Atomistic simulations help researchers understand how molecules and materials behave, from chemical reactions and phase transitions to mechanical failure. These simulations come with a cost-accuracy trade-off. Quantum-mechanical methods such as density functional theory (DFT) can provide accurate energies and forces, but their computational cost often limits their use for large systems or long simulations. Classical interatomic potentials are much faster, but their fixed functional forms limit accuracy and generalizability across different atomic environments. Machine learning interatomic potentials (MLIPs) help bridge this gap. They use neural networks to predict potential energies and interatomic forces at a fraction of the cost, enabling researchers to study larger systems and longer timescales while retaining much of the required accuracy.

Running an MLIP efficiently, however, presents its own challenge. Atomistic simulations are sensitive to small errors that can accumulate over time and are most visible when computing forces, which are obtained by differentiating the predicted energy. During molecular dynamics simulations, these forces guide updates to atomic velocities and positions over many timesteps, making numerical precision important not only for physical accuracy but also for the stability of the resulting trajectory.