NVIDIA 解锁 Tensor Core 性能，加速机器学习原子间势能模型
Unlocking Tensor Core Performance for Machine Learning Interatomic Potentials
NVIDIA 针对机器学习原子间势能（MLIP）模型优化 Tensor Core 性能，以缓解原子模拟中 DFT 等量子力学方法成本高、难以扩展到大型系统的问题。原子模拟可用于研究化学反应、相变和机械失效等行为，但长期受制于成本与精度的权衡。
Atomistic simulations help researchers understand how molecules and materials behave, from chemical reactions and phase transitions to mechanical failure. These...
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…
来源：NVIDIA Developer Blog · developer.nvidia.com