  LixTMO2 (TM=Ni, Co, Mn) forms an important family of cathode materials for
Li-ion batteries, whose performance is strongly governed by Li
composition-dependent crystal structure and phase stability. Here, we use
LixCoO2 (LCO) as a model system to benchmark a machine learning-enabled
framework for bridging scales in materials physics. We focus on two scales: (a)
assemblies of thousands of atoms described by density functional
theory-informed statistical mechanics, and (b) continuum phase field models to
study the dynamics of order-disorder transitions in LCO. Central to the scale
bridging is the rigorous, quantitatively accurate, representation of the free
energy density and chemical potentials of this material system by
coarsegraining formation energies for specific atomic configurations. We
develop active learning workflows to train recently developed integrable deep
neural networks for such high-dimensional free energy density and chemical
potential functions. The resulting, first principles-informed, machine
learning-enabled, phase-field computations allow us to study LCO cathodes'
phase evolution in terms of temperature, morphology, charge cycling and
particle size.
