ArticleScientific reports2025
Artificial intelligence approach for estimating energy density of liquid metal batteries.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Challenges and Design Strategies for Stable Zinc Anodes in Rechargeable Zinc Batteries.Small (Weinheim an der Bergstrasse, Germany) · 2025Review
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5 authors.
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Abstract
Achieving a high energy density in liquid metal batteries (LMBs) still remains a big challenge. Due to the multitude of affecting parameters within the system, traditional ways may not fully capture the complexity of LMBs. The artificial intelligence approach can be effectively applied to deal with low energy density issues. Herein, we represented the first implementation of the Gaussian Process Regression to predict the LMBs' energy density to attain the highest accuracy compared to existing models. Four different kernels, namely Exponential, Matern5/2, Rational Quadratic, and Squared Exponential were utilized to achieve the most accurate GPR model. A huge dataset containing 2158 LMB datapoint was gathered from the literature. It contains 41 input parameters, including alloy-related, LMB-related, and creative features. The GPR-Exponential model showed the greatest battery energy density estimate accuracy among the proposed models. The training and testing R
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