Tensor Networks as a tool to improve LAMMPS simulation potentials

Machine Learning Potentials (MLPs) have emerged as powerful tools for modeling interatomic interactions in materials science. The MLIP package provides an efficient and versatile framework for realizing MLPs within the LAMMPS molecular dynamics simulation package. In this talk, we present an improved MLIP package that includes enhanced functionality and performance. The package is tested for various elements, including aluminum, silicon, and magnesiumBy leveraging the capabilities of tensor networks, we demonstrate the potential of MLPs for modeling complex interatomic interactions. The improved MLIP package offers significant advantages, including faster training times, improved accuracy, and increased scalability. These advancements pave the way for the widespread use of MLPs in materials science simulations.