Molecular Manipulation Lab

The quest for control

Controlled molecular manipulation means knowing the transient conformations and positions of the molecule during manipulation and using this information to reach in a controlled way a target state.

As soon as the relation molecular conformation <----> measurement value has been mapped out completely by automated experiments and machine learning, the identification of precise molecular conformations at any time during manipulation becomes possible. The manipulation process can be described as a hidden Markov chain of incremental tip displacement steps that move the tip through a trajectory Rtip,1,…, Rtip,J, where 1 … J enumerate the discrete steps. We will therefore infer the (hidden) conformations during manipulation from the measured sequence of Δf values [Δf (Rtip,1)…Δf (Rtip,J)] and the manipulation map by employing a particle filter, a method that is used in control theory. In this method a cloud of particles is generated in tip position space of the manipulation map. The particles are randomly distributed around the estimated initial state of the junction (right after tip-molecule contact) and propagated in the manipulation map with each tip displacement step. After J tip steps each particle is characterized by a unique trajectory and sequence [Δf (Rtip,1)…Δf (Rtip,J)] of Δf values read off from the manipulation map. Of these, the actual tip trajectory in the experiment is the one whose sequence matches best with the measured sequence. To avoid unraveling, we will in certain intervals re-condense the cloud of particles around the region of highest probability.

R. Findeisen et al. Control on a molecular scale: A perspective, American Control Conference (ACC) (2016)

January 19th, 2023

MomaLab is part of the Orbital Cinema project!

September 8th, 2022

Warmest congratulations to Taner Esat for winning the Gerhard Ertl Young Investigator Award for his outstanding work in surface science, particularly metastable standing molecules.

April 7th, 2022

Want to make your own standing molecule? "Design Principles for Metastable Standing Molecules" Read at J. Phys. Chem. C

October 10th, 2021

We finally knocked it down: "The stabilization potential of a standing molecule" Read at Science Advances Press release

October 22nd, 2020

Introducing machine learning to the nanoscale: "Autonomous robotic nanofabrication with reinforcement learning" Read at Science Advances Press release

July 2020

Our Helmholtz-AI project MomoNano (together with HZB and TU-Berlin) successfully competed for a three year funding by the Helmholtz Association.

September 2nd, 2020

"The theory of scanning quantum dot microscopy" Read at J Phys. Cond. Mat.
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