TY - JOUR
T1 - Understanding the Correlation between Lithium Dendrite Growth and Local Material Properties by Machine Learning
AU - Ma, Yirui
AU - Jin, Tianwei
AU - Choudhury, Rishav
AU - Cheng, Qian
AU - Miao, Yupeng
AU - Zheng, Changxi
AU - Min, Wei
AU - Yang, Yuan
N1 - Publisher Copyright:
© 2021 The Electrochemical Society ("ECS"). Published on behalf of ECS by IOP Publishing Limited.
PY - 2021/9
Y1 - 2021/9
N2 - Lithium metal batteries are attractive for next-generation energy storage because of their high energy density. 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 experimental data-based modeling. The ANN network reached a high correlation coefficient of 0.8 between predicted and experimental values. Further sensitivity analysis 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.
AB - Lithium metal batteries are attractive for next-generation energy storage because of their high energy density. 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 experimental data-based modeling. The ANN network reached a high correlation coefficient of 0.8 between predicted and experimental values. Further sensitivity analysis 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.
UR - http://www.scopus.com/inward/record.url?scp=85116367232&partnerID=8YFLogxK
U2 - 10.1149/1945-7111/ac201d
DO - 10.1149/1945-7111/ac201d
M3 - Article
AN - SCOPUS:85116367232
SN - 0013-4651
VL - 168
JO - Journal of the Electrochemical Society
JF - Journal of the Electrochemical Society
IS - 9
M1 - 090523
ER -