Interaction potentials from arbitrary multi-particle trajectory data.

Area of Science:

• Physics
• Computational Science
• Materials Science

Background:

• Nanoscale and mesoscale particle systems are vital in many scientific fields.
• Accurate simulation of these systems requires reliable interaction potential functions.
• Traditional experimental methods often struggle to determine these potentials.

Purpose of the Study:

• To develop a straightforward methodology for generating pair potential functions.
• To enable potential function derivation from multi-particle trajectory datasets.
• To overcome limitations of existing methods regarding system equilibration and hydrodynamic interactions.

Main Methods:

• Utilized large multi-particle trajectory datasets.
• Developed a method to generate pair potential functions directly from these datasets.
• Tested robustness against Brownian motion and particle tracking errors using simulated data.

Main Results:

• Successfully generated pair potential functions from trajectory data.
• Demonstrated high robustness against trajectory perturbations and common tracking errors.
• The method imposes no operational constraints on system equilibration or damping.

Conclusions:

• The presented methodology offers a robust and accessible way to obtain critical simulation inputs.
• Advances in microscopy and tracking algorithms will facilitate experimental data collection for this method.
• This work paves the way for more accurate and efficient simulations of complex particle systems.