Revealing and exploiting hierarchical material structure through complex atomic networks
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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,
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2057-3960
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Engineering and Physical Sciences Research Council (EP/P022596/1)
Engineering and Physical Sciences Research Council (EP/L015552/1)
Gatsby Charitable Foundation (GAT3395/CCD)
The Royal Society (uf120247)
EPSRC (1644501)