NVIDIA 如何为机器学习原子间势释放 Tensor Core 性能
Unlocking Tensor Core Performance for Machine Learning Interatomic Potentials
NVIDIA 技术博客介绍如何为机器学习原子间势（MLIP）释放 Tensor Core 性能。文章指出，DFT 等量子力学方法精度高但计算成本大，限制了大体系与长时间模拟的应用。
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 Technical Blog · developer.nvidia.com