This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Imagine a construction site where the bricks are individual molecules and the "cranes" are microscopic needles so sharp they can feel a single atom. For decades, building at this scale was a laborious, time-consuming task where a single human error could damage the delicate tools.
Now, researchers at the Department of Energy's (DOE) Oak Ridge National Laboratory (ORNL) have handed the controls to an artificial intelligence that can "learn" how to build these materials autonomously, working for more than 25 hours straight without a human operator. In a study published in ACS Nano, ORNL researchers from the Center for Nanophase Materials Sciences (CNMS) describe the creation of a fully automated system capable of building functional materials atom-by-atom. The effort grew from a conceptual framework shaped by P.
Baddorf, together with An-Ping Li and Rama Vasudevan, who supervised the implementation and guided the interpretation of the results. Turning that vision into a working platform required an unusual blend of software and hands-on experimental skills. Ganesh Narasimha prepared the core automation software based on input from Vasudevan, while Mykola Telychko developed the experimental setup.
The two then jointly carried out the manipulation trials and assembled the resulting dataset. Wooin Yang helped design the AI's object-detection workflow and performed the critical spectroscopic studies used to verify the materials. Narasimha said the collective effort enables "atomically precise fabrication, with significantly reduced human input." He added that the effort is shifting the research paradigm from discovering materials in nature to engineering specific "artificial lattices," which are orderly, repeating patterns of atoms or molecules that form a material's underlying framework, with tailored electronic behaviors.
The team developed AI models that served as microscopic "eyes" and a "brain." As the "eyes," a computer vision model called YOLO ("You Only Look Once") was used to rapidly detect molecules on a copper surface. As the "brain," reinforcement learning enabled the model to evaluate different strategies for moving molecules and receive a numerical reward based on how well each attempt succeeded. The reward was highest when a molecule reached the intended target site and much lower when it missed.
By linking higher rewards to the actions that produced them, the model learned the best combination of electrical current, bias and manipulation speed to nudge molecules into place without damaging the microscope tip. The researchers demonstrated their design worked by using the AI to build an artificial graphene lattice made of 37 molecules in a perfect honeycomb pattern. Most importantly, they confirmed the structure worked by finding a Dirac point, a unique electronic signature showing that the man-made material behaved exactly like real graphene.
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