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Figures of merit are used to translate science to technology. For organic mixed ionic–electronic conductors (OMIECs), the product of mobility and volumetric capacitance, i.e., the µC* product, is the figure of merit used to guide the development of technologies ranging from bioelectronics to neuromorphics. While organic electrochemical transistors (OECTs) are used extensively to measure the µC* product, if there is a kink in the transistor current, then the µC* product is overestimated. Here, we show that the origin of the kink is a change in the rate with which the ions diffuse into the channel. We also discover that the OECT channel geometry has an unprecedented role on how the ions are distributed within the OMIEC. Finally, we observe that electronic charge carriers are first be injected and drift in the OMIEC, before ions can drift from the electrolyte into the OMIEC. Overall, the diverse charge transport phenomena identified here are essential for understanding the complexities of integrating novel OMIECs into traditional device structures, providing the key information needed to realize their promising application potential. 

Organic semiconductors (OSCs) offer the capacity for distinctive and finely tuned electronic, optical, thermal, and mechanical properties, making them of interest across a range of energy generation and storage, sensor, lighting, display, and electronics applications. The pathway from molecular building block design to material, however, is complicated by complex synthesis– processing–structure–property–function relationships that are inherent to OSCs. The adoption of artificial intelligence (AI) tools, including the subset of AI referred to as machine learning (ML), into the materials design and discovery pipeline offers significant potential to overcome the multifaceted roadblocks along this pathway. Here, we review recent advances in the application of AI/ML for OSCs, with a focus on the development and use of ML. We present a brief primer on ML models and then highlight efforts wherein ML is used to predict molecular and material properties and discover new molecular building blocks and OSCs. 

Atomistic molecular dynamics (MD) simulations of ordered and disordered P3HT at varying temperatures reveal distinct thermomechanical behavior in the amorphous and crystalline material domains.