Stability and transferability of machine learning force fields for molecular dynamics applications

In this study, we focus on simplifying the generation of Machine Learning Force Fields (MLFFs) for Molecular Dynamics (MD) simulations of inorganic materials, with an emphasis on sustainable use of computational resources. We evaluate the efficiency and accuracy of existing state-of-the-art graph neural network (GNN) models and introduce new benchmarks that go beyond conventional mean absolute error on forces and energies. We showcase our methodology on the example of lithium-ion conductor materials, paving the way to a broader screening of ionic conductors for batteries and fuel cells. We benchmark GNN models for MLFF-MD and introduce new metrics beyond conventional force and energy errors. Our approach, demonstrated on lithium-ion conductors, aims to broaden ionic conductor screening for batteries.