Over the past decade, researchers have developed several new methods to create microscopic machines that move in a liquid environment by absorbing energy. This self-propelled motion of the so-called microswimmers not only mimics a fundamental element of living systems, but also follows the vision put forward by the 1966 movie Fanstatic Voyage and many others since. The idea is that machines can be miniaturized so that they can be injected into the human body for use against injury and disease. While the idea at the time still involved miniaturizing humans to control the machines, today's research on machine learning and artificial intelligence, which also roots back to the 1960s, assumes that such machines could be intelligent themselves. While this is still a dream, research on the active matter in conjunction with machine learning goes far beyond the perspectives of healthcare but considers synthetic micromachines as novel building blocks of adaptive self-organized or even intelligent materials for information processing itself. The following chapter presents some basic elements of how machine learning can be used to enhance the motion of small micromachines or active particles. In particular, it focuses on reinforcement learning as an exploratory learning approach that is demonstrated in variants to control single or multiple microswimmers in the experiment.
%0 Book Section
%1 CICHOS2023113
%A Cichos, Frank
%A Landin, Santiago Mui�os
%A Pradip, Ravi
%B Intelligent Nanotechnology
%D 2023
%E Zheng, Yuebing
%E Wu, Zilong
%I Elsevier
%K topic_physchemistry Active Feedback Machine Multi Optical Reinforcement agent control control, learning, particles, reinforcement
%P 113--144
%R https://doi.org/10.1016/B978-0-323-85796-3.00005-6
%T Chapter 5 - Artificial intelligence (AI) enhanced nanomotors and active matter
%U https://www.sciencedirect.com/science/article/pii/B9780323857963000056
%X Over the past decade, researchers have developed several new methods to create microscopic machines that move in a liquid environment by absorbing energy. This self-propelled motion of the so-called microswimmers not only mimics a fundamental element of living systems, but also follows the vision put forward by the 1966 movie Fanstatic Voyage and many others since. The idea is that machines can be miniaturized so that they can be injected into the human body for use against injury and disease. While the idea at the time still involved miniaturizing humans to control the machines, today's research on machine learning and artificial intelligence, which also roots back to the 1960s, assumes that such machines could be intelligent themselves. While this is still a dream, research on the active matter in conjunction with machine learning goes far beyond the perspectives of healthcare but considers synthetic micromachines as novel building blocks of adaptive self-organized or even intelligent materials for information processing itself. The following chapter presents some basic elements of how machine learning can be used to enhance the motion of small micromachines or active particles. In particular, it focuses on reinforcement learning as an exploratory learning approach that is demonstrated in variants to control single or multiple microswimmers in the experiment.
%@ 978-0-323-85796-3
@incollection{CICHOS2023113,
abstract = {Over the past decade, researchers have developed several new methods to create microscopic machines that move in a liquid environment by absorbing energy. This self-propelled motion of the so-called microswimmers not only mimics a fundamental element of living systems, but also follows the vision put forward by the 1966 movie Fanstatic Voyage and many others since. The idea is that machines can be miniaturized so that they can be injected into the human body for use against injury and disease. While the idea at the time still involved miniaturizing humans to control the machines, today's research on machine learning and artificial intelligence, which also roots back to the 1960s, assumes that such machines could be intelligent themselves. While this is still a dream, research on the active matter in conjunction with machine learning goes far beyond the perspectives of healthcare but considers synthetic micromachines as novel building blocks of adaptive self-organized or even intelligent materials for information processing itself. The following chapter presents some basic elements of how machine learning can be used to enhance the motion of small micromachines or active particles. In particular, it focuses on reinforcement learning as an exploratory learning approach that is demonstrated in variants to control single or multiple microswimmers in the experiment.},
added-at = {2024-10-15T13:24:46.000+0200},
author = {Cichos, Frank and Landin, Santiago Mui�os and Pradip, Ravi},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2ed94dc69d85c51e75eb0ea9fae8609f3/scadsfct},
booktitle = {Intelligent Nanotechnology},
doi = {https://doi.org/10.1016/B978-0-323-85796-3.00005-6},
editor = {Zheng, Yuebing and Wu, Zilong},
interhash = {413cef12826d8c3c35dfb765da19c01e},
intrahash = {ed94dc69d85c51e75eb0ea9fae8609f3},
isbn = {978-0-323-85796-3},
keywords = {topic_physchemistry Active Feedback Machine Multi Optical Reinforcement agent control control, learning, particles, reinforcement},
pages = {113--144},
publisher = {Elsevier},
series = {Materials Today},
timestamp = {2024-11-22T15:49:38.000+0100},
title = {Chapter 5 - Artificial intelligence (AI) enhanced nanomotors and active matter},
url = {https://www.sciencedirect.com/science/article/pii/B9780323857963000056},
year = 2023
}
