ML Unlocks Molecular Tools for Data Encryption

Researchers in Switzerland and Australia cracked the proverbial code, creating molecule chains to display designated colors in response to such stimuli as light, chemicals and energy. They used machine learning (ML) to address charge transfer and color emission in chains of molecules.

A composite image displaying a 3D-printed QR code with a pattern that is deciphered under exposure to ultraviolet light. (Image Credit: ETH Zurich)

These chains, or polymers can be assembled in patterns for different visual effects, for example emitting a certain color when exposed to UV light. The polymers are used in data storage, security, organic light-emitting diodes (OLEDs), and solar energy. An ML model was trained to better understand behavior inside and between the molecules. With very limited data to study, the model proved to be a fast learner.

The team was able to confirm that the transfer of charge within the chain or network is dependent on the pattern the molecules are arranged in, and the distance between them.

The researchers can now create molecule chains that display designated colors in response to different stimuli.

Read the full article here.

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