Berlin 2024 – wissenschaftliches Programm
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CPP: Fachverband Chemische Physik und Polymerphysik
CPP 49: Organic Electronics and Photovoltaics IV
CPP 49.3: Vortrag
Freitag, 22. März 2024, 10:00–10:15, H 0107
Static and Dynamic Energetic Disorder in Amorphous Organic Semiconductors via Physics-Inspired Machine Learning — •Ke Chen1, Karsten Reuter1, and Johannes T. Margraf1,2 — 1Fritz-Haber-Institut der MPG, Berlin — 2University of Bayreuth
Organic semiconductors (OSCs) are attractive for electronic applications due to their low cost and mechanical flexibility. However, the relatively low charge mobility (σ) of OSCs hinders their adoption in many commercial applications. Designing high-σ OSCs is therefore highly desirable. In thin film applications many OSCs form amorphous structures, where the static and dynamic energetic disorder of site-energies is one of crucial factors determining σ. Multiscale simulations based on density functional calculations and kinetic models can be used to analyze the energetic disorder in OSCs, but this is computationally prohibitive for realistic amorphous simulation cells containing thousands of molecules. In this context, machine learning (ML) can drastically accelerate these analyses by providing fast and accurate surrogates to density functional calculations. In this work, we apply our recently reported [1] physics-inspired ML approach to predict energy levels and orbital locations of OSC molecules in large amorphous systems. This opens the door towards the multiscale modeling of realistic amorphous OSCs.
[1] K. Chen et al., Chem. Sci. 14, 4913 (2023).
Keywords: Amorphous organic semiconductors; Static and dynamic energetic disorder; Physics-inspired machine learning