Inferring interaction potentials from stochastic particle trajectories
Ella M. King, Megan C. Engel, Caroline Martin, Alp M. Sunol, Qian-Ze, Zhu, Sam S. Schoenholz, Vinothan N. Manoharan, Michael P. Brenner

TL;DR
This paper presents a novel method to infer interaction potentials directly from particle trajectory data, applicable to both equilibrium and non-equilibrium systems, without assuming a specific potential form.
Contribution
The authors introduce a trajectory-based inference method that explicitly solves equations of motion to determine interaction potentials, overcoming limitations of existing equilibrium-based techniques.
Findings
Successfully inferred depletion interactions from experimental colloidal data.
Applicable to large particle systems in typical conditions.
Works for systems both in and out of equilibrium.
Abstract
Accurate interaction potentials between microscopic components such as colloidal particles or cells are crucial to understanding a range of processes, including colloidal crystallization, bacterial colony formation, and cancer metastasis. Even in systems where the precise interaction mechanisms are unknown, effective interactions can be measured to inform simulation and design. However, these measurements are difficult and time-intensive, and often require conditions that are drastically different from in situ conditions of the system of interest. Moreover, existing methods of measuring interparticle potentials rely on constraining a small number of particles at equilibrium, placing limits on which interactions can be measured. We introduce a method for inferring interaction potentials directly from trajectory data of interacting particles. We explicitly solve the equations of motion to…
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Taxonomy
TopicsStatistical Mechanics and Entropy · Electrostatics and Colloid Interactions
