Tomáš Jungwirth: Antiferromagnetic spintronics: From memories to topological phenomena, neuromorphics, and ultra-fast optics
Begin: 07.10.2020, 14:10
Location: lecture room F2, first floor Ke Karlovu 5

Daniel Braithwaite: UTe2: Excitement along the road of heavy fermion superconductivity
Begin: 14.10.2020, 14:10
Location: via ZOOM - link: tba

Jan Honolka: Magnetic order in 2D systems: example of RKKY-coupled Co spins on WS2 monolayers
Begin: 21.10.2020, 14:10
Location: lecture room F2, first floor Ke Karlovu 5

Title: Prokop Hapala: Toward Automatic Interpretation of submolecular AFM images
Number: 74/19
Status: Closing date exceeded
Begin: Thursday, 05.12. 2019, 14:00
Tutor: Václav Holý, Milan Dopita
Location: Lecture room F2, Facutly of Mathematics and Physics, First floor Ke Karlovu 5, Prague 2


Nano Seminar

 joint with

Seminar of the Condensed Matter Theory

Thursday, 5. 12. 2019, 14.00,

Lecture room F2 (1st floor), MFF UK, Ke Karlovu 5


Prokop Hapala

Institute of Physics of the Czech Academy of Sciences, Department of Condensed Matter Theory, Prague, Czech Republic 

This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Toward Automatic Interpretation of submolecular AFM images

In recent decade Atomic Force Microscopy with tip functionalized by carbon monoxide (CO) provided unique tool to experimentally image sub-molecular details of individual organic molecules [1]. In principle it allows to map distribution of electron density and electrostatic potential, which is of great importance e.g. for on-surface chemistry. Direct interpretation of the images is however complicated by convolution of electric fields and strong relaxation of CO molecule causing severe image distortions. This problem can be partially overcome by simple mechanical model (Probe-Particle Model [2]) which can reproduce these effects, therefore simulate AFM images for given molecular structure. However, this still requires laborious search for molecular structure which reproduces that particular experimental image. Instead we attempt to develop automatic tool to conduct inverse task – to recover molecular structure from given set of AFM images. Our result shows that convolution neural network (CNN) [3] trained on simulated AFM images can learn this inverse mapping at least for simulated images. One of the challenges in order to achieve reliable results on experimental data is synthesis of sufficient amount of realistic training examples at feasible computational cost.

[1] Gross, L., Mohn, F., Moll, N., Liljeroth, P., Meyer, G.; The chemical structure of a molecule resolved by atomic force microscopy, Science, 325(5944), 1110–1114 (2009).
[2] Hapala, P., Kichin, G., Wagner, C., Tautz, F. S., Temirov, R., Jelínek, P. Phys. Rev. B, 90(8), 085421 (2014).
[3] Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.; Proceedings of the IEEE, 86(11), 2278–2324 (1998).