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 dataset contains information on the composition and processing conditions of aluminium alloys. The mechanical properties included are yield strength, tensile strength, and elongation. Additionally, the dataset provides information about the class to which each alloy belongs.

ULTERA database, developed under the ARPA-E's ULTIMATE program, is aimed at collecting literature data on high entropy alloys (HEAs) to facilitate rapid ML-based discovery of new ones using forward and inverse design.

The main scope of this dataset is collecting data on compositionally complex alloys (CCAs), also known as high entropy alloys (HEAs) and multi-principle-element alloys (MPEAs), with extra attention given to (1) high-temperature (refractory) mechanical data, (2) phases present under different processing conditions. Although low-entropy alloys (incl. binaries) are typically not presented to the end-user (or counted in statistics), some are present and used in ML efforts; thus, all high-quality alloy data contributions are welcome! You can set up a contribution in as little as few minutes with this contribution repository at contribute.ultera.org

As of July 2023, ULTERA contains over:

6,830 property-datapoints, corresponding to

2,850 unique HEAs, collected from

536 unique DOIs.

All data is available through a high-performance API, following FAIR principles, while statistics on it can be found at our ultera.org project web page. The database architecture is designed to automatically integrate starting literature data in real time with methods such as experiments, generative modeling, predictive modeling, and validations.

Beyond large size, ULTERA has further advantage of being highly curated with many steps of data validation and then processed through our abnormal data detection tools (pyqalloy.ultera.org).

Data overview .............

alloy_spreadsheet_v3.1.csv A comma delimited csv file containing the processed superalloy database. The spreadsheet was originally compiled in Google Sheets, with pre-processing carried out using the Google Apps Script. Each row corresponds to a unique alloy, with uniqueness determined by both composition and heat treatments. Each row (alloy) has been assigned a unique tag in the 1st column. Note that constituent alloy phases have their own rows---this is premised on the assumption that the constituent phases are themselves valid equilibrium alloys.

alloy_spreadsheet_v3.1_input.csv Similar to alloy_spreadsheet_v3.1.csv, this spreadsheet contains the unprocessed data.

alloy_images Contains .png files of alloy microstructure (principally SEM or TEM micrographs). The files are labelled systematically and each name corresponds to a relevant data entry in alloy_spreadsheet_v3.1.csv.

Methodological information .......................... The databse has been compiled from a mixture of published scientific articles and commercial or industry datasets. Each data source is cited in the dataset, including a DOI were appropriate. Some data was obtained from plots or figures using the WebPlotDigitizer tool.

Data-specific information ......................... Units used in the data are specified in the dataset's header. A dash "-" indicates that a certain element or heat treatment was not used in that particular alloy. An empty entry (cell) indicates that data was missing or simply not collected in the original data source.

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

Fatigue is a process of mechanical degradation that is usually assessed based on empirical rules and experimental data obtained from standardized tests. Fatigue data of engineering materials are commonly reported in S-N (the stress-life relation), e-N (the strain-life relation), and da/dN- ΔK (the relation between the fatigue crack growth rate and the stress intensity factor range) data. Fatigue and static mechanical properties of additively manufactured (AM) alloys, as well as the types of materials, parameters of AM, processing, and tests are collected from thousands of scientific articles till the end of 2022 using natural language processing, machine learning, and computer vision techniques. The results show that the performance of AM alloys could reach that of conventional alloys although data dispersion and system deviation are present. The database (FatigueData-AM2022) is formatted in compact structures, hosted in an open repository, and analyzed to show their patterns and statistics. The quality of data collected from the literature is measured by defining rating scores for datasets reported in individual studies and through the fill rates of data entries across all the datasets. The database also serves as a high-quality training set for data processing using machine learning models. Data extraction and analysis procedures are outlined and the tools are publicly released. A unified language of fatigue data is suggested to regulate data reporting for the fatigue performance of materials to facilitate data sharing and the development of open science.

Article DOI: 10.1038/s41597-023-02150-x

China Industrial Production: Aluminum Alloy data was reported at 1,655.000 Ton th in Mar 2025. This records an increase from the previous number of 1,552.000 Ton th for Dec 2024. China Industrial Production: Aluminum Alloy data is updated monthly, averaging 350.000 Ton th from Jul 2000 (Median) to Mar 2025, with 275 observations. The data reached an all-time high of 1,655.000 Ton th in Mar 2025 and a record low of 20.000 Ton th in Jan 2001. China Industrial Production: Aluminum Alloy data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BA: Industrial Production.

The thermodynamic calculations with the database enables a well reproduction of experimental findings and a physical-metallurgical understanding of the microstructure formation in solidification and annealing. It is helpful in designing chemical compositions and microstructure of novel Mg-Sr alloy for biomedical applications.

OverviewThis dataset, contained within Database.csv, is a comprehensive collection tailored for machine learning applications in the field of catalysis and materials science, focusing on single-atom alloys. It encompasses a wide array of data with 10,950 entries, each featuring 85 intrinsic descriptors alongside novel information on the predicted C-H dissociation energy barriers and reaction rates. These intrinsic descriptors include a variety of element and surface properties extracted from renowned databases like the Materials Project and Pymatgen, as well as surface structural features and characteristics derived through expert knowledge.Intrinsic DescriptorsThe 85 intrinsic descriptors provided in this dataset offer a detailed insight into the properties of single-atom alloys. These descriptors cover:Element Properties: Extracted from the Materials Project and Pymatgen databases, these properties include atomic size, electronegativity, and other elemental characteristics critical for the study of material properties.Surface Properties: Features related to the surface characteristics of the alloys, which play a significant role in their catalytic behavior and interaction with reactants.Surface Structural Features: Detailed information on the structural aspects of the alloy surfaces, which can influence the material's catalytic activity and stability.Expert-Derived Features: A set of features developed through expert knowledge, combining various data points to form comprehensive descriptors for machine learning applications.Predicted PropertiesC-H Dissociation Energy Barrier: A key metric for evaluating the catalytic efficiency of single-atom alloys, particularly in processes involving hydrocarbons.Reaction Rates: Provides valuable insights into the kinetics of reactions facilitated by single-atom alloys, crucial for the development and optimization of catalytic processes.

We develop a machine learning workflow to predict the critical casting diameter, glass transition temperature, and Young’s modulus for 45 ternary reported amorphous alloy systems. The predicted results have been organized into a database, enabling direct retrieval of predicted values based on compositional information.

Database of Uniaxial Cyclic and Tensile Coupon Tests for Structural Metallic Materials

Background

This dataset contains data from monotonic and cyclic loading experiments on structural metallic materials. The materials are primarily structural steels and one iron-based shape memory alloy is also included. Summary files are included that provide an overview of the database and data from the individual experiments is also included.

The files included in the database are outlined below and the format of the files is briefly described. Additional information regarding the formatting can be found through the post-processing library (https://github.com/ahartloper/rlmtp/tree/master/protocols).

Usage

• The data is licensed through the Creative Commons Attribution 4.0 International.
• If you have used our data and are publishing your work, we ask that you please reference both:
  1. this database through its DOI, and
  2. any publication that is associated with the experiments. See the Overall_Summary and Database_References files for the associated publication references.

Included Files

• Overall_Summary_2022-08-25_v1-0-0.csv: summarises the specimen information for all experiments in the database.
• Summarized_Mechanical_Props_Campaign_2022-08-25_v1-0-0.csv: summarises the average initial yield stress and average initial elastic modulus per campaign.
• Unreduced_Data-#_v1-0-0.zip: contain the original (not downsampled) data
  • Where # is one of: 1, 2, 3, 4, 5, 6. The unreduced data is broken into separate archives because of upload limitations to Zenodo. Together they provide all the experimental data.
  • We recommend you un-zip all the folders and place them in one "Unreduced_Data" directory similar to the "Clean_Data"
  • The experimental data is provided through .csv files for each test that contain the processed data. The experiments are organised by experimental campaign and named by load protocol and specimen. A .pdf file accompanies each test showing the stress-strain graph.
  • There is a "db_tag_clean_data_map.csv" file that is used to map the database summary with the unreduced data.
  • The computed yield stresses and elastic moduli are stored in the "yield_stress" directory.
• Clean_Data_v1-0-0.zip: contains all the downsampled data
  • The experimental data is provided through .csv files for each test that contain the processed data. The experiments are organised by experimental campaign and named by load protocol and specimen. A .pdf file accompanies each test showing the stress-strain graph.
  • There is a "db_tag_clean_data_map.csv" file that is used to map the database summary with the clean data.
  • The computed yield stresses and elastic moduli are stored in the "yield_stress" directory.
• Database_References_v1-0-0.bib
  • Contains a bibtex reference for many of the experiments in the database. Corresponds to the "citekey" entry in the summary files.

File Format: Downsampled Data

These are the "LP_

• The header of the first column is empty: the first column corresponds to the index of the sample point in the original (unreduced) data
• Time[s]: time in seconds since the start of the test
• e_true: true strain
• Sigma_true: true stress in MPa
• (optional) Temperature[C]: the surface temperature in degC

These data files can be easily loaded using the pandas library in Python through:

import pandas
data = pandas.read_csv(data_file, index_col=0)

The data is formatted so it can be used directly in RESSPyLab (https://github.com/AlbanoCastroSousa/RESSPyLab). Note that the column names "e_true" and "Sigma_true" were kept for backwards compatibility reasons with RESSPyLab.

File Format: Unreduced Data

These are the "LP_

• The first column is the index of each data point
• S/No: sample number recorded by the DAQ
• System Date: Date and time of sample
• Time[s]: time in seconds since the start of the test
• C_1_Force[kN]: load cell force
• C_1_Déform1[mm]: extensometer displacement
• C_1_Déplacement[mm]: cross-head displacement
• Eng_Stress[MPa]: engineering stress
• Eng_Strain[]: engineering strain
• e_true: true strain
• Sigma_true: true stress in MPa
• (optional) Temperature[C]: specimen surface temperature in degC

The data can be loaded and used similarly to the downsampled data.

File Format: Overall_Summary

The overall summary file provides data on all the test specimens in the database. The columns include:

• hidden_index: internal reference ID
• grade: material grade
• spec: specifications for the material
• source: base material for the test specimen
• id: internal name for the specimen
• lp: load protocol
• size: type of specimen (M8, M12, M20)
• gage_length_mm_: unreduced section length in mm
• avg_reduced_dia_mm_: average measured diameter for the reduced section in mm
• avg_fractured_dia_top_mm_: average measured diameter of the top fracture surface in mm
• avg_fractured_dia_bot_mm_: average measured diameter of the bottom fracture surface in mm
• fy_n_mpa_: nominal yield stress
• fu_n_mpa_: nominal ultimate stress
• t_a_deg_c_: ambient temperature in degC
• date: date of test
• investigator: person(s) who conducted the test
• location: laboratory where test was conducted
• machine: setup used to conduct test
• pid_force_k_p, pid_force_t_i, pid_force_t_d: PID parameters for force control
• pid_disp_k_p, pid_disp_t_i, pid_disp_t_d: PID parameters for displacement control
• pid_extenso_k_p, pid_extenso_t_i, pid_extenso_t_d: PID parameters for extensometer control
• citekey: reference corresponding to the Database_References.bib file
• yield_stress_mpa_: computed yield stress in MPa
• elastic_modulus_mpa_: computed elastic modulus in MPa
• fracture_strain: computed average true strain across the fracture surface
• c,si,mn,p,s,n,cu,mo,ni,cr,v,nb,ti,al,b,zr,sn,ca,h,fe: chemical compositions in units of %mass
• file: file name of corresponding clean (downsampled) stress-strain data

File Format: Summarized_Mechanical_Props_Campaign

Meant to be loaded in Python as a pandas DataFrame with multi-indexing, e.g.,

tab1 = pd.read_csv('Summarized_Mechanical_Props_Campaign_' + date + version + '.csv',
          index_col=[0, 1, 2, 3], skipinitialspace=True, header=[0, 1],
          keep_default_na=False, na_values='')

• citekey: reference in "Campaign_References.bib".
• Grade: material grade.
• Spec.: specifications (e.g., J2+N).
• Yield Stress [MPa]: initial yield stress in MPa
  • size, count, mean, coefvar: number of experiments in campaign, number of experiments in mean, mean value for campaign, coefficient of variation for campaign
• Elastic Modulus [MPa]: initial elastic modulus in MPa
  • size, count, mean, coefvar: number of experiments in campaign, number of experiments in mean, mean value for campaign, coefficient of variation for campaign

Caveats

• The files in the following directories were tested before the protocol was established. Therefore, only the true stress-strain is available for each:
  • A500
  • A992_Gr50
  • BCP325
  • BCR295
  • HYP400
  • S460NL
  • S690QL/25mm
  • S355J2_Plates/S355J2_N_25mm and S355J2_N_50mm

2813 Italy import shipment records of Steel alloy from United States with prices, volume & current Buyer’s suppliers relationships based on actual Italy import trade database.

81962 Global import shipment records of Alloy Steel Scrap with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This is a database of multi-principal element alloy phase-specific mechanical properties measured with nano-indentation. Each row contains an arbitrary sample name, a magnification level where SEM/EDS was performed, phase names, phase compositions, percentage of the indented area covered by each phase, and mechanical properties from nanoindentation. Additionally, microscopy for each sample can be found in the folder with the associated name. Included in each sample folder is an SEM backscatter image before and after indentation as well as EDS maps.

United States Alloy Steel Product Imports: Rails Standard data was reported at 323.000 USD th in Jun 2018. This records a decrease from the previous number of 432.000 USD th for May 2018. United States Alloy Steel Product Imports: Rails Standard data is updated monthly, averaging 1,418.000 USD th from Jan 2000 (Median) to Jun 2018, with 221 observations. The data reached an all-time high of 14,466.000 USD th in Jun 2004 and a record low of 0.000 USD th in Dec 2003. United States Alloy Steel Product Imports: Rails Standard data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.WA014: Steel Products Imports: Alloy Steel Product: Value.

Fatigue failure of metallic structures is of great concern to industrial applications. A material will not be able to practically useful if it is prone to fatigue failure. To take the advantage of lately emerged high entropy alloys (HEAs) for designing novel fatigue-resistant alloys, we compiled a fatigue database of HEAs from the literature reported till the yearend of 2021. The database is subdivided into three categories, i.e., low-cycle fatigue (LCF), high-cycle fatigue (HCF), and fatigue crack growth rate (FCGR), which contains 15, 23, and 28 distinct data records, respectively. Each data record in any of three categories is characteristic of a summary, which is comprised of alloy composition, key fatigue properties, and additional information influential to or interrelated with fatigue (e.g., material processing history, phase constitution, grain size, uniaxial tensile properties, and fatigue testing conditions), and an individual dataset, which makes up the original fatigue testing curve.

1317 Global export shipment records of White Metal Alloy with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

1118 Global import shipment records of Alloy Ferro with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

1555 United States import shipment records of Alloy steel from Germany with prices, volume & current Buyer’s suppliers relationships based on actual United States import trade database.

Hungary Imports from Mexico of Wire of Other Alloy Steel was US$2 Thousand during 2010, according to the United Nations COMTRADE database on international trade. Hungary Imports from Mexico of Wire of Other Alloy Steel - data, historical chart and statistics - was last updated on July of 2025.