Diffusion in the Presence of Correlated Returns in a Two-dimensional Energy Landscape and non-Monotonic Friction Dependence: Examination of Simulation Results by a Random Walk Model
Subhajit Acharya, Biman Bagchi

TL;DR
This study combines theory and simulations to analyze how correlated returns and noise influence diffusion in a two-dimensional energy landscape, revealing non-monotonic friction dependence and the role of dimensionality.
Contribution
It introduces a comprehensive analysis of diffusion in multidimensional energy landscapes considering correlated returns, noise effects, and compares simulation results with theoretical predictions.
Findings
Diffusion exhibits non-monotonic dependence on friction at intermediate damping.
Correlation persists in trajectories even without noise, affecting diffusion.
Theoretical predictions align well with simulations at high friction, less so at intermediate friction.
Abstract
Diffusion in a multidimensional energy surface with minima and barriers is a problem of importance in statistical mechanics and also has wide applications, such as protein folding. To understand it in such a system, we carry out theory and simulations of a tagged particle moving on a two-dimensional periodic potential energy surface, both in the presence and absence of noise. Langevin dynamics simulations at multiple temperatures are carried out to obtain the diffusion coefficient of a solute particle. Friction is varied from zero to large values. Diffusive motion emerges in the limit of long times, even in the absence of noise, although the trajectory is found to remain correlated over a long time. This correlation is manifested in correlated returns to the starting minima following a scattering by surrounding maxima. Noise destroys this correlation, induces chaos, and increases…
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Taxonomy
Topicsstochastic dynamics and bifurcation · Advanced Thermodynamics and Statistical Mechanics · Protein Structure and Dynamics
