Datasetpublic

foundry-ml/dataset_rpv_tts

Predictions and uncertainty estimates of reactor pressure vessel steel embrittlement using Machine learning Dataset containing 4535 transition temperature shifts of reactor pressure vessel steels Dataset Information Source: Foundry-ML DOI: 10.18126/3zkm-yd51 Year: 2023 Authors: Jacobs, Ryan, Yamamoto, Takuya, Odette, G. Robert, Morgan, Dane Data Type: tabular Fields Field Role Description Units temperature_C input Temperature of… See the full description on the dataset page: https://huggingface.co/datasets/foundry-ml/dataset_rpv_tts.

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Dataset Card

Predictions and uncertainty estimates of reactor pressure vessel steel embrittlement using Machine learning

Dataset containing 4535 transition temperature shifts of reactor pressure vessel steels

Dataset Information

—Source: Foundry-ML
—DOI: 10.18126/3zkm-yd51
—Year: 2023
—Authors: Jacobs, Ryan, Yamamoto, Takuya, Odette, G. Robert, Morgan, Dane
—Data Type: tabular

Fields

Field	Role	Description	Units
temperature_C	input	Temperature of measurement	degC
wtpercentCu	input	Amount of Cu	wt%
wtpercentNi	input	Amount of Ni	wt%
wtpercentMn	input	Amount of Mn	wt%
wtpercentP	input	Amount of P	wt%
wtpercentSi	input	Amount of Si	wt%
wtpercentC	input	Amount of C	wt%
log(fluencencm2)	input	Irradiation fluence (log scale)	n/cm2
log(fluxncm2_sec)	input	Irradiation flux (log scale)	n/cm2-s
datatype	input	Data subtype
Measured DT41J [C]	target	Ductile-to-brittle transition temperature shift	degC

Splits

—train: train

Usage

With Foundry-ML (recommended for materials science workflows)

python

from foundry import Foundry

f = Foundry()
dataset = f.get_dataset("10.18126/3zkm-yd51")
X, y = dataset.get_as_dict()['train']

With HuggingFace Datasets

python

from datasets import load_dataset

dataset = load_dataset("Dataset_RPV_TTS")

Citation

bibtex

@misc{https://doi.org/10.18126/3zkm-yd51
doi = {10.18126/3zkm-yd51}
url = {https://doi.org/10.18126/3zkm-yd51}
author = {Jacobs, Ryan and Yamamoto, Takuya and Odette, G. Robert and Morgan, Dane}
title = {Predictions and uncertainty estimates of reactor pressure vessel steel embrittlement using Machine learning}
keywords = {machine learning, foundry}
publisher = {Materials Data Facility}
year = {root=2023}}

License

other

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