Speaker
Description
The Wang-Landau algorithm is a powerful Monte Carlo method designed to reconstruct self-consistently the density of states of a system by uniformly exploring the energy spectrum. This approach allows the efficient sampling of otherwise rare configurations, making it particularly well-suited for systems with rugged energy landscapes. In this work, we present a continuous variant of the Wang-Landau algorithm tailored for soft matter systems, such as molecular liquids and polymer chains.
Our approach incorporates a novel, biased generation of proposal moves based on Brownian dynamics. This choice enables controlled diffusion across the energy spectrum, ensuring efficient exploration while maintaining detailed balance. Additionally, we introduce a replica-exchange-like mechanism to further enhance sampling efficiency and avoid kinetic traps due to ergodicity breaking. By allowing swaps between system replicas at different energy levels, this mechanism accelerates convergence and improves the accuracy of the density-of-states estimation. This adaptation is particularly beneficial for studying thermodynamic properties of soft matter systems, where energy fluctuations occur over a broad range and traditional methods often struggle to capture rare but physically relevant states.
Through extensive simulations, we demonstrate that our version of the Wang-Landau algorithm provides a robust framework for studying thermodynamic properties, free energy landscapes, and phase transitions in complex molecular systems. Our findings suggest that this approach can serve as a valuable tool in computational physics, opening new avenues for efficiently exploring the statistical mechanics of soft matter systems.
| Role | Master/PhD student |
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