Identifying "communities" within energy landscapes
Claire P. Massen, Jonathan P. K. Doye

TL;DR
This paper explores the community structure of energy landscape networks using advanced algorithms, revealing insights into landscape funnels and challenging interpretations of modularity in different network types.
Contribution
It introduces refined modularity measures and improved optimization algorithms for detecting communities in energy landscape networks, including complex and heterogeneous cases.
Findings
Community detection aligns with landscape funnel concepts.
Refined modularity improves community identification accuracy.
High modularity observed in lattice networks raises interpretative questions.
Abstract
Potential energy landscapes can be represented as a network of minima linked by transition states. The community structure of such networks has been obtained for a series of small Lennard-Jones clusters. This community structure is compared to the concept of funnels in the potential energy landscape. Two existing algorithms have been used to find community structure, one involving removing edges with high betweenness, the other involving optimization of the modularity. The definition of the modularity has been refined, making it more appropriate for networks such as these where multiple edges and self-connections are not included. The optimization algorithm has also been improved, using Monte Carlo methods with simulated annealing and basin hopping, both often used successfully in other optimization problems. In addition to the small clusters, two examples with known heterogeneous…
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