foundry-ml/dataset_metallicglass_rc

Machine Learning Prediction of the Critical Cooling Rate for Metallic Glasses from Expanded Datasets and Elemental Features

Dataset containing experimental critical cooling rates of 2125 metallic glasses

Dataset Information

• Source: Foundry-ML
• DOI: 10.18126/rtsj-2e11
• Year: 2022
• Authors: Afflerbach, Benjamin T., Francis, Carter, Schultz, Lane E., Spethson, Janine, Meschke, Vanessa, Strand, Elliot, Ward, Logan, Perepezko, John H., Thoma, Dan, Voyles, Paul M., Szlufarska, Izabela, Morgan, Dane
• Data Type: tabular

Fields

Field	Role	Description	Units
index	input	Entry ID
FORMULA	input	Material composition
Prediction	input
phase	input	Material phase designation
quality	input	Experiment type to obtain data
weight	input
composition	input	Material composition
logRc	target	Critical cooling rate (log scale)	K

Splits

• train: train

Usage

With Foundry-ML (recommended for materials science workflows)

from foundry import Foundry

f = Foundry()
dataset = f.get_dataset("10.18126/rtsj-2e11")
X, y = dataset.get_as_dict()['train']

With HuggingFace Datasets

from datasets import load_dataset

dataset = load_dataset("Dataset_metallicglass_Rc")

Citation

@misc{https://doi.org/10.18126/rtsj-2e11
doi = {10.18126/rtsj-2e11}
url = {https://doi.org/10.18126/rtsj-2e11}
author = {Afflerbach, Benjamin T. and Francis, Carter and Schultz, Lane E. and Spethson, Janine and Meschke, Vanessa and Strand, Elliot and Ward, Logan and Perepezko, John H. and Thoma, Dan and Voyles, Paul M. and Szlufarska, Izabela and Morgan, Dane}
title = {Machine Learning Prediction of the Critical Cooling Rate for Metallic Glasses from Expanded Datasets and Elemental Features}
keywords = {machine learning, foundry}
publisher = {Materials Data Facility}
year = {root=2022}}

License

other

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This dataset was exported from Foundry-ML, a platform for materials science datasets.