Developing a data infrastructure and tools for organic semiconductor discovery

Organic semiconductors, derived from π-conjugated molecules and polymers, offer significant potential for (opto)electronics applications with finely tuned electronic, optical, and mechanical properties. While chemists have the capacity to manipulate molecular-scale electronic, redox, and optical properties that in turn determine organic semiconductor response, the chemical design space for organic semiconductors is vast, and it is not clear that current Edisonian practices of make, test, and repeat offer the ability to identify systems as efficiently as required for many needed technology advances. Here we will discuss the development of the OCELOT (Organic Crystals for Electronic and Light Oriented Technologies) data infrastructure for the collection and dissemination of experimental and computational data for π-conjugated molecules and organic semiconductors, developed under the auspices of the FAIR data principles with the aim that all data and models are AI (artificial intelligence)-ready. Among the data tools offered on OCELOT, we will discuss the development of machine-learning models to predict molecular and semiconductor electronic, redox, and optical properties.