A computational approach to characterize formation of a passivation layer in lithium metal anodes

Abstract

Li metal anode is the “Holy Grail” material of advanced Lithium‐ion‐batteries (LIBs). However, it is plagued by uncontrollable dendrite growth resulting in poor cycling efficiency and short‐circuiting of batteries. This has spurred a plethora of research to understand the underlying mechanism of dendrite formation. While experimental studies suggest that there are complex physical and chemical interactions between heterogeneous solid‐electrolyte interphase (SEI) and dendrite growth, most of the studies do not reveal the mechanisms triggering these interactions. To deal with this knowledge gap, we propose a multiscale modeling framework which couples kinetic Monte Carlo and Molecular Dynamics simulations. Specifically, the model has been developed to account for (a) heterogeneous SEI, (b) dendrite‐SEI interactions, and (c) effect of electrolyte on Li electrodeposition and potential dendrite formation. This allows the proposed computational model to be extended to various electrolytes and SEI species and generate results consistent with previous experimental studies.

CFD-Based Computational Studies of Quantum Dot Size Control in Slug Flow Crystallizers: Handling Slug-to-Slug Variation

“…Further, these input values were taken from the kMC simulation results under varying v slug and C 0 conditions. It is important to note that the kMC simulation is stochastic in nature, − and to smooth out these differences, an average of 10 kMC realizations were used as inputs. Last, the input values were normalized to avoid any bias based on the magnitude of these different inputs.…”

Modeling ligand crosslinking for interlocking quantum dots in thin-films

“…19,27,28 Although traditional molecular dynamics (MD) simulations can model the crosslinking reaction kinetics in great detail, it has a very high computational cost, and cannot consider a large number of surface ligands with different QDs. [29][30][31] Furthermore, the time-scale for MD simulations is very small (i.e., ps to ns), which would be too short for capturing the entire crosslinking phenomenon in various crosslinker systems. Other coarse-grained molecular-level models can also incorporate bond-breaking and bond-creation steps albeit requiring a large computational cost and might be unable to consider a large number of surface ligands.…”

“…19,27,28 Although traditional molecular dynamics (MD) simulations can model the crosslinking reaction kinetics in great detail, it has a very high computational cost, and cannot consider a large number of surface ligands with different QDs. 29–31 Furthermore, the time-scale for MD simulations is very small ( i.e. , ps to ns), which would be too short for capturing the entire crosslinking phenomenon in various crosslinker systems.…”

Kinetic Monte Carlo simulations applied to Li-ion and post Li-ion batteries: a key link in the multi-scale chain

“…An aspect not considered in the previous works is the passivation layer formed on the Li metal surface. This topic was treated by Sitapure et al [180] with kMC and MD simulations, considering the impact of the SEI on dendrite growth. MD was used to simulate SEI formation in different electrolytes: LiPF6 + Dioxolane (DOL) + Fluoroethylene carbonate (FEC), and LiPF6 + Ethylene carbonate (EC).…”