Autonomous robotic nanofabrication with reinforcement learning
This work addresses the challenge of building complex supramolecular structures for nanotechnology, representing an incremental advance by automating a previously human-performed task.
The researchers tackled the problem of uncontrolled variability and poor observability in atomic-scale conformations for nanofabrication by using reinforcement learning to autonomously manipulate single molecules with a scanning probe microscope, achieving excellent performance in subtractive manufacturing tasks.
The ability to handle single molecules as effectively as macroscopic building-blocks would enable the construction of complex supramolecular structures inaccessible to self-assembly. The fundamental challenges obstructing this goal are the uncontrolled variability and poor observability of atomic-scale conformations. Here, we present a strategy to work around both obstacles, and demonstrate autonomous robotic nanofabrication by manipulating single molecules. Our approach employs reinforcement learning (RL), which finds solution strategies even in the face of large uncertainty and sparse feedback. We demonstrate the potential of our RL approach by removing molecules autonomously with a scanning probe microscope from a supramolecular structure -- an exemplary task of subtractive manufacturing at the nanoscale. Our RL agent reaches an excellent performance, enabling us to automate a task which previously had to be performed by a human. We anticipate that our work opens the way towards autonomous agents for the robotic construction of functional supramolecular structures with speed, precision and perseverance beyond our current capabilities.