Revealing and exploiting hierarchical material structure through complex atomic networks

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 required to represent a given modular decomposition we can furthermore find the most succinct descriptions of a given atomic ensemble. Our straightforward, automatic and general approach is applied to complex crystal structures. We show that modular decomposition of these structures considerably simplifies configuration space, which in turn can be used in discovery of novel crystal structures, and opens up a pathway towards accelerated molecular dynamics of complex atomic ensembles. The power of this approach is demonstrated by the identification of a possible allotrope of boron containing 56 atoms in the primitive unit cell, which we uncover using an accelerated structure search, based on a modular decomposition of a known dense phase of boron, $\gamma$-B${}_{28}$.

25 pages, 7 figures, data available from https://figshare.com/articles/Complex_atomic_networks_Ahnert_Grant_and_Pickard_2017/4780456, code available from https://www.mtg.msm.cam.ac.uk/Codes/AIRSS (see airss/examples/2.3 and 2.4)

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• Navigating the Ti-C-O and Al-C-O ternary systems through theory-driven discovery