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Table of Contents
Sloppyfile
The sloppyfile.uq output by the ensemble method compilation contains the list of accepted MCMC potentials, see uncertainty quantification.
Format
The file begins by outputting the calculated Hessian followed by rows listing its eigenvalues and eigenvectors in the format eigenvalue, eigenvector (e1,e2,…,eN)
. Then the accepted MCMC steps are listed. with the final three output columns for performance analysis purposes.
id
an identifier for the potential (the best fit potential parameter set is the first output in the following part of the output file, with an id
of 0
.)
param_1 param_2 … param_N
the potential parameter set for each ensemble member
cost
the fitting cost for the potential
weight
the corresponding potential weight (this is the number of MCMC trial steps proposed before the next potential is accepted)
accepted
a flag of value 1
denotes that the step was accepted (users can have potfit output all unaccepted steps when compiling with the -debug
option, see compiling, with unsuccessful attempts referred to by a flag of 0
in this column)
attempts
the total number of attempted MCMC moves, used to calculate the acc_prob
(obviously when generating a potential ensemble users should let the Markov chain burn is to attain the correct acceptance probability before attempting to tune this using the R value, acceptance_rescaling
input parameter, see input parameters)
acc_prob
proportion of accepted MCMC potential parameter sets
Example
An example output for a fictitious LJ potential fit is illustrated below:
#------------------------------------------------------ # hessian: # 804480.9 713749.4 # 713749.4 1411542.1 #------------------------------------------------------ #------------------------------------------------------ # eigenvalue, eigenvector (e1, e2, ..., eN) # 332402.6 0.83406 -0.551658 # 1883620.4 0.55165 0.834069 #------------------------------------------------------ # id param_1 param_2 cost weight accepted attempts acc_prob 0 0.6830 2.1120 62.5 10 1 10 0.10 1 0.6808 2.1137 63.8 10 1 20 0.10