Package: ppafm
Probe Particle Model (PPM)Simple and efficientsimulation software for high-resolution atomic force microscopy(HR-AFM) and other scanning probe microscopy (SPM) techniques with sub-molecular resolution (STM, IETS, TERS). It simulates deflection of the particle attached to the tip (typically CO molecule, but also e.g. Xe, Cl-, H2O and others).InstallationThe standard way of installingppafmis:$pipinstallppafmThis should install the package and all its dependencies.The most up-to-date installation guide can be found on thededicated wiki page.Command line interface (CLI)Onceppafmis installed, a collection of command-line tools will become available to the user.
Their names start withppafm-preffix.
To get more information about a given tool, run it with-hoption, e.g.:ppafm-generate-ljff-hFor more information, please consult thededicated pageon command line interface ofppafm.Graphical User Interface (GUI)The package comes with a convenient graphical user interface.
Unlike CLI, this interface needs to be explicitly enabled during the installation.
To enable it, check thededicated sectionon theInstall ppafmwiki page.
To know more about the GUI interface, please consultPPAFM GUIwiki page.Usage examplesWe provide a set of examples in theexamplesdirectory.
To run them, navigate to the directory and run therun.shscript.
For example:$cdexamples/PTCDA_single
$./run.shYou can study the script to see how to run the simulation.
Also, have a look at theparams.inifile andthe wikito see how to set up the simulation parameters.Once the simulation is finished, a number of files and folders will be created.Run GPU generator for machine learningexamples/CorrectionLoopGrapheneuse GPU accelerated PPM to iteratively improve the estimate of molecular geometry by comparing simulated AFM images with reference. This is work-in-progress. Currently, modification of estimate geometry is random (Monte-Carlo), while later we plan to develop a more clever (e.g. Machine-Learned) heuristic for more efficient improvment.examples/Generatorquickly generates a batch of simulated AFM images (resp. 3D data stacks) which can be further used for machine learning. Especially in connection with (https://github.com/SINGROUP/ASD-AFM).Flavors of PPMSince 2014 PPM developed into the toolbox of various methodologies adjusted for a particular use case.CPU version:- Original implementation using Python & C/C++. It can simulate a typical AFM experiment (3D stack of AFM images) in ~1 minute. It is the base version for the development of new features and methodology. All available simulation models are implemented in this version, including:Point charge electrostatics + Lennard-Jones:Original fully classical implementation allows the user to set up calculation without any ab-initio input simply by specifying atomic positions, types and charges.Hartree-potential electrostatics + Lennard-Jones:Electrostatics is considerably improved by using Hartree potential from DFT calculation (e.g. LOCPOT from VASP) and using the Quadrupole model for CO-tip. We found this crucial to properly simulate polar molecules (e.g. H2O clusters, carboxylic acids, PTCDA) which exhibit strong electrostatic distortions of AFM images. Thanks to implementation using fast Fourier transform (FFT) this improvement does not increase the computational time (still ~1 minute), as long as the input electrostatic field is accessible.Hartree-potential electrostatics + Density overlap:Further accuracy improvement is achieved when Pauli repulsion between electron shells of atoms is modeled by the overlap between electron density of tip and sample. This repulsive term replaces the repulsive part of Lennard-Jones while the attractive part (C6) remains. This modification considerably improves especially simulation of molecules with electron pairs (-NH-, -OH, =O group), triple bonds and other strongly concentrated electrons. Calculation of the overlap repulsive potential is again accelerated by FFT to achieve minimal computational overhead (2-3 minutes) as long as input densities of tip and sample are available.GPU version:- Version specially designed for generation of training data for machine learning. Implementation usingpyOpenCLcan parallelize the evaluation of forcefield and relaxation of probe-particle positions over hundreds or thousands of stream-processors of the graphical accelerator. Further speed-up is achieved by using hardware accelerated trilinear interpolation of 3D textures available in most GPUs. This allows simulating 10-100 AFM experiments per second on consumer-grade desktop GPU.GPU version is designed to work in collaboration with machine-learning software for AFM (https://github.com/SINGROUP/ASD-AFM) and use various generators of molecular geometry.GUI @ GPU- The speed of GPU implementation also allows to make interactive GUI where AFM images of molecules can be updated on the fly (<<0.1s) on a common laptop computer while the user is editing molecular geometry or parameters of the tip. This provides an invaluable tool especially to experimentalists trying to identify and interpret the structure and configuration of molecules in experiments on-the-fly while running the experiment.Other branchesmaster_backup- Oldmasterbranch was recently significantly updated and namedmain. For users who miss the old master branch, we provided a backup copy. However, this version is very old and its use is discouraged. If you miss some functionality or are not satisfied with the behavior of currentmainbranch please let us know by creating anissue.PhotonMap- implements the latest developments concerning sub-molecular scanning probe combined with Raman spectroscopy (TERS)y and fluorescent spectroscopy (LSTM).complex_tip- Modification of probe-particle model with 2 particles allows a better fit to experimental results at the cost of additional fitting parameters.For developersIf you would like to contribute to the development of the ppafm code, please read theDeveloper's Guidewiki page.Further informationWiki:https://github.com/Probe-Particle/ProbeParticleModel/wikiPython API documentation:https://ppafm.readthedocs.io/en/latest/Notable publications using Probe Particle ModelProkop Hapala, Georgy Kichin, Christian Wagner, F. Stefan Tautz, Ruslan Temirov, and Pavel Jelínek, Mechanism of high-resolution STM/AFM imaging with functionalized tips, Phys. Rev. B 90, 085421 – Published 19 August 2014Prokop Hapala, Ruslan Temirov, F. Stefan Tautz, and Pavel Jelínek, Origin of High-Resolution IETS-STM Images of Organic Molecules with Functionalized Tips, Phys. Rev. Lett. 113, 226101 – Published 25 November 2014,LicenseMIT