An active machine learning method for discovering new semiconductors

A research team from the Technical University of Munich (TUM) and the Fritz Haber Institute in Berlin is using active machine learning in the search for suitable molecular materials for new organic semiconductors, the basis for organic field effect transistors (OFETs), light-emitting diodes (OLEDs) and organic solar cells (OPVs). To efficiently deal with the myriad of possibilities for candidate molecules, machine learning proves an invaluable tool. It is envisaged that organic semiconductors will enable important future technologies such as portable solar cells or rollable displays. For such applications, improved organic molecules – which make up these materials – need to be discovered. For material discovery tasks of this nature researchers are increasingly utilising machine learning methods, training on data from computer simulations or experiments.