The NIST Alloy data web application (https://trc.nist.gov/metals_data) provides access to thermophysical property data with a focus on unary, binary, and ternary metal systems.

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.

Researchers continue to explore and develop aluminum alloys with new compositions and improved performance characteristics. An understanding of the current design space can help accelerate the discovery of new alloys. We present two datasets: 1) chemical composition, and 2) mechanical properties for predominantly wrought aluminum alloys. The first dataset contains 14,884 entries on aluminum alloy compositions extracted from academic literature and US patents using text processing techniques, including 550 wrought aluminum alloys which are already registered with the Aluminum Association. The second dataset contains 1,278 entries on mechanical properties for aluminum alloys, where each entry is associated with a particular wrought series designation, extracted from tables in academic literature.

The past few decades have witnessed rapid progresses in the research and development of complex metallic alloys such as metallic glasses and multi-principal element alloys, which offer new solutions to tackle engineering problems of materials such as the strength-toughness conflict and deployment in harsh environments and/or for long-term service. A fatigue database (FatigueData-CMA2022) is compiled from the literature by the end of 2022. Data for both metallic glasses and multi-principal element alloys are included and analyzed for their statistics and patterns. Automatic extraction and manual examination are combined in the workflow to improve the efficiency of processing, the quality of published data, and the reusability. The database contains 272 fatigue datasets of S-N (the stress-life relation), ε-N (the strain-life relation), and da/dN-ΔK (the relation between the fatigue crack growth rate and the stress intensity factor range) data, together with the information of materials, processing and testing conditions, and mechanical properties. The database and scripts are released in open repositories, which are designed in formats that can be continuously expanded and updated.

Article DOI: 10.1038/s41597-023-02354-1

The Matbench test suite v0.1 contains 13 supervised ML tasks from 10 datasets. Matbench’s data are sourced from various subdisciplines of materials science, such as experimental mechanical properties (alloy strength), computed elastic properties, computed and experimental electronic properties, optical and phonon properties, and thermodynamic stabilities for crystals, 2D materials, and disordered metals. The number of samples in each task ranges from 312 to 132,752, representing both relatively scarce experimental materials properties and comparatively abundant properties such as DFT-GGA formation energies. Each task is a self-contained dataset containing a single material primitive as input (either composition or composition plus crystal structure) and target property as output for each sample.

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.

We present a dataset of structural relaxations of bulk ternary transition metal dichalcogenides (TMDs) computed via plane-wave density functional theory (DFT). We examined combinations of up to two chalcogenides with seven transition metals from groups 4-6 in octahedral (1T) or trigonal prismatic (2H) coordination. The full dataset consists of 672 unique stoichiometries, with a total of 50,337 individual configurations generated during structural relaxation. Our motivations for building this dataset are (1) to develop a training set for the generation of machine and deep learning models and (2) to obtain structural minima over a range of stoichiometries to support future electronic analyses. We provide the dataset as individual VASP xml files and in the ASE database format.

We achieved to create a database of electronic structure properties of bulk materials (metals, metal oxides, carbon) with a good relative accuracy compared to experimental values and minimal computational cost using density functional theory and semi-empirical quantum mechanical methods.

Mechanical properties on Metal Additive manufacturing

Abstract

This dataset contains the results on the experimental procedure to gather data on the manufacturing of Additive Manufacturing (AM) pieces for Laser Metal Deposition (LMD) processes.To create this dataset, a set of 37 pieces were manufactured using the same material and procedure but different manufacturing parameters and strategies. The process was monitored and recorded, capturing thermal images and sensor readings on each point of the manufacturing. Each one of the pieces was divided in 4 coupons, that were tested to obtain mechanical properties of resistance and hardness.

Contents

This dataset contains data related to 37 coupons (named T1 - T37), including the monitoring data gathered during manufacturing, the process parameters, results of tensile testing, results of hardness testing and pictures of the coupons.

The dataset is structured as follows:

[Piece ID]

Hdf5 file

Piece picture

Piece crosssection

Testing

Parameters.csv: tabular data with process parameters and piece measurements as a direct result of the manufacturing

Coupons.csv: tabular data with tensile and hardness metrics on the tested coupons for each piece

README: explicative document

Formats

The data is provided in the following formats:

Monitoring: HDF5 file format. This format allows the recording of multimodal manufacturing data.

Pictures: jpg format

Parameters: csv file containing process parameters

Coupons: csv file with the material testing results.

More information on the contents and formats of the dataset is contained in the attached README document.

Acknowledgments

This work has been funded under the "Red de Excelencia en Fabricación Aditiva" (READI) with the support of the Spanish Ministry of Science and Innovation and the Centre for the Development of Industrial Technology (CDTI), under the program Cervera (CER-20191020).

This dataset is a continuously growing collection of lead isotope reference data. Lead isotopes are an established method to reconstruct the raw material provenance of archaeological objects. They are typically applied to artefacts made of copper, lead, silver, and their alloys. However, also the raw material provenance of other materials such as glass, pigments and pottery was already reconstructed with lead isotopes. To successfully reconstruct the origin of the raw material, lead isotope signatures from as many as possible suitable raw material occurrences must be known. In the past, large-scaled research projects were carried out to characterise ore deposits especially in the Mediterranean area and Western Europe. However, many of these data are dispersed in the literature and were published in scientific articles or monographies. Consequently, each researcher or at least each research group had to build their own up-to-date data base of reference data from the literature. To overcome these restrictions, to facilitate work with lead isotope reference data and particularly to make the data FAIR, i.e. findable, accessible, interoperable and reusable (Wilkinson et al., 2016), these published data are compiled and transferred into a uniform layout. They are further enhanced with additional metadata to facilitate their use in raw material provenance studies. Currently, the database is restricted to ores and minerals as these are the most relevant materials for provenance studies of ancient metals. Future updates will include hitherto uncovered regions but also additional data from countries already present. Slag and other metallurgical (by-) products from ancient sites in close vicinity to ore deposits generally are a genuine representation of the ores utilised in historic times. As such, they are highly relevant for provenance studies and an extension to these materials is therefore planned. GlobaLID is a representation of the collective work of researchers on Pb isotope studies. As such, the database is seen as a community engagement project that invites scientists all over the world to become active contributors of GlobaLID. The initiators of the database dedicate their effort to the continuation and maintenance of the database but only the support of the whole community will allow a rapid and successful growth of GlobaLID.

The deposit contains data for the paper "From crystal phase mixture to pure metal-organic frameworks – Tuning pore and structure properties" including BET, CIF files, cRED, FTIR, Raman, QEXAFSm SXRD, XRD, SEM, TEM.

The AiiDA archives of the high-throughput calculations presented in the paper “Tuning Structural and Electronic Properties of Metal-Organic Framework 5 by Metal Substitution and Linker Functionalization”

The files “*_MOF_workflows.aiida” contains the actual calculation data for metal nodes Be, Ba, Ca, Cd, Mg, Sr, and Zn and the files with suffix “*.yaml” contain configuration files of the workflows. Additional data on the elemental phases can be retrieved https://zenodo.org/records/10082633.

Lean duplex stainless steel (LDSS), which is expected to apply to infrastructures, exhibits rounded shape of stress-strain curve. For this reason, a constitutive equation which is able to accurately express the curve is required for the ultimate strength analysis of LDSS structures. Authours have already proposed MRO curve as this kind of equation. However, not only 0.2% proof stress and tensile strength, which are specified in common material standard and a mill certificate, but also mechanical properties such as proportion limit etc are needed to describe the equation. In this study, we collected tension coupon test results of LDSS and created the simple estimated equation by means of linear regression analysis. Also, we predicted the me- chanical properties by using Random Forest (RF) which is one of machine learning method. According to comparison predicted results by RF with those by estimated equation, it was revealed that RF has same prediction accuracy of mechanical properties as estimation equation.

This file includes data used in the calibration of statistical models for the different random variables affecting stainless steel members. This data was used in the analysis carried out and reported in the publication: "Statistical data of material, geometrical and imperfection characteristics of structural stainless steels and members"

Measured and nominal values are listed for geometry-related data.

Measured values are listed for material, imperfection and residual stress data.

Metal Sputtering Target Market Outlook

The global metal sputtering target market size is poised to witness significant expansion, growing from a market valuation of $4.2 billion in 2023 to approximately $6.8 billion by 2032, reflecting a compound annual growth rate (CAGR) of 5.2%. The market is driven by the increasing demand for advanced electronic devices and renewable energy systems, coupled with technological advancements in the semiconductor and display industries.

One of the primary growth factors in the metal sputtering target market is the rapid advancement of the semiconductor industry. Semiconductors are integral to a vast array of electronic devices, including smartphones, laptops, and wearables. The continuous innovation and miniaturization of these devices necessitate high-quality metal sputtering targets for the precise deposition of thin films. Furthermore, the proliferation of IoT devices and the advent of 5G technology are expected to substantially boost semiconductor production, thereby driving the demand for sputtering targets.

Additionally, the growing adoption of solar energy systems is significantly contributing to the market's growth. Metal sputtering targets are crucial in the manufacturing of photovoltaic cells, which are the building blocks of solar panels. As governments worldwide push for renewable energy adoption to combat climate change, the demand for efficient and cost-effective solar panels is on the rise. This trend is likely to propel the need for metal sputtering targets in the coming years, as manufacturers strive to enhance the efficiency and durability of solar cells.

Another vital growth factor is the burgeoning data storage industry. With the exponential growth of data generated by various digital platforms and the increasing need for secure storage solutions, there is a rising demand for high-capacity storage devices. Metal sputtering targets are essential in the production of hard disk drives (HDDs) and solid-state drives (SSDs), wherein they facilitate the creation of thin magnetic films necessary for data storage. The transition to cloud computing and big data analytics further amplifies the need for advanced data storage solutions, thus driving the market for sputtering targets.

From a regional perspective, Asia Pacific is anticipated to dominate the metal sputtering target market, primarily due to its robust electronics manufacturing sector. Countries like China, Japan, and South Korea are home to major electronics and semiconductor companies, which are significant consumers of sputtering targets. Furthermore, government initiatives to promote renewable energy and advancements in automotive technology within this region are also expected to bolster market growth. North America and Europe are also poised to experience substantial growth due to their strong presence in the aerospace and energy sectors.

Material Type Analysis

The metal sputtering target market by material type is segmented into pure metals, alloys, and compounds. Pure metals such as aluminum, copper, and titanium are widely used due to their excellent electrical and thermal conductivity. These metals are critical in the deposition processes for semiconductors and display panels. The demand for pure metal targets is expected to rise with the continued growth of the electronics industry, as they provide the necessary properties for high-performance electronic components.

Alloys, which are combinations of two or more metals, offer enhanced properties such as improved strength, corrosion resistance, and specific electrical characteristics. This segment is particularly significant in applications where pure metals cannot meet the desired specifications. For instance, in the aerospace industry, metal alloys are preferred for their superior mechanical properties and lightweight nature. The increasing demand for advanced materials in aerospace applications is likely to drive the market for alloy sputtering targets.

Compounds, including oxides, nitrides, and carbides, are used in various high-tech applications due to their unique chemical and physical properties. These materials are essential in the production of thin films for electronic devices, photovoltaics, and protective coatings. The growing need for advanced materials with specific functionalities in cutting-edge technologies is expected to propel the demand for compound sputtering targets. Innovations in material science and the development of new compound materials will further drive this market segment.

The choice

CoRE MOF Atomic Coordinates & Properties

High Throughput Screening of Metal-Organic Frameworks

By [source]

About this dataset

This dataset contains atomic coordinates and properties for metal-organic frameworks (MOFs) from the CoRE MOF 2019 Edition. With over 350 data columns, this is one of the largest and most comprehensive MOF datasets available today. This data can be used to evaluate existing structures, design new structures and to gain insight into the structural characteristics that lead to desired properties.

The columns include the filename of each MOF, along with its largest cavity diameter (LCD), pore limiting diameter (PLD), largest free pore diameter (LFPD), volume per gram(cm3_g), accessible surface area per cm3 (ASA_m2_cm3) & gram (ASA_m2_g) & non-accessible surface area per cm3(NASA_m2_cm3) & gram(NASA_m2_g). Also included is the accessible volume fraction(AVVF), accessible volume per gram UAV/g, non-accessible volume/gram NAV/g as well as other properties such as number of metal atoms, open metal sites and disorder state.

Other parameters present in this dataset include file extension details like FSR overlap fractions with respect to Cambridge crystallographic Data Centre & Core database along with details like presence or absence of open metal sites in each sheet structure. Additionally there are paper DOI's pertaining to public availability date date added on CSD ,matched CoRE numbers list possible CoRe matches etc., making this one of most valuable datasets currently available on Metal Organic Frameworks screening for research community around world for material designing studies

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How to use the dataset

This dataset contains atomic coordinates and properties of metal-organic frameworks from the CoRE MOF 2019 Edition. The dataset includes information on filename, LCD, PLD, LFPD, cm3_g, ASA_m2_cm3, ASA_m2_g NASA_m2_cm3 NASA_m2-g AVVF AV-cm3-g NAV cm3 g All Metals Has OMS Open Metal Sites Extension FSR overlap from CSD public disordered CSD overlap in CoRE CSD of WoS in CoRE CSO overlap In ccdc data csd DOI public note Matched CSD of CORe Possible List Csd Of Corre.

This dataset is a useful resource for researchers working on high throughput screening studies involving metal organic frameworks (MOFS). With this data you can investigate different types of MOFs to determine their atomic coordinates and properties. You can compare the different parameters contained in the datasets such as LCD, PLD, LFPD ,etc. as well as find out which have similar chemical composition or layout to create better understanding of potential synthesis strategies and design improved materials with better performances.

To use this dataset first download it onto your system and open it with Excel or any other spreadsheet program that supports CSV files (comma separated value). The columns will provide information about each MOF's structure including filename LCD PLD LFPDnbspcme gnbsp ASMQGTncmnbspASAQGTngnbspNASAMQTncmnbspNASAMQTgnbs pAVVFNBSPAVCMGTgnbspnavicmgtnbspallmetalshasomsopen metal sitesextensionFSRoverlapfromCSdPublicDISOrderedCSDo verlappingcorrecsdofWosinCORECSOverlappingccdcDATAcsdDOIpublicNOTEmatchedC SDCoredPossibleLISTcsdOfCoRe .

Once you have loaded up the file review the column headers closely to gain an understanding o what kind o metrics are present such s ISIS RGB values colorspace dimensions large freedompor diameters etc You will also want to be cognizant o overlapping parameter between simulations fram position discrepancies across runs com mon chemical elements etc So take some time here t determine hw best t interpret these terms as they wil ultimately influence how we interpret our findings later n down th line

Finally when all is said n done analyse yur dat by accurately

Research Ideas

• Developing a machine-learning algorithm to predict the best MOF for specific applications based on its properties (e.g. pore size, surface area, porosity).
• Screening the dataset for MOFs with high open metal sites and all-metal content, in order to find potential materials for hydrogen storage applications or renewable energy storage solutions
• Comparing overlapping MOFs from different databases (i.e., CoRE and CCDC) to identify which ones present similar structural characteristics that could potentially be useful for new syntheses and/or optimizations of existing structures of interest

Acknowledgements

If you use this dataset in your research, please credit the original authors. [Data Source](https://zenodo.org/record/3...

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Biological Magnetic Resonance Bank Entry 16757: Location and properties of metal-binding sites on the human prion protein

Raw data for the paper: Monitoring alterations in a salt layer’s deliquescence properties during the atmospheric corrosion of a metal surface using a quartz crystal microbalance

electronic supplementary material