Revealing and exploiting hierarchical material structure through complex atomic networks
Sebastian E. Ahnert, William P. Grant, Chris J. Pickard

TL;DR
This paper introduces a hierarchical modular network approach to simplify and analyze complex atomic structures, enabling faster discovery of novel materials and insights into atomic configurations.
Contribution
The authors present a novel method that decomposes atomic networks into hierarchical modules, reducing configuration complexity and aiding material discovery.
Findings
Simplifies complex crystal structures through modular decomposition.
Identifies a potential new allotrope of boron with 56 atoms.
Accelerates structure search processes for atomic ensembles.
Abstract
One of the great challenges of modern science is to faithfully model, and understand, matter at a wide range of scales. Starting with atoms, the vastness of the space of possible configurations poses a formidable challenge to any simulation of complex atomic and molecular systems. We introduce a computational method to reduce the complexity of atomic configuration space by systematically recognising hierarchical levels of atomic structure, and identifying the individual components. Given a list of atomic coordinates, a network is generated based on the distances between the atoms. Using the technique of modularity optimisation, the network is decomposed into modules. This procedure can be performed at different resolution levels, leading to a decomposition of the system at different scales, from which hierarchical structure can be identified. By considering the amount of information…
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