When Literature Data Mislead Artificial Intelligence in Materials Discovery
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...
| Authors | Qian Wang, Ying Li, Ryuhei Sato et al. |
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| Primary category | cs.IR |
| Published | 18 July 2026 |
| Last updated at source | 18 July 2026 |
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