Any publications using this code are requested to cite the following references:

1. Tiwary and Berne, Proc. Natl. Acad. Sci. 113 DOI: 10.1073/pnas.1600917113
2. Smith, Pramanik, Tsai and Tiwary, J. Chem. Phys., 2018, 149 234105; DOI: 10.1063/1.5064856
3. Tiwary and Parrinello, J. Phys. Chem. B, 2015, 119 736; DOI: 10.1021/jp504920s

Use reweight to convert biased trajectories to usable unbiased probability distributions and analyze them with SGOOP. SGOOP may also be used with unbiased data using the rc_eval function.

Run SGOOP on multiple numbers of wells (sgoop.wells) checking each time that the number is self-consistent with the number of wells in the probability distribution (depth > kT). The optimized RC with highest number of self-consistent wells is the optimal RC. For instance, if 2 wells was self-consistent but 3 was not the 2 well RC would be the optimum. These consistency checks will soon be automated and sgoop.wells values will be looped through.

Files needed: Maximum Caliber (unbiased) trajectory, COLVAR file, FES files

The following function is an example of using SGOOP and reweight together. This would be used with an optimization method such as simulated annealing:

import sgoop import reweight as rw import scipy.optimize as opt import numpy as np

def opti_func(rc): p = rw.reweight(rc) # Calculating probability along RC sgoop.set_bins(rc,np.shape(p)[0],rw.s_min,rw.s_max) # Setting bins for maxcal return sgoop.sgoop(rc,p) # Calculating the spectral gap on the given RC.