Adaptive Biasing Potential Method: Difference between revisions
Created page with "Navigation: Documentation / Methods / Adaptive Biasing Potential Method ---- __NOEDITSECTION__ ==Contents== * Adaptive Biasing Potential Method#Description|ABP:D..." |
No edit summary |
||
| (4 intermediate revisions by the same user not shown) | |||
| Line 1: | Line 1: | ||
Navigation: [[Documentation]] / [[Methods]] / [[Adaptive Biasing Potential Method]] | Navigation: [[Documentation]] / [[Methods]] / [[Adaptive Biasing Potential Method]] __NOEDITSECTION__ | ||
---- | ---- | ||
=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:Controls]] | ||
* [[ABP:Collective | * [[ABP:Collective Variables]] | ||
* [[ABP: | * [[ABP:Multiple Walker Approach]] | ||
* [[ABP: | * [[ABP:Files]] | ||
* [[ABP:Utilities]] | * [[ABP:Utilities]] | ||
* [[ABP:Examples]] | * [[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. <i>J. Phys. Chem. B</i> <b>2010</b>, <i>114 (17)</i>, 5823–5830. [https://doi.org/10.1021/jp100926h https://doi.org/10.1021/jp100926h]. | |||
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.