Learning surface molecular structures via machine vision

Abstract

Machine learning: Computers automatically decode complex molecular patterns Complex patterns formed by thousands of molecules on a surface can now be automatically recognized and classified by a computer. Sergei Kalinin, Maxim Ziatdinov and Artem Maksov at Oak Ridge National Lab have developed a ‘machine vision’ approach, adapting techniques already used in cancer detection and satellite imaging, to analyze up to 1000 s of individual molecular configurations from microscopic images of so-called ‘buckybowl’ molecules on gold surfaces. These bowl-shaped molecules may rest face up or down, and can adopt different rotational orientations, resulting in a rich tapestry of molecular patterns which are too complicated to sort manually or with existing computational methods. However, this machine vision approach revealed details of how buckybowls interact with their neighbors to build complex arrays, which could help in the design of molecular memory devices.