Lakshminarayanan Madhavkrishnan, Dutta Rajdeep, Repaka D V Maheswar, Jayavelu Senthilnath, Leong Wei Lin, Hippalgaonkar Kedar
School of Electrical Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore.
Institute of Materials Research & Engineering, Agency for Science, Technology and Research (A*STAR), Singapore, 138632, Singapore.
Sci Rep. 2021 Dec 8;11(1):23621. doi: 10.1038/s41598-021-02737-7.
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), 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), 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.
在过去几十年中，有机半导体的器件应用范围和领域有所增加，例如有机场效应晶体管、有机光伏器件和发光二极管。一些研究人员研究了这些材料中的电输运，并提出了物理模型来描述具有不同材料参数的电荷输运，大多数无序半导体表现出跳跃输运。然而，在不同模型之间缺乏关于准确和统一描述跳跃输运的共识。在这项工作中，我们首先评估使用纯数据驱动方法（即符号回归）来揭示测量的场效应迁移率与温度和栅极电压的可控输入之间关系的有效性。虽然回归器能够以平均绝对误差（MAE）O(10)很好地捕捉缩放后的迁移率，比传统使用的跳跃输运模型更好，但它无法得出具有物理可解释性的输入-输出关系。然后，我们研究一种受物理启发的重整化方法，以相对于尺度不变参考温度来描述缩放后的迁移率。我们观察到，重整化方法具有更高的通用性和可解释性，MAE为O(10)，仍然比传统使用的跳跃模型更好，但与符号回归方法相比准确性较低。我们的工作表明，与纯数据驱动建模相比，基于物理的方法更强大，能够提供对数据的直观理解并具有外推能力。
