  Uncontrollable dendrites growth during electrochemical cycles leads to low
Coulombic efficiency and critical safety issues in Li metal batteries. Hence, a
comprehensive understanding of the dendrite formation mechanism is essential
for further enhancing the performance of Li metal batteries. Machine learning
accelerated molecular dynamics (MD) simulations can provide atomic-scale
resolution for various key processes at an ab-initio level accuracy. However,
traditional MD simulation tools hardly capture Li electrochemical depositions,
due to lack of an electrochemical constant potential (ConstP) condition. In
this work, we propose a ConstP approach that combines a machine learning force
field with the charge equilibration method to reveal the dynamic process of Li
dendrites nucleation at Li metal anode surfaces. Our results show that both
dead Li cluster formation and inhomogeneous Li electro-depositions can induce
Li dendrites nucleation. We further reveal that the local aggregation of Li
atoms in amorphous inorganic components of solid electrolyte interphase is the
key factor triggering the nucleation process. Overall, our simulations provide
microscopic insights for Li dendrites formations in Li metal anodes. More
importantly, we present an efficient and accurate simulation method for
modeling realistic ConstP conditions, which holds considerable potential for
broader applications in modeling of complex electrochemical interfaces.
