ppafm 0.4.0

Classical force field model for simulating atomic force microscopy images.

Probe-Particle Model

Simple and efficient simulation 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 the deflection of the probe particle attached to the tip, where the probe particle represents a flexible tip apex (typically CO molecule, but also e.g. Xe, Cl-, H2O and others). The Python package is named as ppafm.

Installation

The standard way of installing ppafm is:

$ pip install ppafm

This should install the package and all its dependencies.

The most up-to-date installation guide can be found on the dedicated wiki page.

Command line interface (CLI)

Once ppafm is installed, a collection of command-line tools will become available to the user. Their names start with the ppafm- prefix. To get more information about a given tool, run it with the -h option, e.g.:

ppafm-generate-ljff -h

For more information, please consult the dedicated page on the command line interface of ppafm.

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 the dedicated section on the Install ppafm wiki page. To know more about the GUI interface, please consult the ppafm GUI wiki page.

Usage examples

We provide a set of examples in the examples directory. To run them, navigate to the directory and run the run.sh script. For example:

$ cd examples/PTCDA_single
$ ./run.sh

You can study the script to see how to run the simulation. Also, have a look at the params.ini file and the wiki to see how to set up the simulation parameters.

Once the simulation is finished, several files and folders will be created.

See also the tutorial for using the Python API for running GPU-accelerated simulations.

Run GPU generator for machine learning

• examples/CorrectionLoopGraphene use GPU accelerated ppafm to iteratively improve the estimate of molecular geometry by comparing simulated AFM images with reference. This is a 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 improvement.
• examples/Generator quickly 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).

ppafm simulation models and implementations

Since 2014 ppafm developed into the toolbox of various methodologies adjusted for a particular use case.

1. CPU version: - Original implementation using Python & C/C++. It can simulate a typical AFM experiment (3D stack of AFM images) in a matter of a few minutes. It is the base version for the development of new features and methodology. All available simulation models are implemented in this version, including:
   1. Point charge electrostatics + Lennard-Jones: Original fully classical implementation allows the user to set up calculation without any ab initio input by specifying atomic positions, types and (optionally) charges.
   2. Hartree-potential electrostatics + Lennard-Jones: Electrostatics is considerably improved by using Hartree potential from DFT calculation 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.
   3. Hartree-potential electrostatics + Density overlap: Further accuracy improvement is achieved when Pauli repulsion between electron shells of atoms is modelled 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 the simulation of molecules with electron pairs (-NH-, -OH, =O group), triple bonds and other strongly concentrated electrons.
2. GPU version: - Version specially designed for the generation of training data for machine learning. Implementation using pyOpenCL can parallelize the evaluation of forcefield and relaxation of probe-particle positions over hundreds or thousands of stream processors of the graphical accelerator. The 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.
3. GUI @ GPU - The speed of GPU implementation enables 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 for experimentalists trying to identify and interpret the structure and configuration of molecules in experiments on the fly while running the experiment.

Other branches

• master_backup is the old master branch that was recently significantly updated and named main. For users who miss the old master branch, we provided a backup copy. However, this version is very old and its use is discouraged.
• PhotonMap implements the latest developments concerning sub-molecular scanning probes combined with Raman spectroscopy (TERS) and fluorescent spectroscopy (LSTM).
• complex_tip is a modification of the Probe-Particle Model with 2 particles that allows a better fit to experimental results at the cost of additional fitting parameters.

For contributors

If you miss some functionality or have discovered issues with the latest release - let us know by creating an issue. If you would like to contribute to the development of the ppafm code, please read the Developer's Guide wiki page. Small improvements in the documentation or minor bug fixes are always welcome.

Further information

• Wiki: https://github.com/Probe-Particle/ProbeParticleModel/wiki
• Python API documentation: https://ppafm.readthedocs.io/en/latest/

Publications describing the Probe-Particle Model

If you have used ppafm in your research, please cite the following articles:

• Niko Oinonen, Aliaksandr V. Yakutovich, Aurelio Gallardo, Martin Ondracek, Prokop Hapala, Ondrej Krejci, Advancing Scanning Probe Microscopy Simulations: A Decade of Development in Probe-Particle Models, Comput. Phys. Commun. 305, 109341 - Available online 10 August 2024
• Prokop 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 2014
• Prokop 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

License

MIT