University of Cambridge > Talks.cam > Machine learning in Physics, Chemistry and Materials discussion group (MLDG) > Discovering new materials by detecting modules in atomic networks

Discovering new materials by detecting modules in atomic networks

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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 decompositions of these structures vastly simplifies configuration space, which in turn can be use 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, γ-B28.

This talk is part of the Machine learning in Physics, Chemistry and Materials discussion group (MLDG) series.

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