set is the molecular energy data set for molecules.1. Multi-fidelity band gap data for crystalsThe full band gap data used in the paper is located at band_gap_no_structs.gz. Users can use the following code to extract it. import gzipimport jsonwith gzip.open("band_gap_no_structs.gz", "rb") as f: data = json.loads(f.read())data is a dictionary with the following format{"pbe": {mp_id: PBE band gap, mp_id: PBE band gap, ...},"hse": {mp_id: HSE band gap, mp_id: HSE band gap, ...},"gllb-sc": {mp_id: GLLB-SC band gap, mp_id: GLLB-SC band gap, ...},"scan": {mp_id: SCAN band gap, mp_id: SCAN band gap, ...},"ordered_exp": {icsd_id: Exp band gap, icsd_id: Exp band gap, ...},"disordered_exp": {icsd_id: Exp band gap, icsd_id: Exp band gap, ...}}where mp_id is the Materials Project materials ID for the material, and icsd_id is the ICSD materials ID. For example, the PBE band gap of NaCl (mp-22862, band gap 5.003 eV) can be accessed by data['pbe']['mp-22862']. Note that the Materials Project database is evolving with time and it is possible that certain ID is removed in latest release and there may also be some band gap value change for the same material. To get the structure that corresponds to the specific material id in Materials Project, users can use the pymatgen REST API. 1.1. Register at Materials Project https://www.materialsproject.org and get an API key.1.2. In python, do the following to get the corresponding computational structure. from pymatgen import MPRester mpr = MPRester(#Your API Key) structure = mpr.get_structure_by_material_id(#mp_id)A dump of all the material ids and structures for 2019.04.01 MP version is provided here: https://ndownloader.figshare.com/files/15108200. Users can download the file and extract the material_id and structure from this file for all materials. The structure in this case is a cif file. Users can use again pymatgen to read the cif string and get the structure. from pymatgen.core import Structurestructure = Structure.from_str(#cif_string, fmt='cif')For the ICSD structures, the users are required to have commercial ICSD access. Hence the structures will not be provided here.2. Multi-fidelity molecular energy dataThe molecule_data.zip contains two datasets in json format. 2.1 G4MP2.json contains two fidelity G4MP2 (6095) and B3LYP (130831) calculations results on QM9 molecules {"G4MP2": {"U0": {ID: G4MP2 energy (eV), ...}, { "molecules": {ID: Pymatgen molecule dict, ...}},"B3LYP": {"U0": {ID: B3LYP energy (eV), ...} {"molecules": {ID: Pymatgen molecule dict, ...}}}2.2 qm7b.json contains the molecule energy calculation resultsi for 7211 molecules using HF, MP2 and CCSD(T) methods with 6-31g, sto-3g and cc-pvdz bases. {"molecules": {ID: Pymatgen molecule dict, ...},"targets": {ID: {"HF": {"sto3g": Atomization energy (kcal/mol), "631g": Atomization energy (kcal/mol), "cc-pvdz": Atomization energy (kcal/mol)}, "MP2": {"sto3g": Atomization energy (kcal/mol), "631g": Atomization energy (kcal/mol), "cc-pvdz": Atomization energy (kcal/mol)}, "CCSD(T)": {"sto3g": Atomization energy (kcal/mol), "631g": Atomization energy (kcal/mol), "cc-pvdz": Atomization energy (kcal/mol)}, ...}}}

The Tissue Properties database provides comprehensive estimates for tissue material parameter values and also statistical information about the spread and standard deviation per tissue for the different thermal and fluidic parameters. This information is important for assessment of the contribution to the uncertainty in a quantity of interest due to the selection of the material parameters in simulations. For some material parameters, e.g., perfusion, the variation can be large, which in turn can have a severe effect on simulation results.

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.

Source/split

wiki Data from wikipedia and wikidata CC-BY-SA license

Bradley Data from Jean-Claude Bradley Open Melting Point Dataset

RadonPy Simulated data from RadonPy BSD 3-Clause License

This comprehensive dataset is specifically designed for the exploration of mechanical properties and electrical conductivity in Cu-Ni-Si alloys, offering detailed insights into chemical compositions, thermo-mechanical processing variables, and their impacts on alloy properties. The collection provides an extensive foundation for understanding and analyzing how various factors influence the performance and characteristics of Cu-Ni-Si alloys.

The dataset was curated to facilitate the development and validation of a predictive Hybrid Deep Learning (DL) and Ensemble Learning (EL) model that aims to fill the research gaps in the current understanding of Cu-Ni-Si alloys. It includes data on alloy compositions, processing conditions, and the resultant electrical and mechanical characteristics. The unique combination of DL and EL techniques provides a robust framework for predicting alloy behavior, which is demonstrated through superior predictive performance, showcased by near-perfect R2 values for both training and test datasets.

Moreover, for those looking to incorporate machine learning techniques into materials science, this dataset provides a unique opportunity to delve into the complex interplay between alloy composition, processing, and resultant properties. By offering a granular look at these relationships, the dataset opens up new avenues for innovation and research in material science and engineering.

The file "Cu-Ni-Si-alloys.xlsx" contains a detailed dataset on various properties of copper-nickel-silicon (Cu-Ni-Si) alloys. It includes columns for the composition of these alloys in terms of percentages of copper (Cu), aluminum (Al), cobalt (Co), chromium (Cr), magnesium (Mg), nickel (Ni), silicon (Si), tin (Sn), and zinc (Zn). Additionally, it provides data on their solid solution strengthening temperature (Tss in K), aging temperature and time, as well as their mechanical and electrical properties such as hardness (HV), yield strength (MPa), ultimate tensile strength (MPa), and electrical conductivity (%IACS). Each entry also includes a DOI link to its source and references for further reading.

The dataset presented herein is extracted from the comprehensive collection of data on the mechanical properties and electrical conductivity of copper-based alloys curated by Gorsse, Stephane; Gouné, Mohamed; LIN, Wei-Chih; Girard, Lionel (2023) titled "Dataset of mechanical properties and electrical conductivity of copper-based alloys" available on figshare (Collection, DOI: https://doi.org/10.6084/m9.figshare.c.6475600.v1).

Open Quantum Materials Database (OQMD) is a database of DFT calculated thermodynamic and structural properties materials, created in Chris Wolverton's group at Northwestern University

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.

Dataset accompanying the publication 'M. Cermak, N. Perez, and M. Bahrami, 'Material properties and structure of natural graphite sheet', Science and Technology of Advanced Materials, 2020'.

The dataset contains the raw data related to the measurements of material properties, implementation of data processing in Matlab and Microsoft Excel, and microscope images of the material structure.