# Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials

arXiv: https://arxiv.org/abs/2502.05379 · HTML: https://mlsanity.com/paper/2502.05379/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Graph neural network distillation, one of 12 directions in Knowledge distillation.
- Builds on 0 of its 7 references that are in the map.
- First posted on arXiv 7 Feb 2025.

## Placed in

- Knowledge distillation / [Graph neural network distillation](https://mlsanity.com/distill/graph-neural-network-distillation/)

## Built on by (most cited first, 1 of 1)

- [Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials](https://mlsanity.com/paper/2503.14293/) (2025)

## Abstract (arXiv)

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.