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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.… See the full description on the dataset page: https://huggingface.co/datasets/foundry-ml/dataset_metallicglass_rc.

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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

FieldRoleDescriptionUnits
indexinputEntry ID
FORMULAinputMaterial composition
Predictioninput
phaseinputMaterial phase designation
qualityinputExperiment type to obtain data
weightinput
compositioninputMaterial composition
logRctargetCritical cooling rate (log scale)K

Splits

  • —train: train

Usage

With Foundry-ML (recommended for materials science workflows)

python
from foundry import Foundry

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

With HuggingFace Datasets

python
from datasets import load_dataset

dataset = load_dataset("Dataset_metallicglass_Rc")

Citation

bibtex
@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

This dataset was exported from [Foundry-ML](https://github.com/MLMI2-CSSI/foundry), a platform for materials science datasets.