  The Probe-Particle Model combine theories designed for the simulation of
scanning probe microscopy experiments, employing non-reactive, flexible tip
apices to achieve sub-molecular resolution. In the article we present the
latest version of the Probe-Particle Model implemented in the open-source ppafm
package, highlighting substantial advancements in accuracy, computational
performance, and userfriendliness. To demonstrate this we provide a
comprehensive review of approaches for simulating non-contact Atomic Force
Microscopy. They vary in complexity from simple Lennard-Jones potential to the
latest full density-based model. We compared those approaches with ab initio
calculated references, showcasing their respective merits. All parts of the
ppafm package have undergone acceleration by 1-2 orders of magnitude using
OpenMP and OpenCL technologies. The updated package includes n interactive
graphical user interface and seamless integration into the Python ecosystem via
pip, facilitating advanced scripting and interoperability with other software.
This adaptability positions ppafm as an ideal tool for high-throughput
applications, including the training of machine learning models for the
automatic recovery of atomic structures from nc-AFM measurements. We envision
significant potential for this application in future single-molecule analysis,
synthesis, and advancements in on-surface science in general. Additionally, we
discuss simulations of other sub-molecular scanning-probe imaging techniques,
such as bond-resolved scanning tunnelling microscopy and kelvin probe force
microscopy, all built on the robust foundation of the Probe-Particle Model.
Altogether this demonstrates the broad impact of the model across diverse
domains of surface science and molecular chemistry.
