Unlocking Tensor Core Performance for Machine Learning Interatomic Potentials

Machine learning interatomic potentials (MLIPs) 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.