Model and Data from: A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations

This repository contains the dataset, models, and code supporting the paper: "A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations". Contents included: checkpoints.tar: Pre-trained checkpoints of the 7net-Nano model. Modified SevenNet packages for fine-tuning. example.tar: Example code for fine-tuning applied to liquid electrolyte applications. dft.tar: DFT calculation data used for benchmarking SiO2 with CFx plasma etching simulations