Accelerating Battery Research through Atomistic and Multi-Scale Computational Chemistry

Computational screening and advanced insights from simulations will help you develop new and improved battery materials quicker and with less resources. We will discuss our efforts to help researchers to do that with the Amsterdam Modeling Suite (AMS). The AMS framework separates the atomistic engines (DFT, DFTB, ReaxFF, ML potentials) from the driver, which enables the exploration of potential energy surfaces (PESs), mechanical, and electronic properties at several levels of theory. The central AMS driver supports advanced PES explorations, molecular dynamics (MD) and Grand Canonical Monte Carlo (GCMC), based on energies and forces from the engines. With this driver-engine set up, you can calculate and predict relevant processes and properties for battery materials. We will discuss recent developments and examples using: DFT and GW with COSMO-RS for accurate electrode and redox potentials (e)ReaxFF for discharge processes including diffusion, discharge profiles, electrolyte degradation, and solid-electrolyte interface formation polarizable force fields for accurate viscosities and charge mobilities pre-parametrized and on-the-fly machine learning potentials for diffusion barriers, intercalation potentials, and reactive processes kinetic Monte Carlo to describe mesoscale processes such as dendrite formation We will also discuss where we envision future ML and multi-scale developments to accelerate materials discovery, particularly focusing on battery applications.