Machine Learning for Understanding the Relationship between the Charge Transport Mobility and Electronic Energy Levels for n‐Type Organic Field‐Effect Transistors

Abstract Organic field‐effect transistors (OFETs) have received considerably more attention than inorganic‐based field‐effect transistors for use in next generation of organic circuits. There are a number of variables, for example, the ordering of the OFETs, their energy levels, and the material used for source/drain electrodes, that influence the magnitude of charge transport mobility. Importantly, a suitable energy level match between highest occupied molecular orbital (HOMO) or lowest unoccupied molecular orbital (LUMO) energy level and work function of the electrodes may have a large influence on the measured mobility. An informatics approach, specifically use of machine learning, is proposed for charge transport mobility prediction. Gradient Boosting and Random Forest regression algorithms are used to model previous experimental datasets and HOMO and LUMO energy levels of n‐type materials are optimized using expected machine‐learning methods. The results reveal that Random Forest model benefits the functional analysis of n‐type OFETs in three ways: 1) it provides better understanding of current n‐type organic materials, 2) it may guide the choice of n‐type organic materials and conducting electrodes, and 3) it measures the tradeoffs between the charge transport mobility and electronic energy levels for n‐type OFETs.