This repository contains a project exploring the dynamics of liquid–liquid phase separation (LLPS) in a confined, elastic filament system, modeled as a phenomenological homogeneous setup.
The project draws on ideas from nucleation theory, resetting dynamics in active systems, and nonequilibrium statistical mechanics. Key focuses include droplet behavior under thermal fluctuations and mechanical coupling, the effects of stochastic resetting on phase separation, and the examination of fluctuation–dissipation relations (FDR) and large deviation theory (LDT).
We provide Python source code for simulating Langevin dynamics in this complex system (see python_ver/one_d_model/langevin1d.py and main_langevin.py for our convention and implementation). Scripts are also included for data analysis, such as visualization of frequency-domain autocorrelations and MCMC-based Bayesian regression to characterize effective droplet potentials (see data_util directory).
Overall, the project aims to connect theoretical principles with computational modeling to better understand LLPS in both biological and physical contexts.
if you are on UCLA's Hoffman2, you need to load the anaconda interactively first:
module load anaconda3
then creating a virtual environment, i.e.
conda create -n env_name
in your terminal. Then activate the environment via:
conda activate env_name
In the environment, first, install our package:
pip install -e .
Run the model with installed library:
python -m python_ver.one_d_model.main_langevin
Active liquid-liquid phase separation in a confining environment
Chen Lin, Robijn Bruinsma Phys.Rev.E [paper]