Acceleration without\nDisruption: DFT Software as a\nService

Density functional theory (DFT) has been a cornerstone\nin computational\nchemistry, physics, and materials science for decades, benefiting\nfrom advancements in computational power and theoretical methods.\nThis paper introduces a novel, cloud-native application, Accelerated\nDFT, which offers an order of magnitude acceleration in DFT simulations.\nBy integrating state-of-the-art cloud infrastructure and redesigning\nalgorithms for graphic processing units (GPUs), Accelerated DFT achieves\nhigh-speed calculations without sacrificing accuracy. It provides\na user-friendly and scalable solution for the increasing demands of\nDFT calculations in scientific communities. The implementation details,\nexamples, and benchmark results illustrate how Accelerated DFT can\nsignificantly expedite scientific discovery across various domains.

Publication Details

Journal
Figshare
Published
2024-12-11
DOI
https://doi.org/10.1021/acs.jctc.4c00940.s001
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
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article

Acceleration without\nDisruption: DFT Software as a\nService

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Machine Learning in Materials Science
article

Acceleration without\nDisruption: DFT Software as a\nService

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Abstract

Density functional theory (DFT) has been a cornerstone\nin computational\nchemistry, physics, and materials science for decades, benefiting\nfrom advancements in computational power and theoretical methods.\nThis paper introduces a novel, cloud-native application, Accelerated\nDFT, which offers an order of magnitude acceleration in DFT simulations.\nBy integrating state-of-the-art cloud infrastructure and redesigning\nalgorithms for graphic processing units (GPUs), Accelerated DFT achieves\nhigh-speed calculations without sacrificing accuracy. It provides\na user-friendly and scalable solution for the increasing demands of\nDFT calculations in scientific communities. The implementation details,\nexamples, and benchmark results illustrate how Accelerated DFT can\nsignificantly expedite scientific discovery across various domains.

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Machine Learning in Materials Science
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