The arithmetic path collective variable in CV space uses the same formula as the one proposed by Branduardi[55] et al., except that it computes and in CV space instead of RMSDs in Cartesian space. Moreover, this implementation allows different coefficients for each CV components as described in [56]. Assuming a path is composed of reference frames and defined in an -dimensional CV space, then the equations of and of the path are
This colvar component computes the variable.
List of keywords (see also for additional options):
This colvar component computes the variable. Options are the same as in .
The usage of azpathCV and aspathCV is illustrated below:
colvar {
# Progress along the path
name as
# Path defined by the CV space of two dihedral angles
aspathCV {
pathFile ./path.txt
weights {1.0 1.0}
lambda 0.005
dihedral {
name 001
group1 {atomNumbers {5}}
group2 {atomNumbers {7}}
group3 {atomNumbers {9}}
group4 {atomNumbers {15}}
}
dihedral {
name 002
group1 {atomNumbers {7}}
group2 {atomNumbers {9}}
group3 {atomNumbers {15}}
group4 {atomNumbers {17}}
}
}
}
colvar {
# Distance from the path
name az
azpathCV {
pathFile ./path.txt
weights {1.0 1.0}
lambda 0.005
dihedral {
name 001
group1 {atomNumbers {5}}
group2 {atomNumbers {7}}
group3 {atomNumbers {9}}
group4 {atomNumbers {15}}
}
dihedral {
name 002
group1 {atomNumbers {7}}
group2 {atomNumbers {9}}
group3 {atomNumbers {15}}
group4 {atomNumbers {17}}
}
}
}
The path collective variables defined by Branduardi et al. [55] are based on RMSDs in Cartesian coordinates. Noting the RMSD between the current set of Cartesian coordinates and those of image number of the path:
(13.17) |
(13.18) |
where is the smoothing parameter.
These coordinates are implemented as Tcl-scripted combinations of rmsd components. The implementation is available as file colvartools/pathCV.tcl, and an example is provided in file examples/10_pathCV.namd of the Colvars public repository. It implements an optimization procedure, whereby the distance to a given image is only calculated if its contribution to the sum is larger than a user-defined tolerance parameter. All distances are calculated every freq timesteps to update the list of nearby images.