Comparing data driven and physics inspired models for hopping transport in organic field effect transistors

• Main Authors: Lakshminarayanan, Madhavkrishnan, Dutta, Rajdeep, Repaka, D. V. Maheswar, Jayavelu, Senthilnath, Leong, Wei Lin, Hippalgaonkar, Kedar
• Other Authors: School of Materials Science and Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Mechanical engineering; Computational Science; Materials for Devices; Organic Molecules in Materials Science; Scaling Laws; Scientific Data; Theory and Computation
• Online Access: https://hdl.handle.net/10356/154934
• Institution: Nanyang Technological University

The past few decades have seen an uptick in the scope and range of device applications of organic semiconductors, such as organic field-effect transistors, organic photovoltaics and light-emitting diodes. Several researchers have studied electrical transport in these materials and proposed physical models to describe charge transport with different material parameters, with most disordered semiconductors exhibiting hopping transport. However, there exists a lack of a consensus among the different models to describe hopping transport accurately and uniformly. In this work, we first evaluate the efficacy of using a purely data-driven approach, i.e., symbolic regression, in unravelling the relationship between the measured field-effect mobility and the controllable inputs of temperature and gate voltage. While the regressor is able to capture the scaled mobility well with mean absolute error (MAE) ~ O(10–2), better than the traditionally used hopping transport model, it is unable to derive physically interpretable input–output relationships. We then examine a physics-inspired renormalization approach to describe the scaled mobility with respect to a scale-invariant reference temperature. We observe that the renormalization approach offers more generality and interpretability with a MAE of the ~ O(10–1), still better than the traditionally used hopping model, but less accurate as compared to the symbolic regression approach. Our work shows that physics-based approaches are powerful compared to purely data-driven modelling, providing an intuitive understanding of data with extrapolative ability.