foundry-ml/foundry_osdb_v1-1

Data for: Ab initio control of zeolite synthesis and intergrowth with high-throughput simulations

Dataset Information

• Source: Foundry-ML
• DOI: 10.18126/c5z9-zej7
• Year: 2021
• Authors: Schwalbe-Koda, Daniel, Gómez-Bombarelli, Rafael
• Data Type: tabular

Fields

Field	Role	Description	Units
crystal_id	input	unique identifier associated with each pose. It is
Zeolite	input	IZA code of the zeolite
SMILES	input	SMILES string of the guest docked in the zeolite
InchiKey	input	InchiKey of the guest docked in the zeolite
Ligand formula	input	formula of one molecular guest
Loading	input	number of OSDAs per unit cell in the calculated po
Binding (SiO2)	target	binding energy between the host and the guest, cal	kJ/mol
Binding (OSDA)	target	binding energy between the host and the guest, cal
Directivity (SiO2)	target	binding energy between the host and the guest, usi	kJ/mol
Competition (SiO2)	target	competition energy between different hosts for a g	kJ/mol
Competition (OSDA)	target	competition energy between different hosts for a g	kJ/mol
Templating	target	templating energy at 400 K, as calculated in the p	kJ/mol
SCScore	input	Synthetic Complexity Score, as proposed by Coley e	kJ/mol
Volume (Angstrom3)	input	volume of the OSDA, given in Angstrom^3.	Angstrom^3
Axis 1 (Angstrom)	input	first principal component of the OSDA, given in An	Angstrom
Axis 2 (Angstrom)	input	second principal component of the OSDA, given in A	Angstrom
In literature?	input	If the pair is known in the literature, the value	kJ/mol
lattice	input	lattice matrix of the crystal
nxyz	input	tuple containing the atomic number and the (x, y,

Splits

• train: train

Usage

With Foundry-ML (recommended for materials science workflows)

from foundry import Foundry

f = Foundry()
dataset = f.get_dataset("10.18126/c5z9-zej7")
X, y = dataset.get_as_dict()['train']

With HuggingFace Datasets

from datasets import load_dataset

dataset = load_dataset("foundry_osdb_v1.1")

Citation

@misc{https://doi.org/10.18126/c5z9-zej7
doi = {10.18126/c5z9-zej7}
url = {https://doi.org/10.18126/c5z9-zej7}
author = {Schwalbe-Koda, Daniel and Gómez-Bombarelli, Rafael}
title = {Data for: Ab initio control of zeolite synthesis and intergrowth with high-throughput simulations}
keywords = {machine learning, foundry, zeolite, database}
publisher = {Materials Data Facility}
year = {root=2021}}

License

CC-BY 4.0

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