为机器学习原子间势能释放 Tensor Core 性能
Unlocking Tensor Core Performance for Machine Learning Interatomic Potentials
NVIDIA 介绍如何让机器学习原子间势能（MLIP）模型用上 Tensor Core 性能。原子尺度模拟长期面临成本与精度的取舍：DFT 等量子力学方法能给出精确能量与力，但计算开销限制了其在大型体系或长时间模拟上的使用。
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