2012 February

A spectral lens for light

Tuesday, 28th February 2012Publication highlights

Links: Prinzip der Spektrallinse. Ein Lichtpuls wird in die Linse eingebracht, die dessen anfänglich breites Frequenzspektrum (rot dargestellt) zu einem schmaleren Spektrum (blau) komprimiert. Bei diesem Prozess werden die tieferen Lichtfrequenzen (entsprechend den roten Spektralfarben) stärker erhöht als die höheren Frequenzen (blaue Spektralfarben). Rechts: Konstruktion der Spektrallinse. Ein Testpuls wird in einen Lichtleiter eingekoppelt, den man sich als eine fehlende Reihe von Löchern in einer perforierten Siliziummembran vorstellen kann. Dabei dringen die roten Spektralanteile des Lichtpulses tief in die perforierten Bereiche ein, während die blauen Anteile in der Mitte des Lichtleiters konzentriert bleiben. Nun stört ein intensiver Kontrollpuls das Testlicht, allerdings nur dessen rote Spektralanteile, weil die blauen Anteile von einem dünnen Goldstreifen vor dem Kontrollpuls geschützt werden. Infolgedessen werden nur die roten Lichtanteile beeinflusst und zu höheren Frequenzen (d. h. blauen Spektralfarben) verschoben, ganz so, wie im linken Bild dargestellt.


Scientists of the Fritz Haber Institute, the Institute for Atomic and Molecular Physics (AMOLF) in Amsterdam, and the University of St Andrews have constructed a spectral lens, that is, a nanostructure that can either expand (“magnify”) or compress (“demagnify”) the spectrum of light (see left figure). Such lens could find application in spectroscopy (to improve the resolution of spectrometers) or in optical data transfer (to reduce the bandwidth of a signal of finite duration). These results will be published in one of the next issues of the esteemed journal Physical Review Letters.

How does the spectral lens work? In order to obtain spectral compression (demagnification), the scientists raise the frequency of light, but ensure that lower frequency components undergo a larger frequency shift than the higher frequencies (see left image). A photonic-crystal waveguide (see right image) is used to separate the frequencies in space: the higher frequencies are concentrated in the center of the waveguide whereas the lower frequencies spread out into the surrounding photonic crystal. The spectral lens consists of such a waveguide in combination with a shadow mask. By hitting it with an intense laser pulse (“pump” in right image), the lower frequencies are shifted more as the mask protects the waveguide center. In a first experiment, the researchers were able to compress the spectrum of an ultrashort test pulse by 12%. Much higher compression will be possible by optimizing the parameters of the structure.

D. M. Beggs,T. F. Krauss, L. Kuipers, and T. Kampfrath, Phys. Rev. Lett. 108, 033902 (2012)

URL: http://link.aps.org/doi/10.1103/PhysRevLett.108.033902.
DOI: 10.1103/PhysRevLett.108.033902


Calculating molecular properties within milliseconds

Monday, 13th February 2012General science information

Press Release from TU Berlin and Fritz-Haber-Institute of the Max-Planck-Gesellschaft

Rapid procedure for the exploration of chemical compound space unites quantum chemistry with artificial intelligence
By combining quantum chemistry with artificial intelligence (Machine Learning), researchers at the Institute for Pure and Applied Mathematics at the University of California—Los Angeles achieved a scientific breakthrough expected to aid in exploring chemical compound space, i.e. the virtual space populated by all possible chemical compounds. The interdisciplinary team from the Technical University Berlin (Germany), the Fritz-Haber Institute of the Max-Planck Society (Germany), and the Argonne Leadership Computing Facility (United States) dramatically increased the speed of calculating energies of small molecules with quantum chemical accuracy. Quantum chemical methods permit scientists to calculate molecular properties on a computer from first principles (i.e., without having to conduct any experiments)—they are necessary for many chemical applications such as catalysis, or the discovery of novel materials. Previously, such calculations demanded intensive computational resources.
Machine Learning, on the other hand, generates predictive models based on examples. While common in daily life, such as in Google’s internet search engines or Amazon’s book suggestions, it is also used in scientific domains, such as genetic research or brain computer interfaces. When applied to quantum chemistry, thousands of quantum chemical reference energies have been calculated in order to “learn” a molecular model. The resulting Machine permits the prediction of molecular properties with comparable accuracy within milliseconds, instead of hours. Such speed-up paves the way for highly accurate calculations of unprecedentedly many molecules.

More information can be obtained from:

Prof. Dr. Klaus-Robert Müller, TU Berlin, Tel.: 030/314-78620, E-Mail: klaus-robert.mueller@tu-berlin.de
Dr. Alexander Tkatchenko, Fritz-Haber-Institut der Max-Planck-Gesellschaft, Tel.: 030/8413-4812, E-Mail: tkatchen@fhi-berlin.mpg.de