DOI: 10.5281/zenodo.17612939
title: Multi-Category Materials Information Extraction (Composition, Processing, Microstructure, Properties)

description: Dataset Overview

This dataset contains the comprehensive ground truth annotations used for evaluating end-to-end materials information extraction pipelines, focusing on precipitate-containing multi-principal element alloys (MPEAs). It was constructed from 100 peer-reviewed journal articles, from which 396 distinct materials were identified and annotated.

Each material entry contains a structured set of 47 features across four major categories:

Composition (14 features)

The dataset records the atomic percentage (at%) of 14 constituent elements commonly found in multi-principal element alloys (MPEAs). The features correspond exactly to the elemental composition fields extracted from the primary literature. These include:

Fe (Iron)

Ni (Nickel)

Co (Cobalt)

Mn (Manganese)

Cr (Chromium)

Al (Aluminum)

Ti (Titanium)

Cu (Copper)

Si (Silicon)

V (Vanadium)

Nb (Niobium)

B (Boron)

Mo (Molybdenum)

Ta (Tantalum)

Each alloy entry provides a complete vector of these atomic fractions, enabling downstream tasks such as clustering, alloy design, and analysis of compositional effects on microstructure and mechanical properties.

Processing Parameters (12 features)

Processing information captures the thermo-mechanical treatment history of each alloy. The dataset includes four major processing stages, with their corresponding numeric parameters and binary flags indicating whether a step was reported:

1. Homogenization

Homogenization? (boolean / yes-no flag indicating applicability)

Homogenization Temperature (°C)

Homogenization Time (h or min)

2. Rolling

Rolling? (boolean)

Rolling Temperature (°C)

Rolling Percentage (%)

3. Recrystallization

Recrystallization? (boolean)

Recrystallization Temperature (°C)

Recrystallization Time (h or min)

4. Aging

Aging? (boolean)

Aging Temperature (°C)

Aging Time (h or min)

These parameters enable quantitative comparison of process–structure–property relationships, and support automated analysis of how thermal–mechanical treatments influence precipitate formation and mechanical behavior.

Microstructure (11 features)

Microstructural features capture both the primary matrix characteristics and the precipitate populations reported in the literature. The dataset includes:

Matrix

Matrix Type (e.g., FCC, BCC, dual-phase, ordered phase)

Matrix Volume Percentage (%)

Precipitates (up to three distinct precipitate populations)

For Precipitate 1, Precipitate 2, and Precipitate 3, the following features are recorded for each population:

Precipitate Type(e.g., FCC, B2, L12, σ-phase, carbides, intermetallics)

Precipitate Size(µm or nm, as reported)

Precipitate Volume Percentage (%)

This structure supports precise characterization of multi-phase microstructures, including complex precipitate strengthening mechanisms that are central to the performance of multi-principal element alloys.

Material Properties (10 features)

The dataset includes 10 mechanical and thermomechanical properties, capturing both room-temperature behavior and cryogenic heat-treated performance. These include:

Strength Measurements

Ultimate Tensile Strength (UTS)

Ultimate Compressive Strength (UCS)

Tensile Yield Strength (TYS)

Compressive Yield Strength (CYS)

Hardness

Hardness (e.g., HV, HRC; values recorded as reported)

Ductility

Tensile Ductility (%)

Compressive Ductility (%)

Non-Room-Temperature Cryogenic Heat-Treated Properties

For alloys subjected to cryogenic heat treatments:

Cryo HT Temperature

Cryo HT Strength

Cryo HT Ductility (%)