Exploring electron beam induced atomic assembly via reinforcement learning in a molecular dynamics environment.
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.
['Vasudevan|Rama K|RK|0000-0003-4692-8579', 'Ghosh|Ayana|A|0000-0002-0432-3689', 'Ziatdinov|Maxim|M|0000-0003-2570-4592', 'Kalinin|Sergei V|SV|']
reinforcement learning molecular dynamics graphene dopants