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Automated probe microscopy via evolutionary optimization at the atomic scale

<27> The scanning tunnelling microscope (STM), a Nobel prize winning invention, considered one of the most inspirational tools in physics is also a frustrating technique requiring difficult hours spent manually altering the microscope's controls. We addressed this longstanding problem using a new approach combining Machine Vision and Cellular Genetic Algorithms that autonomously varies STM's controls. This work, output of EPSRC grant EP/H024905/1, won the 2012 Silver Award (US$ 3K) in the Human-Competitive Evolutionary Computation Techniques Competition of the ACM's special interest group on Evolutionary Computation and we are negotiating its licensing with a global manufacturer of STMs.