Berlin 2024 – wissenschaftliches Programm

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O: Fachverband Oberflächenphysik

O 93: Scanning Probe Techniques: Method Development

O 93.4: Vortrag

Donnerstag, 21. März 2024, 15:45–16:00, MA 043

Autonomous chemical reactions in scanning tunneling microscope — •Nian Wu¹, Markus Aapro¹, Alexander Ilin², Robert Drost¹, Joakim Jestilä¹, Zhijie He², Peter Lijeroth¹, and Adam S. Foster^{1, 3} — ¹Applied Physics, Aalto University, Espoo, Finland — ²Computer Science, Aalto University, Espoo, Finland — ³WPI Nano Life Science Institute, Kanazawa University, Kanazawa, Japan

Several breakthrough studies have harnessed scanning probe microscopy (SPM) manipulations to control chemical reactions in on-surface molecular synthesis. In general, for scanning tunnelling microscope (STM) manipulations, they are predominantly controlled via parameters of the tip position, pulse voltages and tunneling conductance. However, the selection of proper parameters requires extensive domain knowledge, which is time consuming and not necessarily transferable to new systems. Recent research has allowed the automation of a wide range of challenges in SPM, including image quality assessment, lateral and vertical manipulation. However, the automation for breaking or forming covalent bonds, which is an indispensable step during chemical synthesis is, as yet, unexplored. To address this problem, we build on our deep reinforcement learning approach to automate bromine removal from 5,15-bis(4-bromo-2,6-methyl-phenyl)porphyrin (Br2Me4DPP) through learning manipulation parameters in STM. We further explore the potential of automated STM to then controllably react the resultant fragments into larger molecular structures.

Keywords: automation; nanofabrication; scanning tunneling microscope; break covalent bond; deep reinforcement learning