Machine learning interatomic potentials (MLIPs) are revolutionizing the field of molecular dynamics (MD) simulations. Recent MLIPs have tended towards more complex architectures trained on larger datasets. The resulting increase in computational and memory costs may prohibit the application of these MLIPs to perform large-scale MD simulations. Here, we present a teacher-student training framework in which the latent knowledge from the teacher (atomic energies) is used to augment the students' training. We show that the light-weight student MLIPs have faster MD speeds at a fraction of the memory footprint compared to the teacher models. Remarkably, the student models can even surpass the accuracy of the teachers, even though both are trained on the same quantum chemistry dataset. Our work highlights a practical method for MLIPs to reduce the resources required for large-scale MD simulations.
@article{arxiv.2502.05379,
title = {Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials},
author = {Sakib Matin and Alice E. A. Allen and Emily Shinkle and Aleksandra Pachalieva and Galen T. Craven and Benjamin Nebgen and Justin S. Smith and Richard Messerly and Ying Wai Li and Sergei Tretiak and Kipton Barros and Nicholas Lubbers},
journal= {arXiv preprint arXiv:2502.05379},
year = {2025}
}