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MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks
Name
s41597-022-01181-0.pdf
Description
Published version
Size
2.88 MB
Format
Adobe PDF
Checksum (MD5)
4b140080740f0130cfd92a87c50c0ad1
Author(s)
Nandy, Aditya
Terrones, Gianmarco
Arunachalam, Naveen
Duan, Chenru
Kastner, David W
Kulik, Heather J
Date Issued
December 2022
Journal
Scientific Data
Publisher
Springer Science and Business Media LLC
Citation
Nandy, Aditya, Terrones, Gianmarco, Arunachalam, Naveen, Duan, Chenru, Kastner, David W et al. 2022. "MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks." Scientific Data, 9 (1).
Version
Final published version
Abstract
AbstractWe report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal–organic framework (MOF) literature describing structurally characterized MOFs and their solvent removal and thermal stabilities. We obtain over 2,000 solvent removal stability measures from text mining and 3,000 thermal decomposition temperatures from thermogravimetric analysis data. We assess the validity of our NLP methods and the accuracy of our extracted data by comparing to a hand-labeled subset. Machine learning (ML, i.e. artificial neural network) models trained on this data using graph- and pore-geometry-based representations enable prediction of stability on new MOFs with quantified uncertainty. Our web interface, MOFSimplify, provides users access to our curated data and enables them to harness that data for predictions on new MOFs. MOFSimplify also encourages community feedback on existing data and on ML model predictions for community-based active learning for improved MOF stability models.
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
10.1038/s41597-022-01181-0
