Autor: |
Ju F; Microsoft Research AI for Science, Beijing 100080, China., Wei X; Microsoft Research AI for Science, Beijing 100080, China., Huang L; Microsoft Research AI for Science, Beijing 100080, China., Jenkins AJ; Microsoft Azure Quantum, Redmond, Washington 98052, United States., Xia L; Microsoft Research AI for Science, Beijing 100080, China., Zhang J; Microsoft Research AI for Science, Beijing 100080, China., Zhu J; Microsoft Research AI for Science, Beijing 100080, China., Yang H; Microsoft Research AI for Science, Shanghai 200232, China., Shao B; Microsoft Research AI for Science, Beijing 100080, China., Dai P; Microsoft Research AI for Science, Beijing 100080, China., Williams-Young DB; Microsoft Azure Quantum, Redmond, Washington 98052, United States., Mayya A; Microsoft Azure Quantum, Redmond, Washington 98052, United States., Hooshmand Z; Microsoft Azure Quantum, Redmond, Washington 98052, United States., Efimovskaya A; Microsoft Azure Quantum, Redmond, Washington 98052, United States., Baker NA; Microsoft Azure Quantum, Redmond, Washington 98052, United States., Troyer M; Microsoft Azure Quantum, Redmond, Washington 98052, United States., Liu H; Microsoft Azure Quantum, Redmond, Washington 98052, United States. |
Abstrakt: |
Density functional theory (DFT) has been a cornerstone in computational chemistry, physics, and materials science for decades, benefiting from advancements in computational power and theoretical methods. This paper introduces a novel, cloud-native application, Accelerated DFT, which offers an order of magnitude acceleration in DFT simulations. By integrating state-of-the-art cloud infrastructure and redesigning algorithms for graphic processing units (GPUs), Accelerated DFT achieves high-speed calculations without sacrificing accuracy. It provides a user-friendly and scalable solution for the increasing demands of DFT calculations in scientific communities. The implementation details, examples, and benchmark results illustrate how Accelerated DFT can significantly expedite scientific discovery across various domains. |