foundry-ml/cac7b605-bd94-4657-ad62-bfceab05f0ce

Candidate perovskite compositions from "Machine Learning Materials Properties with Accurate Predictions, Uncertainty Estimates, Domain Guidance, and Persistent Online Accessibility" by Jacobs et al

Dataset Information

• Source: Foundry-ML
• DOI: 10.18126/acd8-rj94
• Year: 2024
• Authors: Engler, Will
• Data Type: tabular

Fields

Field	Role	Description	Units
Composition	input	Perovskite composition strings, e.g. Sr0.5Bi0.125P

Splits

• test: test

Usage

With Foundry-ML (recommended for materials science workflows)

from foundry import Foundry

f = Foundry()
dataset = f.get_dataset("10.18126/acd8-rj94")
X, y = dataset.get_as_dict()['train']

With HuggingFace Datasets

from datasets import load_dataset

dataset = load_dataset("cac7b605-bd94-4657-ad62-bfceab05f0ce")

Citation

@misc{https://doi.org/10.18126/acd8-rj94
doi = {10.18126/acd8-rj94}
url = {https://doi.org/10.18126/acd8-rj94}
author = {Engler, Will}
title = {Candidate perovskite compositions from "Machine Learning Materials Properties with Accurate Predictions, Uncertainty Estimates, Domain Guidance, and Persistent Online Accessibility" by Jacobs et al}
keywords = {machine learning, foundry, materials science}
publisher = {Materials Data Facility}
year = {root=2024}}

License

other

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