When Literature Data Mislead Artificial Intelligence in Materials Discovery
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Computer Science > Information Retrieval
arXiv:2609.01621 (cs)
[Submitted on 18 Jul 2026]
Title:When Literature Data Mislead Artificial Intelligence in Materials Discovery
View a PDF of the paper titled When Literature Data Mislead Artificial Intelligence in Materials Discovery, by Qian Wang and 6 other authors
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Abstract:Artificial intelligence (AI) increasingly treats scientific literature as a data source for building databases, training predictive models, and guiding discovery. Yet literature-derived datasets often assume that reported experimental values are internally consistent and directly reusable. Here, we analyze this assumption using solid electrolyte (SE) conductivity data as a representative materials-science case. By tracing values from source articles to curated datasets, we identify recurrent text-figure mismatches, ambiguous axis annotations, unit inconsistencies, and missing measurement context. These discrepancies are often numerically plausible and therefore difficult to detect through routine preprocessing, but they can propagate as structured label noise during database construction and machine-learning reuse. A cross-database example shows how ambiguous reporting can create a 100-fold conductivity error. Our analysis reframes data accuracy as an infrastructure requirement for artificial-intelligence-driven discovery and motivates traceable reporting, curation, and validation practices for reusable scientific data.
Keywords: AI for science; Data reliability; Scientific databases; Structured label noise; Literature-derived data; Materials informatics; Solid electrolytes
| Comments: | 3 main figures, 2 supplementary tables, research article on data reliability for AI-driven materials discovery |
| Subjects: | Information Retrieval (cs.IR); Materials Science (cond-mat.mtrl-sci); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG) |
| ACM classes: | I.2.6; H.2.8; J.2 |
| Cite as: | arXiv:2609.01621 [cs.IR] |
| (or arXiv:2609.01621v1 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2609.01621
arXiv-issued DOI via DataCite
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