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Clare R. Rees-Zimmerman

Publications and source records attributed to Clare R. Rees-Zimmerman.

2 recordsLinked to original sources

Effective interactions in active Brownian particles

We report an approach to obtain effective pair potentials which describe the structure of two-dimensional systems of active Brownian particles. The pair potential is found by an inverse method, which matches the radial distribution function found from two different schemes. The inverse method, previously demonstrated via simulated equilibrium configurations of passive particles, has now been applied to a suspension of active particles. Interestingly, although active particles are inherently not in equilibrium, we still obtain effective interaction potentials which accurately describe the structure of the active system. Treating these effective potentials as if they were those of equilibrium systems, furthermore allows us to measure effective chemical potentials and pressures. Both the passive interactions and active motion of the active Brownian particles contribute to their effective interaction potentials.

cond-mat.soft↗

Numerical methods for unraveling inter-particle potentials in colloidal suspensions: A comparative study for two-dimensional suspensions

We compare three model-free numerical methods for inverting structural data to obtain interaction potentials, namely iterative Boltzmann inversion (IBI), test-particle insertion (TPI), and a machine-learning (ML) approach called ActiveNet. Three archetypal models of two-dimensional colloidal systems are used as test cases: Weeks--Chandler--Anderson short-ranged repulsion, the Lennard-Jones potential, and a repulsive shoulder interaction with two length scales. Additionally, data on an experimental suspension of colloidal spheres are acquired by optical microscopy and used to test the inversion methods. The methods have different merits. IBI is the only choice when the radial distribution function is known but particle coordinates are unavailable. TPI requires snapshots with particle positions and can extract both pair- and higher-body potentials without the need for simulation. The ML approach can only be used when particles can be tracked in time and it returns the force rather than the potential. However, it can unravel pair interactions from any one-body forces (such as drag or propulsion) and does not rely on equilibrium distributions for its derivation. Our results may serve as a guide when a numerical method is needed for application to experimental data, and as a reference for further development of the methodology itself.

cond-mat.soft↗