Abstract
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.