----------- PAPER TITLE “Automated probe microscopy via evolutionary optimization at the atomic scale” ------------------------------ CRITERIA SATISFIED BY THE WORK (A) The result was patented as an invention in the past, is an improvement over a patented invention, or would qualify today as a patentable new invention. (D) The result is publishable in its own right as a new scientific result independent of the fact in was mechanically created. (E) The result is equal to or better than the most recent human-created solution to a long-standing problem for which there has been a succession of increasingly better human-created solutions. (F) The result is equal to or better than a result that was considered an achievement in its field at the time it was first discovered. (G) The result solves a problem of indisputable difficulty in its field. (H) The result holds its own or wins a regulated competition involving human contestants (in the form of either live human players or human-written computer programs) --------------------------------------------------------------- BACKGROUND AND STATEMENT OF HUMAN-COMPETITIVENESS (A, D, E, F, G & H) Since the Noble prize winning invention of the scanning tunnelling microscope (STM) by Gerd Binnig and Heinrich Rohrer in 1981, it has been associated with some of the most inspirational and elegant experimental works in physics and chemistry. Arguably the instrument enables the ultimate imaging and control of matter on surfaces; by exploiting the rules of quantum mechanics one can image and even manipulate individual atoms, going far beyond traditional imaging techniques. It also, unfortunately, can be one of the most frustrating techniques used by the nanoscientist because the instruments operation is inherently reliant on the quality of a probe scanning (under feedback) just a few Angstroms (one ten millionth of a millimetre) above the surface of inquiry. Many difficult hours are spent manually altering the microscopes control parameters to coerce the arrangement of atoms, or atom, at the probes apex into a configuration that yields, retains, and accurately reproduces atomic resolution images. Over some 30 years the community has made a number of gradually improving manual methods to mechanically sharpen the probes apex (e.g. electrochemical etching, electron and ion bombardment, and explosive delamination) which must then be followed by a human operator’s fine tuning of the imaging control parameters, which can also affect the probes structure. During the probe (and thus image) optimization stage these control and conditioning parameters are adjusted based on the operator’s experience (usually acquired over the course of a PhD) rather than arising from a well-defined metric (or set of metrics). We have addressed these longstanding problems of probe optimisation and metrics by using a Machine Intelligence approach that combines Machine Vision (including classification with universal similarity metrics) and a Cellular Genetic Algorithm. Our automated software control system varies the microscope’s imaging parameters and evolves toward a good image (thus a good probe structure) based on a predefined ‘ideal’ target image. In a competition ‘Beat the Nano-machine’ held at the University of Nottingham (regulated by the institutes professional standards) between the computer system and human contestants, the computer system, overall, achieved best image and in a shorter time (CRITERIA H). The system regularly obtains better quality images than the first published results of the prototypical tip-sample combination; a PtIr probe scanning a highly oriented pyrolytic graphite (HOPG) sample (CRITERIA F). The system is so reliable that commercial licenses are being pursued, as software of this nature cannot be patented under UK law (CRITERIA A). Additionally, the system could obtain different probe types by selecting different target images (as the image is the convolution between surface and probe). These results showed apparent phenotypic plasticity (same imaging parameters, different image), an important result and has obvious potential in the analysis and interpretation of SPM image generation (CRITERIA E). Furthermore, this work has been published in R.A.J. Woolley, J. Stirling, A. Radocea, N. Krasnogor, and P. Moriarty. “Automated probe microscopy via evolutionary optimization at the atomic scale.” Applied Physics Letters, 98(25),253104, 2011 (CRITERIA D) and constitutes a crucial stepping stone (CRITERIA G) towards resolving a 30 years old problem: the automation of imaging and atom-by-atom materials fabrication. Thus, because our software solves a longstanding problem, is reliable, achieves better results on the prototypical Pt:Ir/HOPG (tip-sample) combination and shows evidence of phenotypic plasticity we believe it satisfies criteria A, D, E, F, G & H. -------------------------------------------- WHY THIS WORK IS MOST RELAVENT AND IMPORTANT The tantalising promise of creating nano-enabled devices that provide faster, safer, smaller and cheaper products, even quantum computers, relies on the ability of the researcher to image, interrogate and manipulate matter at the nanometre length scale. The scanning probe microscope is an obvious instrument to use in this area but since its invention has suffered greatly from the rate limiting step of probe optimisation. Our work presents a novel solution using a genetic algorithm to solve this long standing problem. Moreover, the control system repeatedly surpasses the performance of a human operator. Of particular relevance is the fact our system is the only method in the world of optimising a scanning probe without a human operator, hence, we are not only human-competitive but even human expert-independent. This enabling technology has the potential to reach into thousands of research labs around the world, saving countless hours of operator time and thus greatly improved efficiency. Critically if we are to realise atomically precise engineering and manufacture based on scanning probe technology we believe we have created a technology cornerstone. Finally, of particular importance is that the genetic algorithm controls a real world instrument, ergo, a robot whose actuators can manipulate individual molecules and even single atoms. In essence, when the system optimises a probe it is evolving a nanostructure toward a predefined functionality and also learning the ideal parameters to use that nanostructure. We believe this work should be considered best because not only is this kind of science far reaching but it’s just unbelievably cool! --------------------------- PAPER CITATION AND ABSTRACT “Automated probe microscopy via evolutionary optimization at the atomic scale”, Richard A.J. Woolley, Julian Stirling, Adrian Radocea, Natalio Krasnogor, and Philip J. Moriarty, Appl. Phys. Lett. 98, 253104 (2011) doi:10.1063/1.3600662 Publishers link: http://dx.doi.org/10.1063/1.3600662 We describe the development and application of an imaging protocol, which evolves a scanning probe’s atomic structure in parallel with automated optimization of the scan parameters. Our protocol coerces the system into a state that produces a specific atomic resolution image type without human involvement. ---------------------------- AUTHORS' CONTACT INFORMATION Richard A.J. Woolley (corresponding author), School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK e-mail: richard.woolley@nottingham.ac.uk Phone: +44 (0)1158467904 Julian Stirling School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK e-mail: ppxjs1@nottingham.ac.uk Phone: +44 (0)1158467904 Philip Moriarty School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK e-mail: philip.moriarty@nottingham.ac.uk Phone: +44 (0)115 9515156 Natalio Krasnogor School of Computer Science University of Nottingham Jubilee Campus, Wollaton Road Nottingham, NG8 1BB, UK e-mail: nxk@cs.nott.ac.uk phone: +44 (0)115 8467592 Adrian Radocea Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA e-mail: radocea2@illinois.edu phone: (914) 356-5493 The prize money, if any, is to be divided; 60% to RAJ Woolley the remaining equally among the co-authors.