Journal
BATTERIES & SUPERCAPS
Volume 4, Issue 5, Pages 758-766Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/batt.202000288
Keywords
artificial intelligence; battery; reproducibility crisis; standards; text mining
Funding
- ALISTORE European Research Institute
- European Union's Horizon 2020 research and innovation programme through the European Research Council (ARTISTIC project) [772873]
- Institut Universitaire de France
- Swedish Energy Agency Batterifondsprogrammet
- French National Research Agency through the Labex STORE-EX project [ANR-10LABX-76-01]
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Artificial Intelligence has the potential to revolutionize battery R&D by accelerating material discovery, but its success depends on access to high-quality data. Scientific publications may provide valuable data for developing reliable AI algorithms, but challenges still need to be addressed.
Artificial Intelligence (AI) has the promise of providing a paradigm shift in battery R&D by significantly accelerating the discovery and optimization of materials, interfaces, phenomena, and processes. However, the efficiency of any AI approach ultimately relies on rapid access to high-quality and interpretable large datasets. Scientific publications contain a tremendous wealth of relevant data and these can possibly, but not certainly, be used to develop reliable AI algorithms useful for battery R&D. To address this, we present here a text mining study wherein we unravel lithium-ion battery researchers' habits when reporting results, reason on how these habits link to issues of lacking reproducibility and discuss the remaining challenges to be tackled in order to develop a more credible and impactful AI for battery R&D.
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