metadata
license: other
task_categories:
- tabular-regression
- tabular-classification
tags:
- materials-science
- chemistry
- foundry-ml
- scientific-data
size_categories:
- 1K<n<10K
Machine Learning Design of Perovskite Catalytic Properties
Dataset containing 2844 perovskite stability data points from DFT
Dataset Information
- Source: Foundry-ML
- DOI: 10.18126/xcye-zy28
- Year: 2023
- Authors: Jacobs, Ryan, Liu, Jian, Abernathy, Harry, Morgan, Dane
- Data Type: tabular
Fields
| Field | Role | Description | Units |
|---|---|---|---|
| composition | input | Material composition with sites | |
| composition (no brackets) | input | Material composition | |
| O pband (eV) | input | DFT-calculated O p-band center | eV |
| energy | input | DFT-calculated total energy | eV/cell |
| Nominal d # | input | Number of transition metal d electrons | |
| Band gap (eV) | input | DFT-calculated band gap | eV |
| E_hull (meV/atom) | target | Energy above hull at UHV, 1200 K | meV/atom |
| E_above_hull_closed (meV/atom) | target | Energy above hull of closed system | meV/atom |
| E_above_hull_open (meV/atom) | target | Energy above hull of open system at 500 C, room ai | meV/atom |
Splits
- train: train
Usage
With Foundry-ML (recommended for materials science workflows)
from foundry import Foundry
f = Foundry()
dataset = f.get_dataset("10.18126/xcye-zy28")
X, y = dataset.get_as_dict()['train']
With HuggingFace Datasets
from datasets import load_dataset
dataset = load_dataset("Dataset_perovskite_stability_updated")
Citation
@misc{https://doi.org/10.18126/xcye-zy28
doi = {10.18126/xcye-zy28}
url = {https://doi.org/10.18126/xcye-zy28}
author = {Jacobs, Ryan and Liu, Jian and Abernathy, Harry and Morgan, Dane}
title = {Machine Learning Design of Perovskite Catalytic Properties}
keywords = {machine learning, foundry}
publisher = {Materials Data Facility}
year = {root=2023}}
License
other
This dataset was exported from Foundry-ML, a platform for materials science datasets.