Learning protocols for the fast and efficient control of active matter

Journal Article · Wed Oct 23 00:00:00 EDT 2024 · Nature Communications

DOI:https://doi.org/10.1038/s41467-024-52878-2· OSTI ID:2473390

Casert, Corneel ^[1]; Whitelam, Stephen ^[2]

---

1. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Ghent University (Belgium)
2. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Exact analytic calculation shows that optimal control protocols for passive molecular systems often involve rapid variations and discontinuities. However, similar analytic baselines are not generally available for active-matter systems, because it is more difficult to treat active systems exactly. Here we use machine learning to derive efficient control protocols for active-matter systems, and find that they are characterized by sharp features similar to those seen in passive systems. We show that it is possible to learn protocols that effect fast and efficient state-to-state transformations in simulation models of active particles by encoding the protocol in the form of a neural network. We use evolutionary methods to identify protocols that take active particles from one steady state to another, as quickly as possible or with as little energy expended as possible. Our results show that protocols identified by a flexible neural-network ansatz, which allows the optimization of multiple control parameters and the emergence of sharp features, are more efficient than protocols derived recently by constrained analytical methods. Our learning scheme is straightforward to use in experiment, suggesting a way of designing protocols for the efficient manipulation of active matter in the laboratory.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 2473390
• Alternate ID(s): OSTI ID: 2483668
• Journal Information: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 15; ISSN 2041-1723
• Publisher: Nature Publishing Group
• Country of Publication: United States
• Language: English

Related Subjects

74 ATOMIC AND MOLECULAR PHYSICS
Phase transitions and critical phenomena
Statistical physics
Thermodynamics