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)

February 15th, 2019

We welcome Joshua Scheidt, computer science student at Maastricht University, who joins the MoMaLab to do his master thesis.

February 4th, 2019

Hand-controlled STM-based atomic manipulation with real-time visual feedback from an MD simulation Read at Beilstein..

October 25th, 2018

We have been granted 2 Mio core‑h on JURECA for our simulation work on molecular adsorption and manipulation.

September 6th, 2018

We are happy that MSc Michael Maiworm joins us till January to work on molecular control algorithms.
Meet us at
▶   IPAM 2nd reunion meeting "Understanding Many-Particle Systems with Machine Learning"Lake Arrowhead, California, 09-14 June 2019