期刊
NANOTECHNOLOGY
卷 33, 期 11, 页码 -出版社
IOP Publishing Ltd
DOI: 10.1088/1361-6528/ac394a
关键词
dopants; reinforcement learning; molecular dynamics; graphene
资金
- Oak Ridge National Laboratory's Center for Nanophase Materials Sciences (CNMS), a US Department of Energy, Office of Science User Facility
- US Department of Energy, Office of Science, Office of Basic Energy Sciences Data, Artificial Intelligence and Machine Learning at DOE Scientific User Facilities program [34532]
This article explores the training of artificial intelligence agents using reinforcement learning to manipulate atoms in a molecular dynamics environment of graphene with Si dopants. The study demonstrates the potential of reinforcement learning in nanoscale fabrication and highlights that the learned dynamics encode specific elements of important physics.
Atom-by-atom assembly of functional materials and devices is perceived as one of the ultimate targets of nanotechnology. Recently it has been shown that the beam of a scanning transmission electron microscope can be used for targeted manipulation of individual atoms. However, the process is highly dynamic in nature rendering control difficult. One possible solution is to instead train artificial agents to perform the atomic manipulation in an automated manner without need for human intervention. As a first step to realizing this goal, we explore how artificial agents can be trained for atomic manipulation in a simplified molecular dynamics environment of graphene with Si dopants, using reinforcement learning. We find that it is possible to engineer the reward function of the agent in such a way as to encourage formation of local clusters of dopants under different constraints. This study shows the potential for reinforcement learning in nanoscale fabrication, and crucially, that the dynamics learned by agents encode specific elements of important physics that can be learned.
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