Acceleration without Disruption: DFT Software as a Service
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.
Authors
- David B. Williams‐Young (ORCID: https://orcid.org/0000-0003-2735-3706)
- Bin Shao (ORCID: https://orcid.org/0000-0002-6568-7996)
- Fusong Ju (ORCID: https://orcid.org/0000-0002-0467-7858)
- Matthias Troyer (ORCID: https://orcid.org/0000-0002-1469-9444)
- Jianwei Zhu (ORCID: https://orcid.org/0000-0002-2210-6442)
- Andrew J. Jenkins (ORCID: https://orcid.org/0000-0002-1014-5225)
- Xinran Wei
- Nathan Baker (ORCID: https://orcid.org/0000-0002-5892-6506)
- Zahra Hooshmand
- Alexandra Efimovskaya
- Han Yang (ORCID: https://orcid.org/0009-0000-0414-9417)
- Hongbin Liu (ORCID: https://orcid.org/0000-0001-9011-1182)
- Jia Zhang (ORCID: https://orcid.org/0000-0002-4158-4546)
- Huang Lin (ORCID: https://orcid.org/0009-0003-5948-5209)
- Leo Xia
- Peggy Dai
- Ashwin Mayya
Institutions
- Microsoft (United States) (US)
- Microsoft Research Asia (China) (CN)
Publication Details
- Journal
- Journal of Chemical Theory and Computation
- Published
- 2024-12-11
- DOI
- https://doi.org/10.1021/acs.jctc.4c00940
- Citations
- 10
- Primary Topic
- Parallel Computing and Optimization Techniques
- Type
- article
- Field-Weighted Citation Impact
- 4.89