Acceleration without Disruption: DFT Software as a Service
Fusong Ju, Xinran Wei, Huang Lin, Andrew J. Jenkins, Leo Xia, Jia Zhang, Jianwei Zhu, Han Yang, Bin Shao, Peggy Dai, David B. Williams‐Young, Ashwin Mayya, Zahra Hooshmand, Alexandra Efimovskaya, Nathan Baker, Matthias Troyer, Hongbin Liu · Journal of Chemical Theory and Computation · 2024
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.