NVIDIA 解锁 Tensor Core 性能以加速机器学习原子势能计算
Unlocking Tensor Core Performance for Machine Learning Interatomic Potentials
NVIDIA 通过优化 Tensor Core 性能，显著加速机器学习原子势能计算。其技术可提升量子力学方法如密度泛函理论（DFT）的计算效率，平衡成本与精度。该方案适用于大系统或长时间模拟，但具体参数规模、benchmark 分数及可用性（开源/闭源/API）原文未提供。
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