Molecular Manipulation Lab

Machine learning for the identification of molecular conformations

We can overcome the information gap by acquiring large amounts of data while manipulating the molecule a small region around its current conformation. As long as this manipulation is fully reversible, the conformation is essentially not changed. We will implement machine learning solutions (such as (un)supervised learning, kernel ridge regression, support vector machines etc.) for the various data analysis tasks required in this context. Supervised machine learning which, based on wavelet analysis, detects discontinuities in the experimental signals that indicate (irreversible) conformational relaxations. Unsupervised machine learning to structure small segments of experimental data into larger units. Acquiring large amounts of simulation data using a reliable and accurate molecular mechanics model. Supervised machine learning (support vector machine) to determine the shape of the region within which manipulation is reversible for experimental and simulated data. Kernel regression to interpolate measured and simulated data in each region. An algorithm to assign experimental regions to simulated ones on a bipartite graph via measurements of manifold distances. This creates a map of all possible manipulation processes.

White Paper: Understanding Many-Particle Systems with Machine Learning, Inst. of Pure and Appl. Mathematics (IPAM) (2017)

December 19th, 2020

Postdoc position available

get more information.

October 22nd, 2020

Congratulations to F. Stefan Tautz for being a winner at the Falling Walls and Berlin Science Week in the category Engineering and Technology. Watch the Online live-stream session Breaking the Wall of Building with Molecules

September 2nd, 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.

October 23th, 2019

We have been granted another 3.5 Mio core‑h on JURECA.

June 27th, 2019

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

June 10th, 2019

New scanning quantum dot microscopy (SQDM) paper online. Quantitative measurements of surface potentials and dipoles. New window to the nanoscale world. Read at Nature Materials Read full-text w/o subscription Press release
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