flatIGW — An inverse algorithm to compute the density of states of lattice self avoiding walks

• Published in: Physica A 2011-04, Vol.390 (7), p.1258-1268
• Main Authors: Ponmurugan, M., Sridhar, V., Narasimhan, S.L., Murthy, K.P.N.
• Format: Article
• Language: English
• Subjects: Algorithms; Asymptotic properties; Density of states; Energy histogram; Extrapolation; Histograms; Interacting Growth Walk; Inverse; Lattices; Mathematical analysis; Monte Carlo simulation; Self avoiding walk; Theta point; Zeros of partition function
• ISSN: 0378-4371; 1873-2119
• DOI: 10.1016/j.physa.2010.11.023

We show that the Density of States (DoS) for lattice Self Avoiding Walks can be estimated by using an inverse algorithm, called flatIGW, whose step-growth rules are dynamically adjusted by requiring the energy histogram to be locally flat. Here, the (attractive) energy associated with a configuration is taken to be proportional to the number of non-bonded nearest neighbor pairs (contacts). The energy histogram is able to explicitly direct the growth of a walk because the step-growth rule of the Interacting Growth Walk (Narasimhan et al. (2003)  [5]) samples the available nearest neighbor sites according to the number of contacts they would make. We have obtained the complex Fisher zeros corresponding to the DoS, estimated for square lattice walks of various lengths, and located the θ temperature by extrapolating the finite size values of the real zeros to their asymptotic value, ∼1.49 (reasonably close to the known value, ∼1.50 (Barkema et al. (1998)  [14]). ► Inverse algorithm to estimate Density of States (DoS) of lattice Self Avoiding Walks. ► Algorithm driven by local flatness requirement of the energy histogram. ► Complex Fisher zeros corresponding to DoS for square lattice walks obtained. ► θ -temperature estimated by analysis of Fisher zeros. ► Agreement with the best known value validates the algorithm.