Learning protocols for the fast and efficient control of active matter
Abstract 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 e...
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Nature Portfolio
2024-10-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-52878-2 |
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| _version_ | 1850203822612283392 |
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| author | Corneel Casert Stephen Whitelam |
| author_facet | Corneel Casert Stephen Whitelam |
| author_sort | Corneel Casert |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-65ea9746774d4ab3ab0e63e42673e32c |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-65ea9746774d4ab3ab0e63e42673e32c2025-08-20T02:11:25ZengNature PortfolioNature Communications2041-17232024-10-011511910.1038/s41467-024-52878-2Learning protocols for the fast and efficient control of active matterCorneel Casert0Stephen Whitelam1Molecular Foundry, Lawrence Berkeley National LaboratoryMolecular Foundry, Lawrence Berkeley National LaboratoryAbstract 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.https://doi.org/10.1038/s41467-024-52878-2 |
| spellingShingle | Corneel Casert Stephen Whitelam Learning protocols for the fast and efficient control of active matter Nature Communications |
| title | Learning protocols for the fast and efficient control of active matter |
| title_full | Learning protocols for the fast and efficient control of active matter |
| title_fullStr | Learning protocols for the fast and efficient control of active matter |
| title_full_unstemmed | Learning protocols for the fast and efficient control of active matter |
| title_short | Learning protocols for the fast and efficient control of active matter |
| title_sort | learning protocols for the fast and efficient control of active matter |
| url | https://doi.org/10.1038/s41467-024-52878-2 |
| work_keys_str_mv | AT corneelcasert learningprotocolsforthefastandefficientcontrolofactivematter AT stephenwhitelam learningprotocolsforthefastandefficientcontrolofactivematter |