Autonomous Single-Molecule Manipulation Based on Reinforcement Learning
Abstract
Building nanostructures one-by-one requires precise control of single molecules over many manipulation steps. The ideal scenario for machine learning algorithms is complex, repetitive, and time-consuming. Here, we show a reinforcement learning algorithm that learns how to control a single dipolar molecule in the electric field of a scanning tunneling microscope. Using about 2250 iterations to train, the algorithm learned to manipulate the molecule toward specific positions on the surface. Simultaneously, it generates physical insights into the movement as well as orientation of the molecule, based on the position where the electric field is applied relative to the molecule. This reveals that molecular movement is strongly inhibited in some directions, and the torque is not symmetric around the dipole moment.
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