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TECH SPACE
A method to simplify pictures makes chemistry calculations a snap
by Staff Writers
Washington DC (SPX) Apr 01, 2015


The same code that Aspuru-Guzik and colleagues created to calculate the vibrational spectrum of anthracene was used on this lighthouse, showing that compressed sensing works for pictures, and for molecules. Image courtesy American Chemical Society. For a larger version of this image please go here.

A method smartphones use to simplify images when storage space is limited could help answer tough chemistry problems. In a report appearing in ACS Central Science, researchers apply this technique, called compressed, or compressive sensing, to quickly and efficiently address central questions, like predicting how molecules vibrate.

As these predictions get better and faster, researchers could get closer to the ideal of a "virtual laboratory," which could address many issues without ever lifting a pipet.

Alan Aspuru-Guzik and colleagues explain that compressed sensing has already been applied to experiments to reduce the amount of collected data to reproduce a given signal.

But its application to calculations of molecular properties has been limited. Compressed sensing could help by removing zeroes (extraneous information) from matrices, which are arrays of numbers widely used in science to understand and analyze physical phenomena.

One of the most computer-intensive calculations that chemists perform using these matrices is the simulation of vibrational spectra, essentially a painting picture of how a molecule bends and stretches. This movement is critical to a chemical's properties. So, Aspuru-Guzik's team decided to apply compressive sensing to address this challenge.

The researchers solved the vibrational spectrum of anthracene, which is relevant to molecular electronics, about three times faster with compressed sensing than with traditional methods.

Although compressed sensing is a form of approximation, they were able to show that the result was sufficiently accurate. The team also demonstrated that by using cheap, low-accuracy calculations, they needed fewer expensive, high-accuracy ones.


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