Dresden 2020 – wissenschaftliches Programm
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O: Fachverband Oberflächenphysik
O 25: Poster Session - Focus Sessions: Innovation in Machine learning PRocEsses for Surface Science (IMPRESS)
O 25.1: Poster
Montag, 16. März 2020, 18:15–20:00, P1A
Recovering molecular configurations during SPM-based manipulation — •Joshua Scheidt1,2,3, Kurt Driessens4, F. Stefan Tautz1,2, and Christian Wagner1,2 — 11Peter Grünberg Institut (PGI-3), Forschungszentrum Jülich, Germany — 2JARA Fundamentals of Future Information Technology, Jülich, Germany — 3Institut für Softwaretechnik und Theoretische Informatik, Technische Universität Berlin, Germany — 4Department of Data Science and Knowledge Engineering, Maastricht University, The Netherlands
Molecular nanorobotics with a scanning probe microscope (SPM) would place the construction of complex supramolecular structures that are not accessible by self-assembly within reach. A fundamental obstacle on the way towards this goal is the poor observability of the atomic-scale molecular conformation during manipulation. Here we present a solution to this problem which utilises the particle filter localisation (PF) technique: Force-gradient data along a manipulation trajectory as received from the SPM is compared to data from a molecular simulation stored using a finite state automaton[1]. The simulation contains all inequivalent tip-molecule-surface configurations on a high-symmetry surface. The PF uses a set of sampling points to identify simulated conformations which likely match the experimental data. The particles from the PF are subsequently clustered to allow for a single or small set of likely molecule conformations to be retrieved. We test the performance of the PF on synthetic as well as experimentally acquired data.
[1] A. Diener, Master’s thesis, Maastricht University, 2018