Adaptive Biasing Force Method: Difference between revisions
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where <math>\bold h</math> is the interval size also called a bin, <math>N_b</math> is the number of samples collected in a bin centered at <math>\boldsymbol \xi^{*}</math>, and <math>N_{corr}</math> is a statistical inefficiency due to correlation in time series. | where <math>\bold h</math> is the interval size also called a bin, <math>N_b</math> is the number of samples collected in a bin centered at <math>\boldsymbol \xi^{*}</math>, and <math>N_{corr}</math> is a statistical inefficiency due to correlation in time series. | ||
Therefore, each CV involved in ABF simulations must be discretized by specifying an interval in which the sampling is performed and the number of intervals (bins) for discretization, for further details, see [[ABF:Collective variables]]. The increasing number of bins improves the accuracy of Equation 5 but it also increases the noise because of a smaller number of samples collected in a bin. A reasonable compromise is the number of bins, which leads to 0.1 Å or 1-2° bin widths. | Therefore, each CV involved in ABF simulations must be discretized by specifying an interval in which the sampling is performed and the number of intervals (bins) for discretization, for further details, see [[ABF:Collective variables]]. The increasing number of bins improves the accuracy of Equation 5 and subsequently the quality of the integrated free energy (Equation 1) but it also increases the noise because of a smaller number of samples collected in a bin. A reasonable compromise is the number of bins, which leads to 0.1 Å or 1-2° bin widths. | ||
===Adaptive Bias=== | ===Adaptive Bias=== | ||