metadata
license: cc-by-4.0
task_categories:
- tabular-regression
- tabular-classification
tags:
- materials-science
- chemistry
- foundry-ml
- scientific-data
size_categories:
- 1K<n<10K
Error assessment and optimal cross-validation approaches in machine learning applied to impurity diffusion
Dataset containing DFT-calculated dilute alloy impurity diffusion barriers for 408 host-impurity pairs
Dataset Information
- Source: Foundry-ML
- DOI: 10.18126/uppe-p8p1
- Year: 2022
- Authors: Lu, Haijin, Zou, Nan, Jacobs, Ryan, Afflerbach, Ben, Lu, Xiao-Gang, Morgan, Dane
- Data Type: tabular
Fields
| Field | Role | Description | Units |
|---|---|---|---|
| Material compositions 1 | input | Host element | |
| Material compositions 2 | input | Solute element | |
| E_regression_shift | target | DFT-calculated solute migration barrier, given rel | eV |
Splits
- train: train
Usage
With Foundry-ML (recommended for materials science workflows)
from foundry import Foundry
f = Foundry()
dataset = f.get_dataset("10.18126/uppe-p8p1")
X, y = dataset.get_as_dict()['train']
With HuggingFace Datasets
from datasets import load_dataset
dataset = load_dataset("diffusion_v1.4")
Citation
@misc{https://doi.org/10.18126/uppe-p8p1
doi = {10.18126/uppe-p8p1}
url = {https://doi.org/10.18126/uppe-p8p1}
author = {Lu, Haijin and Zou, Nan and Jacobs, Ryan and Afflerbach, Ben and Lu, Xiao-Gang and Morgan, Dane}
title = {Error assessment and optimal cross-validation approaches in machine learning applied to impurity diffusion}
keywords = {machine learning, foundry}
publisher = {Materials Data Facility}
year = {root=2022}}
License
CC-BY 4.0
This dataset was exported from Foundry-ML, a platform for materials science datasets.