Abstract
We used data-driven methods to understand the formation of impurity phases in BiFeO3 thin-film synthesis through the sol-gel technique. Using a high-quality dataset of 331 synthesis procedures and outcomes extracted manually from 177 scientific articles, we trained decision tree models that reinforce important experimental heuristics for the avoidance of phase impurities but ultimately show limited predictive capability. We find that several important synthesis features, identified by our model, are often not reported in the literature. To test our ability to correctly impute missing synthesis parameters, we attempted to reproduce nine syntheses from the literature with varying degrees of “missingness”. We demonstrate how a text-mined dataset can be made useful by informing new controlled experiments and forming a better understanding for impurity phase formation in this complex oxide system.
Original language | English |
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Pages (from-to) | 772-785 |
Number of pages | 14 |
Journal | Chemistry of Materials |
Volume | 36 |
Issue number | 2 |
DOIs | |
State | Published - 23 Jan 2024 |
Bibliographical note
Publisher Copyright:© 2023 The Authors. Published by American Chemical Society.
ASJC Scopus subject areas
- General Chemistry
- General Chemical Engineering
- Materials Chemistry