Using Data-driven Methods to Understand and Control Active Materials

Add to your list(s) Download to your calendar using vCal

• Michael Hagan (Brandeis University)
• Wednesday 10 September 2025, 14:30-15:10
• External.

If you have a question about this talk, please contact nobody.

TGM150 - 9th Edwards Symposium – Frontiers in Statistical Physics and Soft Matter

Active matter is composed of particles that generate forces or motion, which leads to spectacular emergent dynamics. In principle, active matter could form the basis for a new class of materials with lifelike properties of biological organisms. Yet, active materials exhibit diverse dynamical states, most of which have chaotic dynamics that do not produce work or other useful functions. Thus, a robust control strategy is needed to drive active materials into an emergent state that corresponds to a desirable function. However, designing control protocols requires accurate dynamical models, which are not available for most active matter systems. Developing quantitative models using traditional statistical physics approaches is challenging because active materials lack the scale separation characteristic of equilibrium systems. In this presentation, I will discuss efforts to combine machine learning, other data-driven techniques, and  physics-based models with control theory to address this challenge in the context of a widely-used active material, microtubule-based active nematics. Recent advances in optogenetic motors have enabled constructing the light-activated active nematics, in which the activity can be spatiotemporally controlled by shining light on the sample. The challenge is to determine the spatiotemporal light sequence required to drive the system into a desired behavior.I will describe two complementary approaches to computationally determine an optimal light sequence. In the first, we have adapted a method to discover optimal physics-based continuum models directly from spatiotemporal data, using sparse regression. We have identified several approaches to mitigate measurement errors in the data. We find that the method can reveal the relative contributions of different physical mechanisms, and quantitatively estimates key experimental parameters. Then, we have developed a framework to combine the optimal physics-based model with optimal control theory to solve for the spatiotemporal activity profile that drives the system into a desired state. We demonstrate that active materials can be driven into arbitrary behaviors, including those which do not correspond to dynamical attractors and thus cannot be accessed without control.Since no model is perfectly accurate for a specific system, in the second approach we develop a deep reinforcement learning (DRL) based controller to enable model-free control of active materials. The controller discovers and implements spatiotemporal sequences of activity to drive a 2D active nematic system toward a prescribed dynamical steady-state. This framework does not require a detailed physics model, making it ideal for complex active materials that lack quantitative theoretical descriptions. Furthermore, the approach is extremely robust to noise and experimental measurement error. We compare the performance of the physics-based and DRL -based controllers for active nematics.This work was supported by DE-SC0022291. Preliminary work was supported by NSF DMR -1855914 and DMR -2011846. Computing resources were provided by XSEDE TG -MCB090163 and the Brandeis HPCC (DMR-MRSEC 2011846 and OAC -1920147).

This talk is part of the Isaac Newton Institute Seminar Series series.

This talk is included in these lists:

• All CMS events
• External
• Featured lists
• INI info aggregator
• Isaac Newton Institute Seminar Series
• School of Physical Sciences
• bld31

Note that ex-directory lists are not shown.