The 2-D Cluster Variation Method: Topography Illustrations and Their Enthalpy Parameter Correlations

One of the biggest challenges in characterizing 2-D image topographies is finding a low-dimensional parameter set that can succinctly describe, not so much patterns themselves, but nature these patterns. The cluster variation method (CVM), introduced by Kikuchi 1951, characterize very local pattern distributions using configuration variables, identifying nearest-neighbor, next-nearest-neighbor, and triplet configurations. Using CVM, we just two parameters; activation enthalpy (ε0) interaction (ε1). Two different initial (“scale-free-like” “extreme rich club-like”) were each computationally brought to CVM free energy minimum, for case where was zero values used enthalpy. results are: (1) computational variable differ significantly from analytically-predicted well before ε1 approaches known divergence as ε1→0.881, (2) range potentially useful values, favoring clustering like-with-like units, limited region ε0<3 ε1<0.25, (3) systems are minimum show interesting visual features, such extended “spider legs” connecting previously unconnected “islands,” evolution “peninsulas” what solid masses.