NVIDIA 解锁 Tensor Core 性能加速机器学习原子间势
Unlocking Tensor Core Performance for Machine Learning Interatomic Potentials
NVIDIA 发布技术方案，用 Tensor Core 加速机器学习原子间势（MLIP）计算，以缓解原子尺度模拟中量子力学方法（如 DFT）成本高、难以扩展到大规模系统的问题。该方案针对 MLIP 的能量与力计算负载，目标是在保持精度的同时提升模拟吞吐。
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