Computational Modeling of Morphology Evolution in Metal-Based Battery Electrodes

Reference work entry


Superior energy and power density, low toxicity, and enhanced shelf life have contributed to the popularity of lithium ion batteries as energy storage devices in the electronics and automobile industries. However, next-generation lithium ion batteries will require even higher energy densities to meet ever-increasing demands for longer battery life. Owing to its extremely high theoretical specific capacity (approximately ten times larger than that of conventional anode materials) and low electrochemical reduction potential (−3.04 V with respect to H/H+ reference electrode), lithium metal is a highly attractive candidate as an anode material for next-generation lithium ion batteries. However, challenges such as dendrite growth, which can lead to short circuits or capacity loss from electrical isolation of growths, have prevented widespread commercial use of lithium metal electrodes. Successful commercialization will require stabilization of lithium deposition. Devising strategies to achieve this goal will require an understanding of the fundamental mechanisms that govern electrochemical deposition and dendrite propagation and which span multiple length scales. Building on experimental observations, several mathematical models have been developed to evaluate the roles of a variety of physical phenomena (such as electrochemical reaction, diffusion, migration, mechanical stress and strain, and surface tension) in lithium deposition processes. The present chapter provides an overview of these approaches for modeling lithium deposition and dendrite growth, summarizes their findings, and discusses remaining questions and future directions for dendrite modeling.


Lithium metal Lithium electrode Lithium anode Lithium deposition Electrodeposition Dendrite Nucleation Lithium ion batteries Computational modeling Liquid electrolyte Polymer electrolyte Solid-state electrolyte 



The authors gratefully acknowledge support from the U. S. Department of Energy (DOE), Vehicle Technologies Office. Argonne National Laboratory is operated for DOE Office of Science by UChicago Argonne LLC under contract number DE-AC02-06CH11357. Lawrence Berkeley National Laboratory is managed for DOE Office of Science by University of California under contract number DE-AC02-05CH11231.


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Copyright information

© This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2020

Authors and Affiliations

  1. 1.Argonne National LaboratoryLemontUSA
  2. 2.Lawrence Berkeley National LaboratoryBerkeleyUSA
  3. 3.California State UniversityFresnoUSA

Section editors and affiliations

  • John Sarrao
    • 1
  • Marius Stan
  1. 1.Los Alamos National LaboratoryLos AlamosUSA

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