Understanding and controlling the atomistic-level reactions governing the
formation of the solid-electrolyte interphase (SEI) is crucial for the
viability of next-generation solid state batteries. However, challenges persist
due to difficulties in experimentally characterizing buried interfaces and
limits in simulation speed and accuracy. We conduct large-scale explicit
reactive simulations with quantum accuracy for a symmetric battery cell,
{\symcell}, enabled by active learning and deep equivariant neural network
interatomic potentials. To automatically characterize the coupled reactions and
interdiffusion at the interface, we formulate and use unsupervised
classification techniques based on clustering in the space of local atomic
environments. Our analysis reveals the formation of a previously unreported
crystalline disordered phase, Li$_2$S$_{0.72}$P$_{0.14}$Cl$_{0.14}$, in the
SEI, that evaded previous predictions based purely on thermodynamics,
underscoring the importance of explicit modeling of full reaction and transport
kinetics. Our simulations agree with and explain experimental observations of
the SEI formations and elucidate the Li creep mechanisms, critical to dendrite
initiation, characterized by significant Li motion along the interface. Our
approach is to crease a digital twin from first principles, without adjustable
parameters fitted to experiment. As such, it offers capabilities to gain
insights into atomistic dynamics governing complex heterogeneous processes in
solid-state synthesis and electrochemistry.
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