Adaptive Biasing Potential Method: Difference between revisions
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=Introduction= | =Introduction= | ||
The Adaptive Biasing Potential (ABP) method implemented in PMFLib follows the mollified density-of-states formulation [1]. During the simulation, | The Adaptive Biasing Potential (ABP) method implemented in PMFLib follows the mollified density-of-states formulation [1]. During the simulation, ABP uses the selected collective variables to construct a discretised, mollified population, which is then used to compute the mollified free energy and the corresponding adaptive biasing force. The directly obtained free-energy estimate is therefore the mollified free energy, not the exact free-energy surface. To recover the final free energy, the mollification error must be removed by post-processing, typically via deconvolution. In PMFLib, this correction and reconstruction of the free-energy profile are performed with the [[abp-energy]] utility. ABP also supports the Multiple-Walker Approach, in which several simulations contribute to a shared accumulator through the MWA server, accelerating the construction of the mollified population and the convergence of the adaptive bias. | ||
=Documentation= | =Documentation= | ||
Latest revision as of 13:27, 20 June 2026
Navigation: Documentation / Methods / Adaptive Biasing Potential Method
Introduction
The Adaptive Biasing Potential (ABP) method implemented in PMFLib follows the mollified density-of-states formulation [1]. During the simulation, ABP uses the selected collective variables to construct a discretised, mollified population, which is then used to compute the mollified free energy and the corresponding adaptive biasing force. The directly obtained free-energy estimate is therefore the mollified free energy, not the exact free-energy surface. To recover the final free energy, the mollification error must be removed by post-processing, typically via deconvolution. In PMFLib, this correction and reconstruction of the free-energy profile are performed with the abp-energy utility. ABP also supports the Multiple-Walker Approach, in which several simulations contribute to a shared accumulator through the MWA server, accelerating the construction of the mollified population and the convergence of the adaptive bias.
Documentation
- ABP:Controls
- ABP:Collective Variables
- ABP:Multiple Walker Approach
- ABP:Files
- ABP:Utilities
- ABP:Examples
References
(1) Dickson, B. M.; Legoll, F.; Lelièvre, T.; Stoltz, G.; Fleurat-Lessard, P. Free Energy Calculations: An Efficient Adaptive Biasing Potential Method. J. Phys. Chem. B 2010, 114 (17), 5823–5830. https://doi.org/10.1021/jp100926h.