A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations
This paper introduces SevenNet-Nano, a lightweight universal machine-learning interatomic potential that leverages knowledge distillation from a large foundation model to achieve high accuracy and transferability while significantly reducing computational costs for large-scale atomistic simulations.