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首页> 外文期刊>Journal of chemical theory and computation: JCTC >Complexity Reduction in Density Functional Theory Calculations of Large Systems: System Partitioning and Fragment Embedding
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Complexity Reduction in Density Functional Theory Calculations of Large Systems: System Partitioning and Fragment Embedding

机译:大型系统密度功能理论计算的复杂性降低:系统分区和碎片嵌入

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摘要

With the development of low order scaling methods for performing Kohn-Sham density functional theory, it is now possible to perform fully quantum mechanical calculations of systems containing tens of thousands of atoms. However, with an increase in the size of the system treated comes an increase in complexity, making it challenging to analyze such large systems and determine the cause of emergent properties. To address this issue, in this paper, we present a systematic complexity reduction methodology which can break down large systems into their constituent fragments and quantify interfragment interactions. The methodology proposed here requires no a priori information or user interaction, allowing a single workflow to be automatically applied to any system of interest. We apply this approach to a variety of different systems and show how it allows for the derivation of new system descriptors, the design of QM/MM partitioning schemes, and the novel application of graph metrics to molecules and materials.
机译:随着用于执行Kohn-Maf密度功能理论的低阶缩放方法的开发,现在可以对包含数万个原子的系统进行完全量子力学计算。然而,随着系统所处的规模增加,复杂性的增加,使得分析这种大型系统并确定紧急性质的原因挑战。为了解决这个问题,在本文中,我们提出了一种系统的复杂性降低方法,可以将大型系统分解为它们的组成片段并量化互换相互作用。此处提出的方法不需要先验信息或用户交互,允许自动应用于任何感兴趣系统的单个工作流程。我们将这种方法应用于各种不同的系统,并展示它是如何推导新系统描述符的推导,QM / MM分区方案的设计,以及图形指标对分子和材料的新颖应用。

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