ppafm

Official Resources

• Source Repository: https://github.com/Probe-Particle/ppafm
• Documentation: https://probe-particle.github.io/ppafm/
• PyPI: https://pypi.org/project/ppafm/
• License: Open source (Apache-2.0)

Overview

ppafm (Probe-Particle AFM) is a simple and efficient simulation software for high-resolution atomic force microscopy (HR-AFM) and other scanning probe microscopy (SPM) techniques with sub-molecular resolution. It simulates the deflection of a probe particle (typically CO or Xe) attached to the tip, enabling realistic AFM, STM, IETS, and TERS simulations.

Scientific domain: Atomic force microscopy, scanning probe microscopy, surface science
Target user community: Researchers simulating and interpreting high-resolution AFM and SPM experiments

Theoretical Methods

• Probe-particle model (CO/Xe tip functionalization)
• Classical force field for tip-sample interaction
• Lennard-Jones potentials
• Point-charge electrostatics
• Hartree potential from DFT
• Tersoff-Hamann STM approximation
• Inelastic tunneling (IETS)
• Tip-enhanced Raman spectroscopy (TERS)

Capabilities (CRITICAL)

• High-resolution AFM image simulation
• STM image simulation
• IETS (inelastic electron tunneling spectroscopy)
• TERS (tip-enhanced Raman spectroscopy)
• Kelvin probe force microscopy (KPFM)
• Sub-molecular resolution imaging
• CO/Xe tip functionalization
• Multiple DFT code interfaces
• 3D force map calculation
• Frequency shift calculation

Sources: GitHub repository, Comput. Phys. Commun. 305, 109341 (2024)

Key Strengths

Probe-Particle Model:

• Realistic tip functionalization (CO, Xe, etc.)
• Sub-molecular resolution
• Efficient classical simulation
• Captures key experimental features
• Well-validated against experiment

Multi-Mode SPM:

• AFM, STM, IETS, TERS, KPFM
• Comprehensive SPM simulation
• Consistent model across modes
• Direct comparison with experiment

DFT Integration:

• VASP, QE, FHI-aims, CP2K, GPAW
• Reads DFT-calculated densities
• Hartree potential for electrostatics
• Orbital data for STM

Efficient:

• Fast classical simulation
• GPU acceleration available
• Python API
• PyPI installation

Inputs & Outputs

• Input formats:

  • DFT charge density (VASP LOCPOT, QE rho)
  • DFT Hartree potential
  • DFT orbitals for STM
  • Force field parameters
  • Probe-particle configuration

• Output data types:

  • AFM images (frequency shift maps)
  • STM images
  • IETS spectra and maps
  • TERS spectra
  • KPFM images
  • 3D force maps

Interfaces & Ecosystem

• PPSTM: STM/STS companion code
• VASP: DFT input
• Quantum ESPRESSO: DFT input
• FHI-aims: DFT input
• CP2K: DFT input
• GPAW: DFT input
• Python: Scripting and visualization

Performance Characteristics

• Speed: Very fast (seconds per image)
• Accuracy: Good qualitative agreement with experiment
• System size: Hundreds of atoms
• Memory: Low
• GPU: Available for acceleration

Computational Cost

• AFM image: Seconds to minutes
• 3D force map: Minutes
• STM image: Seconds
• Typical: Very efficient

Limitations & Known Constraints

• Classical model: Not fully quantum mechanical
• Force field: Simplified tip-sample interaction
• No full NEGF: Approximate tunneling
• Parameter dependence: Results depend on probe-particle stiffness
• No chemical bond formation: Cannot simulate bond-breaking

Comparison with Other Codes

• vs PPSTM: ppafm focuses on AFM, PPSTM on STM/STS
• vs cp2k-spm-tools: ppafm is classical model, cp2k-spm is DFT-based
• vs full DFT AFM: ppafm is much faster, less accurate
• Unique strength: Efficient HR-AFM simulation with probe-particle model, multi-mode SPM, multi-DFT-code interface

Application Areas

On-Surface Molecular Imaging:

• Organic molecules on surfaces
• Bond-resolved AFM
• Molecular structure determination
• Intermolecular bonds

2D Materials:

• Graphene, hBN, TMDs
• Moiré patterns
• Defect characterization
• Edge structure

Tip Functionalization:

• CO-tip AFM
• Xe-tip AFM
• Cl-tip imaging
• Tip-induced contrast

Surface Science:

• Adsorption geometry
• Surface reconstruction
• Charge distribution (KPFM)
• Vibrational mapping (IETS/TERS)

Best Practices

Probe-Particle Parameters:

• Calibrate stiffness (k ~ 0.5 N/m for CO)
• Test tip-sample distance
• Compare with experimental contrast
• Consider lateral force effects

DFT Input:

• Use well-converged Hartree potential
• Adequate vacuum for surface
• Include enough atoms for force field
• Check electrostatic accuracy

Image Interpretation:

• Consider both AFM and STM
• Compare with experimental resolution
• Account for thermal drift
• Validate with known systems

Community and Support

• Open source (Apache-2.0)
• PyPI installation available
• Active development (Probe-Particle team)
• Published in Comput. Phys. Commun. (2024)
• Used by major SPM groups worldwide
• Tutorial examples provided

Verification & Sources

Primary sources:

1. GitHub repository: https://github.com/Probe-Particle/ppafm
2. N. Oinonen et al., Comput. Phys. Commun. 305, 109341 (2024)
3. P. Hapala et al., Phys. Rev. B 90, 085421 (2014)
4. PyPI: https://pypi.org/project/ppafm/

Confidence: VERIFIED

Verification status: ✅ VERIFIED

• Source code: ACCESSIBLE (GitHub)
• Documentation: ACCESSIBLE
• PyPI: AVAILABLE
• Community support: Active (SPM community)
• Academic citations: >500 (method papers)
• Active development: Ongoing
• Specialized strength: Efficient HR-AFM/SPM simulation with probe-particle model, multi-mode SPM, multi-DFT-code interface