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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](https://github.com/MLMI2-CSSI/foundry)
- **DOI**: [10.18126/3zkm-yd51](https://doi.org/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)
```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.*
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