PHYS brown bag seminar: Prokop Hapala (SIN): Computer aided AFM imaging & recognition of 3D molecules

Join us for science and pizza in the Nanotalo lobby!

Presentation by Prokop Hapala (SIN group, Department of Applied Physics, Aalto University)

Abstract: In recent decades Atomic Force Microscopy with the tip functionalized by carbon monoxide (CO) has provided a unique tool to experimentally image sub-molecular details of individual organic molecules [1], which is of great importance e.g. for on-surface chemistry. Most experiments have, however, up to now been limited to flat aromatic molecules, due to difficulties in interpreting highly distorted AFM images originating from non-planer molecules and due to mechanical relaxation of the tip or sample. These problems can be partially overcome using a simple mechanical model (Probe-Particle Model [2]) which can reproduce those distortions and therefore simulate AFM images for a given molecular structure. However, this still requires a laborious search for the molecular structure that reproduces that particular experimental image. We instead attempt to develop automatic tools to conduct the inverse task – to recover molecular structure from a given set of AFM images. Preliminary results suggest that convolutional neural network (CNN) [3] trained on simulated AFM images can learn this inverse mapping rather easily. Yet application of the method on real experimental data, and identification of atomic species remains a challenge.  

[1] Gross, L., Mohn, F., Moll, N., Liljeroth, P., & Meyer, G.; The chemical structure of a molecule resolved by atomic force microscopy. Science (New York, N.Y.), 325(5944), 1110–1114 (2009).

[2] Hapala, P., Kichin, G., Wagner, C., Tautz, F. S., Temirov, R., & Jelínek, P. Physical Review B, 90(8), 085421 (2014).

[3] Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P.; Proceedings of the IEEE, 86(11), 2278–2324 (1998).