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AI materials discovery hindered by unreliable literature data

By PulseAugur Editorial · [1 sources] ·

A new paper published on arXiv highlights significant issues with using scientific literature data to train AI models for materials discovery. Researchers found that data derived from literature often contains inconsistencies, such as text-figure mismatches, ambiguous annotations, and unit errors, which can lead to structured label noise. These discrepancies, even when numerically plausible, can propagate and cause substantial errors, as demonstrated by a 100-fold conductivity error in solid electrolyte data. The study emphasizes the need for improved data traceability, curation, and validation practices to ensure the reliability of AI-driven scientific discovery. AI

IMPACT Highlights critical data quality issues that could slow AI adoption in scientific research and discovery.

RANK_REASON Academic paper detailing issues with data reliability for AI in scientific discovery. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI materials discovery hindered by unreliable literature data

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Academic paper detailing issues with data reliability for AI in scientific discovery. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Qian Wang, Ying Li, Ryuhei Sato, Hidemi Kato, Shin-ichi Orimo, Hao Li, Eric Jianfeng Cheng ·

    When Literature Data Mislead Artificial Intelligence in Materials Discovery

    arXiv:2609.01621v1 Announce Type: cross 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 experime…

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