Machine learning interatomic potentials reduce computational expenses compared to quantum mechanics, but high precision requirements typically restrict models from exploiting the full throughput of modern NVIDIA Tensor Cores. Researchers evaluated operand-splitting methods that run lower-precision parts through Tensor Cores and recombine them to match FP32 accuracy. Testing was conducted on the compute-heavy Point Edge Transformer developed by Lab COSMO at EPFL. These integration strategies accelerate runtime while safeguarding crucial properties like energy conservation, potential-energy smoothness, and structural relaxation.
Key points
The evaluated Tensor Core techniques accelerate model inference speeds without requiring…: The evaluated Tensor Core techniques accelerate model inference speeds without requiring architectural modifications or retraining.
Energy conservation across molecular dynamics simulations remains intact despite processi…: Energy conservation across molecular dynamics simulations remains intact despite processing inputs via lower-precision Tensor Core operations.
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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…
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