Extrapolation of Machine-Learning Interatomic Potentials for Organic and Polymeric Systems
This study establishes a roadmap for creating transferable Machine-Learning Interatomic Potentials for macromolecular systems by demonstrating that convergence in chemical environments and careful neighbor list construction enable accurate extrapolation from small n-polyalkane training data to larger polymers without prohibitive computational costs.