Accelerated Optimization Methods for Force‐Field Parametrization in Battery Electrode Manufacturing Modeling

Abstract The performance, durability and cost of modern Li‐ion batteries (LIBs) strongly depend on their manufacturing process. In this regard, computational methods that attempt to model LIBs manufacturing have the potential to help experimentalists to reduce costs and improve performances of LIBs. To do so, the electrode slurry phase, consisting of a suspension of active material, carbon additive and binder in a solvent must be modeled at first at the mesoscopic scale. However, efforts in that sense are rare in literature due to the inherent complexity of slurries and the difficulty of setting up appropriate metrics for the validation of the modeling results. For the first time, we propose an approach relying on experimental data that allows to validate the properties of simulated Nickel‐Manganese‐Cobalt‐based slurries with different compositions and solid contents through force fields (FFs) parametrization. The latter was attained by Coarse‐Grained Molecular Dynamics (CGMD), which enables to model the slurry phase at the mesoscopic scale and to consider explicitly the carbon binder domain. Moreover, we demonstrate that the CGMD FFs parameterization can be accelerated thanks to different types of Particle Swarm Optimization‐based algorithms, which would allow faster screening of different simulated slurries fabrication conditions.