Federal agency name: defense advanced research projects agency (DARPA), defense sciences office (DSO) funding opportunity title: young faculty award (YFA) funding opportunity number:…

The Department of the Interior Departmental Offices Interior Business Center awarded a $250,000 cooperative agreement to The Leland Stanford Junior University on September 30, 2025, under the Research and Technology Development program (CFDA 12.910) to develop data-driven modeling and optogenetic feedback control of programmable matter containing living bacteria and cells. The research integrates systematic control strategies for emergent collective behaviors among complex living cells, addressing the challenge of controlling microstructure properties such as density and flux through managing multibody interactions and correlated particle dynamics. The award funds work performed in California, with a period of performance through September 29, 2027. The project investigates innovative approaches to harness active matter systems and collective motion phenomena observed in bacterial colonies, bird flocks, and robot swarms, with potential applications in programmable materials, directed assembly, and robotics. The Young Faculty Award (DARPARA2501) solicitation funds research with military or dual-use application that advances the state of the art or results in fundamental change in technology across the Defense Sciences Office and other DARPA technical offices.

Federal agency name: defense advanced research projects agency (DARPA), defense sciences office (DSO) funding opportunity title: young faculty award (YFA) funding opportunity number: DARPARA2501CATALOG of federal domestic assistance (cfda) number(s): 12.910 research and technology developmentprogram description: DARPA is soliciting innovative research proposals in the areas of interest to the following DARPA technical offices: defense sciences office (DSO), information innovation office (I2O), microsystems technology office (MTO), and the tactical technology office (TTO). Further detail regarding the specific technical areas of interest can be found below under open office topics. Proposed research should investigate innovative approaches that enable revolutionary advances in science, devices, or systems. Specifically excluded is research that primarily results in evolutionary improvements to the existing state of practice.active matter systems are composed of autonomous agents that consume energy to generatemechanical motion, leading to emergent phenomena like flocking and swarming 1,2 . Collectivemotion is observed across various natural and engineered systems, like bacterial colonies, birdflocks, and robot swarms 37 . Understanding and harnessing these emergent behaviors mayrevolutionize applications in programmable materials, directed assembly, and robotics. In this project, we integrate data-driven modeling and optogenetic feedback to control thephysical properties of programmable matter containing living bacteria and cells. Controlling themicrostructure of programmable matter, like density or flux, remains a significant challenge dueto the complex multibody interactions and correlated particle dynamics. In prior work, researchers have developed actuation strategies for single active agents using external fields likemagnetic, optical, acoustic, and electric stimuli 818 . However, systematic control of emergentcollective behaviors among complex living cells and active matter remains a significant challenge. The key enabling breakthrough of my labs approach is to use model-based optogenetic feedbackcontrol, guided by data-driven methods on complex living matter. We will overcome two keychallenges in the fields of mathematical modeling of active matter and optogenetic populationcontrol: 1) the design of gray-box models using physics-constrained machine learning onexperimental and simulation data of complex living systems, a grand challenge in data-drivenengineering and 2) the use of patterned light with spatiotemporal feedback to actuatepopulation control of living systems, a grand challenge in optogenetics and bioengineering. Our project is impactful because IT fills an important knowledge GAP in controlling programmablematter exhibiting complex dynamics, like chaotic swarming and flocking motion. We present aframework that combines physics-informed machine learning with model predictive control, resulting in a precise, real-time, and location-specific microstructure control of programmablematerials. We expect our project to enable exquisite control of a large population of autonomousagents, from microscopic agents like bacteria to macroscopic tissues. In fact, we expect ourframework to also be applicable to non-living autonomous agents like engineered robots anddrones. A major impact of our project is the development of mathematical models and theintegration of these models with optogenetics to control addressable matter.my research team encompasses the expertise and capabilities to address the challenges of thisproject. We expect a full-time postdoc and a graduate student researcher will complete ourobjectives within the 36-month period. Indeed, as shown below in technical plan (section 5), ourpromising early results have already demonstrated the feasibility of our strategy for theoptogenetic control of living bacteria and their collective dynamics.