  The optimization of the electrodes manufacturing process constitutes one of
the most critical steps to ensure high-quality Lithium-Ion Battery (LIB) cells,
in particular for automotive applications. Because LIB electrode manufacturing
is a complex process involving multiple steps and interdependent parameters, we
have shown in our previous works that 3D-resolved physics-based models
constitute very useful tools to provide insights about the impact of the
manufacturing process parameters on the textural and performance properties of
the electrodes. However, their high-throughput application for electrode
properties optimization and inverse design of manufacturing parameters is
limited due to the high computational cost associated with this kind of model.
In this work, we tackle this issue by proposing an innovative approach,
supported by a deterministic machine learning (ML)-assisted pipeline for
multi-objective optimization of LIB electrode properties and inverse design of
its manufacturing process. Firstly, the pipeline generates a synthetic dataset
from physics-based simulations with low discrepancy sequences, that allow to
sufficiently represent the manufacturing parameters space. Secondly, the
generated dataset is used to train deterministic ML models for the
implementation of a fast multi-objective optimization, to identify an optimal
electrode and the manufacturing parameters to adopt in order to fabricate it.
Lastly, this electrode was successfully fabricated experimentally, proving that
our modeling pipeline prediction is physical-relevant. Here, we demonstrate our
pipeline for the simultaneous minimization of the electrode tortuosity factor
and maximization of the effective electronic conductivity, the active surface
area, and the density, all being parameters that affect the Li$^+$
(de-)intercalation kinetics, ionic, and electronic transport properties of the
electrode.
