Solid-state synthesizability predictions using positive-unlabeled learning from human-curated literature data
File(s)
Published version
Author(s)
Chung, Vincent
Walsh, Aron
Payne, David
Type
Journal Article
Abstract
The rate of materials discovery is limited by the experimental validation of promising candidate materials generated from high-throughput calculations. Although data-driven approaches, utilizing text-mined datasets, have shown some success in aiding synthesis planning and synthesizability prediction, they are limited by the quality of the underlying datasets. In this study, synthesis information
of 4,103 ternary oxides was extracted from the literature, including whether the oxide has been synthesized via solid-state reaction and the associated reaction conditions. This dataset provides an opportunity to supplement existing solid-state reaction models via reliable data and information from articles whose content and formats are challenging to extract automatically. A simple screening using this dataset identified 156 outliers from a subset of a text-mined dataset that contains 4,800 entries, of which only 15% of the outliers were extracted correctly. Finally, this dataset was used to train a positive-unlabeled learning model to predict the solid-state synthesizability of new ternary oxides, where we predict 134 out of 4,312 hypothetical compositions are likely to be synthesizable.
of 4,103 ternary oxides was extracted from the literature, including whether the oxide has been synthesized via solid-state reaction and the associated reaction conditions. This dataset provides an opportunity to supplement existing solid-state reaction models via reliable data and information from articles whose content and formats are challenging to extract automatically. A simple screening using this dataset identified 156 outliers from a subset of a text-mined dataset that contains 4,800 entries, of which only 15% of the outliers were extracted correctly. Finally, this dataset was used to train a positive-unlabeled learning model to predict the solid-state synthesizability of new ternary oxides, where we predict 134 out of 4,312 hypothetical compositions are likely to be synthesizable.
Date Issued
2025-07-19
Date Acceptance
2025-07-16
Citation
Digital Discovery, 2025, 4, pp.2439-2453
ISSN
2635-098X
Publisher
Royal Society of Chemistry
Start Page
2439
End Page
2453
Journal / Book Title
Digital Discovery
Volume
4
Copyright Statement
© 2025 The Author(s). Published by the Royal Society of Chemistry This Open Access Article is licensed under a Creative Commons Attribution 3.0 Unported Licence
License URL
Publication Status
Published
Date Publish Online
2025-07-19