With the help of virtual rewards, the microswimmers find their way through the liquid while repeatedly being thrown off of their path, mainly by Brownian motion. “Our results show that the best swimmer is not the one that is fastest, but rather that there is an optimal speed,” said Viktor Holubec, who worked on the project as a fellow of the Alexander von Humboldt Foundation and has now returned to the university in Prague. According to the scientists, linking artificial intelligence and active systems like in these microswimmers is a first small step towards new intelligent microscopic materials that can autonomously perform tasks while also adapting to their new environment. At the same time, they hope that the combination of artificial microswimmers and machine learning methods will provide new insights into the emergence of collective behaviour in biological systems. “Our goal is to develop artificial, smart building blocks that can perceive their environmental influences and actively react to them,” said the physicist. Once this method is fully developed and has been applied to other material systems, including biological ones, it could be used, for example, in the development of smart drugs or microscopic robot swarms.
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