AI & Machine Learning

AQVolt26: High-Temperature r$^2$SCAN Halide Dataset for Universal ML Potentials and Solid-State Batteries

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Researchers have introduced AQVolt26, a dataset of 322,656 r $^2$ SCAN single-point calculations for lithium halides at high temperatures, addressing the challenge of accurately modeling ion transport in halide solid-state electrolytes. This development is crucial for advancing machine learning models used in battery research by highlighting the necessity of targeted, high-temperature data to improve model reliability and accuracy in extreme conditions.

Read the full article at arXiv cs.LG (ML)

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