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.

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 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.

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.

This database contains cryogenic material property data.

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

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.

Mean values of chemical, mechanical properties and physical propertiesDiclor: extractives soluble in dichloromethane (%);Etan: extractives soluble in ethanol (%)H2O: extractives soluble in water (%)Total: total extractives (%)Klason: lignin content (%)H_G: lignin composition (%)cP_cH: pentose/hexose ratioMOR: radial modulus of rupture (MPa)MOE: radial modulus of elasticity (MPa)RD: average ring density (g cm-3)EWD: earlywood density (g cm-3)LWD: latewood density (g cm-3)LWP: latewood percentage (%)RW: ring width (mm)EWW: earlywood width (mm)LWW: latewood width (mm)

HI: heterogeneity index

The vastness of materials space, particularly that which is concerned with metal–organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data-mine published MOF papers to extract the materials informatics knowledge contained within journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the DigiMOF database. Using the CDE web scraping package alongside the Cambridge Structural Database (CSD) MOF subset, we automatically downloaded 43,281 unique MOF journal articles, extracted 15,501 unique MOF materials, and text-mined over 52,680 associated properties including the synthesis method, solvent, organic linker, metal precursor, and topology. Additionally, we developed an alternative data extraction technique to obtain and transform the chemical names assigned to each CSD entry in order to determine linker types for each structure in the CSD MOF subset. This data enabled us to match MOFs to a list of known linkers provided by Tokyo Chemical Industry UK Ltd. (TCI) and analyze the cost of these important chemicals. This centralized, structured database reveals the MOF synthetic data embedded within thousands of MOF publications and contains further topology, metal type, accessible surface area, largest cavity diameter, pore limiting diameter, open metal sites, and density calculations for all 3D MOFs in the CSD MOF subset. The DigiMOF database and associated software are publicly available for other researchers to rapidly search for MOFs with specific properties, conduct further analysis of alternative MOF production pathways, and create additional parsers to search for additional desirable properties.

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 mission of the High Throughput Experimental Materials Database (HTEM DB) is to enable discovery of new materials with useful properties by releasing large amounts of high-quality experimental data to public. The HTEM DB contains information about materials obtained from high-throughput experiments at the National Renewable Energy Laboratory (NREL).

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

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.

Yamdb (Yet Another Materials Database/YAMl materials DataBase) is a Python library providing thermophysical properties of liquid metals and molten salts in an easily accessible manner. Mathematical relations describing material properties - usually determined by experiment - are taken from the literature and implemented in Python. The coefficients of these equations are stored separately in YAML files.

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)}, ...}}}

A compilation of material property data on Green River (Piceance Basin) oil shale is presented. While this report is not intended to be a comprehensive review of the literature, it is intended to provide a baseline of data to support various thermomechanical modeling efforts in progress at Sandia National Laboratories. The data, presented in tabular form, are divided into three categories: elastic properties, failure properties and thermal properties. Within each category, the data are listed by kerogen content and test condition (confining pressure, temperature, etc.). Summaries of some of the important features of the elastic and failure properties of oil shale are presented in graphical form.

The Network for Evaluating Structural Components (NESC) coordinated extensive materials testing and fracture analyses performed by a group of twenty European organizations. The NESC-IV project addressed the transferability of fracture toughness data from laboratory specimens to applications that assess the integrity of reactor pressure vessels subjected to upset and normal loading transients. The coordinated experimental and analytical program drew from major elements of the biaxial features testing program conducted by the Heavy Section Steel Technology Program at the Oak Ridge National Laboratory. In this context, the JRC performed impact and tensile tests in the temperature range -90 to 80ºC on type A533B ferritic steel.

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).

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.