Complexity Reduction in Density Functional Theory Calculations of Large Systems: System Partitioning and Fragment Embedding
Complexity Reduction in Density Functional Theory Calculations of Large Systems: System Partitioning and Fragment Embedding
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DOI：
10.1021/acs.jctc.9b01152
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发表时间：
2020-05-12
影响因子：
5.5
通讯作者：
Genovese, Luigi
中科院分区：
文献类型：
--
作者：
Dawson, William;Mohr, Stephan;Genovese, Luigi
随着用于执行Kohn-Sham密度泛函理论的低阶标度方法的发展，现在可以对包含数万个原子的系统执行完全量子力学计算。然而，随着系统规模的增加，复杂性也随之增加，这使得分析如此大的系统并确定紧急属性的原因变得具有挑战性。为了解决这个问题，在本文中，我们提出了一个系统的复杂性降低的方法，可以分解成其组成片段和量化的片段间的相互作用的大系统。这里提出的方法不需要先验信息或用户交互，允许单个工作流自动应用于任何感兴趣的系统。我们将这种方法应用于各种不同的系统，并展示它如何允许新的系统描述符的推导，QM/MM分区方案的设计，以及图形度量的分子和材料的新应用。
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.