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Computational nanoscience

Virtually every engineering task in our macroscopic world such as directed motion, pumping, sensing, information storage and processing etc. has already been achieved on a molecular level in nature. There are turbines like the ATP synthase that rotate in a membrane driven by a proton gradient and transforming mechanical into chemical energy, there are molecular vehicles like kinesin carrying vesicles on a tubulin track or repair enzymes moving along DNA strands, recognizing and repairing wrongly paired bases.

Within the last 15 years, based on an increasing knowledge in supramolecular chemistry, a number of artificial molecular machines have been designed and synthesized. The Feringa motor and a large number of rotaxane shuttles could serve as examples. As the dynamic machine-like functions become more sophisticated, computer aided design strategies and the a priori prediction of the properties are needed to guide the chemical (supramolecular) intuition and to minimize the subsequent synthetic efforts.

We currently are pursuing four different "nano-engineering" projects, the synthesis of a supramolecular machine to assemble molecules, a light driven proton pump, a single molecule magnetic switch and an artificial cilia epithelium to induce directed motion on a surface. Geometries, energies, supramolecular interactions and other properties are predicted using density functional theory methods. After "in silico" optimization of the properties, we are synthesizing and testing our molecular machines in the lab. Usually, an iterative process switching between calculations and experiments is needed to achieve optimized functions.

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Herges, R. Computational nanoscience. Chemistry Central Journal 3 (Suppl 1), O12 (2009).

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