foundry-ml/elwood_md_v1-2

Project Elwood: MD Simulated Monomer Properties

Dataset Information

• Source: Foundry-ML
• DOI: 10.18126/8p6m-e135
• Year: 2022
• Authors: Schneider, L, Schwarting, M, Mysona, J, Liang, H, Han, M, Rauscher, P, Ting, J, Venkatram, S, Ross, R, Schmidt, K, Blaiszik, B, Foster, I, de Pablo, J
• Data Type: tabular

Fields

Field	Role	Description	Units
SMILES	input	Canonical SMILES string of molecule	arb
E_coh (MPa)	target	Simulated cohesive energy (in MPa)	MPa
T_g (K)	target	Simulated glass transition temperature (in Kelvin)	Kelvin
R_gyr (A^2)	target	Simulated squared radius of gyration (in Angstroms	Angstrom^2
Densities (kg/m^3)	target	Simulated density (in kg/m^3)	kg/m^3

Splits

• train: train

Usage

With Foundry-ML (recommended for materials science workflows)

from foundry import Foundry

f = Foundry()
dataset = f.get_dataset("10.18126/8p6m-e135")
X, y = dataset.get_as_dict()['train']

With HuggingFace Datasets

from datasets import load_dataset

dataset = load_dataset("elwood_md_v1.2")

Citation

@misc{https://doi.org/10.18126/8p6m-e135
doi = {10.18126/8p6m-e135}
url = {https://doi.org/10.18126/8p6m-e135}
author = {Schneider, L and Schwarting, M and Mysona, J and Liang, H and Han, M and Rauscher, P and Ting, J and Venkatram, S and Ross, R and Schmidt, K and Blaiszik, B and Foster, I and de Pablo, J}
title = {Project Elwood: MD Simulated Monomer Properties}
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.