pfadsampling

Pfadsampling, also known as path sampling, is a computational technique used primarily in statistical physics and chemistry to explore the configuration space of a system. It is a form of Monte Carlo simulation that aims to overcome the limitations of standard methods like Metropolis Monte Carlo, particularly when dealing with systems that have high-energy barriers or complex free energy landscapes. The core idea behind pfadsampling is to introduce a series of intermediate or "bridging" states between a starting configuration and a target configuration. These bridging states are typically parameterized by a collective variable or a "reaction coordinate" that describes the progress of a process, such as a chemical reaction or a phase transition.

By simulating the system at various points along this predefined path, pfadsampling allows for a more efficient exploration of the phase space. Instead of relying on rare random events to overcome energy barriers, the simulation is guided through these regions of interest. Common algorithms employed in pfadsampling include umbrella sampling and metadynamics. Umbrella sampling uses biasing potentials to flatten the free energy landscape along the chosen coordinate, enabling more frequent sampling of configurations that would otherwise be improbable. Metadynamics, on the other hand, adds a history-dependent bias to the system, discouraging revisits to already explored configurations and thus promoting exploration of new regions. Pfadsampling is particularly useful for calculating thermodynamic properties, reaction rates, and free energy differences, providing valuable insights into the dynamics and behavior of complex molecular systems.