README.md

---
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.*