Regensburg 2019 – wissenschaftliches Programm
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O: Fachverband Oberflächenphysik
O 28: Organic Molecules on Inorganic Substrates I: Switching and Manipulation
O 28.10: Vortrag
Dienstag, 2. April 2019, 12:45–13:00, H24
Machine learning for single molecule manipulation — Philipp Leinen1,2, Malte Esders3, Kristof Schütt3, Klaus-Robert Müller3, F. Stefan Tautz1,2, and •Christian Wagner1,2 — 1Peter 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
The controlled mechanical manipulation of individual molecules with a scanning probe microscope (SPM) allows the fabrication of single-molecule devices [1,2] and metastable supramolecular assemblies [3]. Machine learning can reduce the tedious work of finding a successful manipulation protocol for a certain manipulation tasks. We use reinforcement learning (RL) to automatically solve the prototypical task of removing a single PTCDA (perylene-tetracarboxylic dianhydride) molecule from a hydrogen-bonded assembly [3]. Since our RL application is not fully in-silico but receives its feedback from an actual experiment, it needs a training efficiency on par with a human to be useful. We achieve this by teaching the machine some “intuition” about the Cartesian space in which the manipulation takes place by, e.g., spawning a series of weak learners along all tried trajectories and training on unseen state-action pairs. Our method could be a blueprint for solving various manipulation tasks posed in a Cartesian space.
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