README.md

metadata

license: other
task_categories:
  - tabular-regression
  - tabular-classification
tags:
  - materials-science
  - chemistry
  - foundry-ml
  - scientific-data
size_categories:
  - 1K<n<10K

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
wt_percent_Cu	input	Amount of Cu	wt%
wt_percent_Ni	input	Amount of Ni	wt%
wt_percent_Mn	input	Amount of Mn	wt%
wt_percent_P	input	Amount of P	wt%
wt_percent_Si	input	Amount of Si	wt%
wt_percent_C	input	Amount of C	wt%
log(fluence_n_cm2)	input	Irradiation fluence (log scale)	n/cm2
log(flux_n_cm2_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)

from foundry import Foundry

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

With HuggingFace Datasets

from datasets import load_dataset

dataset = load_dataset("Dataset_RPV_TTS")

Citation

@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

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