Machine Learning Develops Fluorescent Tools for Data Encryption

Researchers in Switzerland and Australia have used machine learning to crack the code governing charge transfer and colour emission in chains of molecules. Chains of molecules, known as polymers, can be put together in patterns to create different visual effects, such as emitting a certain colour when exposed to ultraviolet light or other light sources. Polymers are used in data storage, security inks, organic light-emitting diodes (OLEDs), and even the solar energy industry. Until now, getting the molecules in the right order to create the desired effect has been a slow process of trial and error, limiting its practical application and usefulness. To solve this problem, Exciton Science Research Fellow Dr Nastaran Meftahi of RMIT University, under the supervision of Professor Salvy Russo, trained a machine learning model to better understand the behaviour occurring inside and between the molecules.