機器學習原子間勢如何發揮 Tensor Core 效能

Unlocking Tensor Core Performance for Machine Learning Interatomic Potentials

AI 導讀

NVIDIA 探討如何提升 Tensor Core 在機器學習原子間勢上的效能。原子模擬可協助研究人員瞭解分子和材料的行為；DFT 能準確計算能量和作用力，但計算成本往往限制其在大型系統或長時間模擬中的應用。

正文

Colorful matrix-like blocks transition into a lattice of spheres, illustrating computation for atomistic simulation. 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…

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