Ma, Yirui; Jin, Tianwei; Choudhury, Rishav; Cheng, Qian; Miao, Yupeng; Zheng, Changxi; Min, Wei; Yang, Yuan published the artcile< Understanding the correlation between lithium dendrite growth and local material properties by machine learning>, Quality Control of 112-63-0, the main research area is lithium metal battery dendrite growth neural network machine learning.
Lithium metal batteries are attractive for next-generation energy storage because of their high energy d. A major obstacle to their commercialization is the uncontrollable growth of lithium dendrites, which arises from complicated but poorly understood interactions at the electrolyte/electrode interface. In this work, we use a machine learning-based artificial neural network (ANN) model to explore how the lithium growth rate is affected by local material properties, such as surface curvature, ion concentration in the electrolyte, and the lithium growth rates at previous moments. The ion concentration in the electrolyte was acquired by Stimulated Raman Scattering Microscopy, which is often missing in past exptl. data-based modeling. The ANN network reached a high correlation coefficient of 0.8 between predicted and exptl. values. Further sensitivity anal. based on the ANN model demonstrated that the salt concentration and concentration gradient, as well as the prior lithium growth rate, have the highest impacts on the lithium dendrite growth rate at the next moment. This work shows the potential capability of the ANN model to forecast lithium growth rate, and unveil the inner dependency of the lithium dendrite growth rate on various factors.
Journal of the Electrochemical Society published new progress about Battery electrodes. 112-63-0 belongs to class esters-buliding-blocks, and the molecular formula is C19H34O2, Quality Control of 112-63-0.
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