@laynefrechette
Physical scientist using stat mech, molecular simulations, and ML to to understand assembly and self-organization of nanoscale, biomolecular, and active materials. Postdoc at Brandeis University. UC Berkeley Chemistry alum. https://layne28.github.io/
Our active noise method is customizable and allows for large-scale simulations. Hence, we foresee that it will be a useful tool for simulating complex materials in active baths. Our code is available on GitHub (see paper for links), so feel free to try it out yourself!
How do these dynamic structures form? Particles tend to settle in “low-mobility” regions of the noise field, where the magnitude of the active force is small. But because of excluded volume, not all of the particles can fit in such regions. This interplay sets the cluster size.
By changing the active noise correlation length and time, we can tune the sizes and lifetimes of the clusters.
We tested our method on a system of volume-excluding colloidal particles. At thermal equilibrium, this system is fairly “boring,” exhibiting little structure. In contrast, active noise causes these particles to form striking dynamic structures, such as rotating clusters.
How do we harness directed motion to assemble life-like materials? We developed a computational method to help answer this question. While active fluid flows look chaotic, they have a well-defined correlation length and time. We thus modeled active fluids via correlated noise.
Living systems consume energy to create dynamic assemblies, such as the cytoskeleton. A promising platform for creating life-like synthetic materials is active matter, in which energy consumption drives directed motion (e.g. youtube.com/watch?v=RwBb... from the Dogic Lab).
I’m excited to share that my work on active assembly of passive colloidal particles has been published in Newton (@cp-newton.bsky.social)! You can check it out here: cell.com/newton/fullt.... See thread for more details:
