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Probe particle model code is supposed to simulate AFM ( and to some extent STM and inelastic-STM) images obtained with tip modified by an atom or small molecule (such as Xe, CO, CH4, H2). In all cases the actual particle (atom or molecule) is replaced by a spherical model particle and classical potential (Lenard-Jones) is used for description of Pauli repulsion and Van der Waals attraction.
New code is written in C/Python and can operate in framework of Lennard-Jones forces as well as electrostatic forces, if necessary.
Note: The documentation is for “dev” branch
It can be downloaded from http://github.com/ProkopHapala/ProbeParticleModel/tree/dev or simply from terminal by running this commands:
git clone http://github.com/ProkopHapala/ProbeParticleModel/ cd ProbeParticleModel git checkout dev
python-2.7; python-2.7-numpy(-1.8); gcc(-4.8); python-2.7-matplotlib(-1.3) - for plotting of figures;
module add python27-modules-intel module add scipy-0.17.1-py2.7.10
And don't forget to add:
import matplotlib as mpl; mpl.use('Agg');
on the 3rd line of plot_results.py, if it is not allready in the script and if you want to use it on a cluster. This makes it run without Xserver (e.g. on supercomputer) # see http://stackoverflow.com/questions/4931376/generating-matplotlib-graphs-without-a-running-x-server
The geometry of the sample can be read from *.bas or *.xyz files, which has to have xyz extension!!! Here we show part of the geometry file from example attached to the model:
71 C 0.165 0.165 0.165 -0.0132352941 C 2.2987 1.39372 0.16643 -0.0132352941 C 4.4293 2.62743 0.16468 -0.0132352941 C 6.56208 3.85683 0.16431 -0.0132352941 C 8.69719 5.08888 0.16545 -0.0132352941 ...
Which is in format:
Number of atoms optional line Symbol/or/Z-of-element x y z charge-optional
The Lennard-Jones potential needed for calculations can be created by running:
python PATH_TO_YOUR_PROBE_PARTICLE_MODEL/generateLJFF.py -i YOUR_INPUT_FILE.xyz
If charges are also in the input file add “-q” flag to create electrostatic field.
Optionally, the geometry can be also read from *.xsf or *.cube file, too. The command for creation of L-J force field then looks like:
python PATH_TO_YOUR_PROBE_PARTICLE_MODEL/generateLJFF.py -i YOUR_INPUT_FILE.xsf
or
python PATH_TO_YOUR_PROBE_PARTICLE_MODEL/generateLJFF.py -i YOUR_INPUT_FILE.cube
If an electrostatic Hartree potential is obtained from some DFT calculations, it can be read *.xsf or *.cube files. The electrostatic force field is created by running:
python PATH_TO_YOUR_PROBE_PARTICLE_MODEL/generateLJFF.py -i YOUR_INPUT_FILE.xsf
If default parameters are used, than you have monopole represented by an Gaussian cloud of charge with its FWHM of 0.7 Ǎ. The monopole can be changed to non-tilting dipoles or quadrupoles by adding flag: -t type, where type ∈ {s,px,py,pz,dx2,dy2,dz2,dxy,dxz,dyz}; s stands for monopole (default), p for dipoles, d for quadrupoles. The FWHM of the Gaussian cloud can be changed by adding flag: -s FWHM.
This files contains all important information about the scan. Here we show an example of it:
probeType 8 # atom type of ProbeParticle (to choose L-J potential ),e.g. 8 for CO, 54 for Xe charge 0.0 # effective charge of probe particle [e] stiffness 0.20 0.20 20.00 # [N/m] harmonic spring potential (x,y,R) components, x,y is bending stiffnes, R particle-tip bond-length stiffnes, r0Probe 0.0 0.0 4.00 # [Å] equilibirum position of probe particle (x,y,R) components, R is bond length, x,y introduce tip asymmetry PBC True # Periodic boundary conditions ? [ True/False ] gridN 240 240 200 # Grid division around each cell axis; Not necessary - if it is not here a 0.1 division is applied gridA 23.10994667 0.000000 0.000000 # lattice vector of the cell; If geometry is read from .xsf or .cube gridB 11.55497333 20.01380089 0.000000 # !!!! If geometry is read from *.xyz, but electrostatics from .xsf or .cube than gridN and gridA/B/C has gridC 0.000000 0.000000 20.000000 # to be in agreement with the harteee potential. Also the shift vector in the cube file has to be 0.0 0.0 0.0 !!!! scanMin 00.0 00.0 10.0 # start of scanning (x,y,z) (z should be something like: the highest atom of the sample + R + 2.5) scanMax 30.0 22.0 15.0 # end of scanning (x,y,z) scanStep 0.1 0.1 0.10 # steps of scan (dx, dy, dz) Amplitude 1.0 # [Å] oscillation amplitude for conversion Fz->df
With having L-J and electrostatic forces made by generating scripts, you can tryu to run an AFM scan via:
python PATH_TO_YOUR_PROBE_PARTICLE_MODEL/relaxed_scan.py
which run the scan with charge Q and lateral stiffness K written in params.ini. The results - Fz force acting on the tip is saved in Q?.??K?.?? directory as an OutFz.xsf file. The df results for constant height scans can be plotted by:
python PATH_TO_YOUR_PROBE_PARTICLE_MODEL/plot_results.py --df
that plot results for oscillation written in params.ini. The results - df_???.png maps for each tip height - are in directory: Q?.??K?.??/Amp?.??
optionally WSxM files for each height can be written by putting flag - -WSxM behind the plotting command. Outputs are df_???.xyz files with WSxM head and x y df data. If - -save_df flag is applied the df data are stored in df.xsf file. Flag - -atoms used simultaneously with - -df puts positions of atoms of the sample saved in input_plot.xyz into df_???.png. The flag - -bonds ads into the maps also lines connecting close-by atoms.
If a flag - -pos is applied for both commands (scanning & plotting) than xy positions of the relaxing Probe Particle (PP) are shown in xy_???.png as a red dots, while the gray scale on the background maps represent z position of the PP (brighter - higher).
Examples of df simulation is already in the downloaded/cloned repository in folder examples/. You should try to run these examples before going to your own stuff, in order to see that the code is working on your machine.
python PATH_TO_YOUR_PROBE_PARTICLE_MODEL/relaxed_scan.py --krange min max n --qrange min max n
python PATH_TO_YOUR_PROBE_PARTICLE_MODEL/plot_results.py --df --arange min max n