Unlocking Tensor Core Performance for Machine Learning Interatomic Potentials

Atomistic simulations reveal how molecules and materials behave, from chemical reactions and phase transitions to mechanical failure, but involve a cost-accuracy trade-off. Quantum-mechanical methods such as density functional theory (DFT) provide accurate energies and forces, yet their computational cost often limits their use for large systems or long simulations.