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Using artificial intelligence to steer atoms into lattices

Using artificial intelligence to steer atoms into lattices

 


Researchers aim to use a type of artificial intelligence called deep reinforcement learning to steer atoms in lattices, each a fraction of a nanometer in size, to build new materials or nanodevices. I was.

Postdoctoral researcher I-Ju Chen says a major application of deep reinforcement learning is in robotics. “We’re also building a robotic arm using deep learning, but it’s for moving atoms,” she explains. “Reinforcement learning has been successful in things like chess and video games, but we applied it to solve technical problems at the nanoscale.”

Creating very small devices based on single atoms is important for nanodevices such as transistors and memories. Testing how these devices behave at their absolute limits is one of the applications of this kind of atomic manipulation, Chen says. Building new materials atom-by-atom, rather than using conventional chemical techniques, could also reveal interesting properties related to superconductivity or quantum states.

Using their method, scientists were able to assemble a star-like lattice from single atoms of silver in a very cold vacuum chamber. It’s similar to moving, but you grab each atom with very small tweezers and drag it into place.

The silver star lattice created by Chen and colleagues at Finnish artificial intelligence center FCAI and Aalto University is a demonstration of what deep reinforcement learning can achieve.

“Precise movement of atoms is difficult even for human experts,” Chen says. “We adapted an existing deep reinforcement learning for this purpose. It took about a day for the algorithm to learn and about an hour to build the lattice.” Part refers to how the AI ​​is guided through rewards for correct actions or outputs. “Give a goal and it will come true. It can solve problems that humans don’t know how to solve.”

Applying this approach to the world of nanoscience materials is new. Nanotechnology could become more powerful with the introduction of machine learning, researchers say. This is because it can accelerate parameter selection and trial-and-error, which is usually done by humans. “We have shown that this task can be fully completed by reinforcement learning,” Chen concludes.

Our results demonstrate that state-of-the-art deep reinforcement learning can provide effective solutions to real-world challenges in nanofabrication and a powerful approach to increasingly complex atomic-scale scientific experiments.

Journal reference:

I-Ju Chen, Markus Aapro, Abraham Kipnis, Alexander Ilin, Peter Liljeroth, Adam S. Foster. Accurate atom manipulation with deep reinforcement learning. Nature Communications, 2022. DOI: 10.1038/s41467-022-35149-w

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