dopingModel

Controlling energy levels and Fermi level en route to fully tailored energetics in organic semiconductors

Authors: Ross Warren¹, Alberto Privitera¹, Pascal Kaienburg¹, Andreas E. Lauritzen¹, Oliver Thimm², Jenny Nelson³ & Moritz Riede¹

Affiliations:
¹ Clarendon Laboratory, Department of Physics, University of Oxford, Parks Road, Oxford OX1 3PU, UK.
² IEK5-Photovoltaics, Forschungszentrum Jülich, 52425 Jülich, Germany.
³ Department of Physics, Imperial College London, Exhibition Road, London SW7 2AZ, UK.

This repository is associated with a study that has been published in Nature Communications (DOI 10.1038/s41467-019-13563-x). It was created so that our research is both transparent and reproducible.

The repo contains the data and code for:

• PDS data (Figure 1)
• EPR data (Figure 2)
• Statistical model code (Figures 3, 4 and Supplementary Figures 6,7).

Requirements

• python3
• cython
• pandas

How to use

1. Clone or download this repository

2. Build the statistical model cython module:

   $ python setup.py build_ext --inplace

3. Run whichever script e.g.

   $ python fig-4a-fixed-dopant-EA.py

Reference

If you find this code or data helpful, please reference it as:

Warren, R., Privitera, A., Kaienburg, P. et al. Controlling energy levels and Fermi level en route to fully tailored energetics in organic semiconductors. Nat Commun 10, 5538 (2019) doi:10.1038/s41467-019-13563-x