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The density of states of continuous models is known to span many orders ofmagnitudes at different energies due to the small volume of phase space nearthe ground state. Consequently, the traditional Wang-Landau sampling which usesthe same trial move for all energies faces difficulties sampling the lowentropic states. We developed an adaptive variant of the Wang-Landau algorithmthat very effectively samples the density of states of continuous models acrossthe entire energy range. By extending the acceptance ratio method of Bouzida,Kumar, and Swendsen such that the step size of the trial move and acceptancerate are adapted in an energy-dependent fashion, the random walker efficientlyadapts its sampling according to the local phase space structure. TheWang-Landau modification factor is also made energy-dependent in accordancewith the step size, enhancing the accumulation of the density of states.Numerical simulations show that our proposed method performs much better thanthe traditional Wang-Landau sampling.

Dynamically Optimized Wang-Landau Sampling with Adaptive Trial Moves and Modification Factors