QuantumPatch

QuantumPatch (QP) corresponds to spectroscopy-type experiments commonly performed on organic thin films. It is an efficient tool for the calculation of microscopic electronic structures and corresponding properties of molecules embedded in organic thin films, such as the HOMO LUMO energies, ionization energy (IP) and electron affinity (EA), intermolecular electronic couplings, reorganization energy, or distributions thereof. The computation of thesequantities can help to streamline time-consuming and costly experimental R&D efforts by efficient pre-screening of material candidates or by establishing structure-function relationships. Further, they serve as input for device simulations with LightForge

Electronic properties of organic molecules are modified when embedded in thin films, as illustrated in Figure 1. Molecular properties should therefore not be computed either for molecules in vacuum, but for molecules embedded in thin films, taking into account their unique electrostatic environment and structure on a full quantum-mechanical level. 

Figure 1: Schematic view of the shell setup of the QuantumPatch calculation

To this end QP computes the electronic structure of molecules  by self-consistent equilibration of the charge densities of all molecules using coupled single molecule-DFT level calculations. Electronic properties are computed on a representative subset of molecules near the center of atomistic morphologies, for example generated with Deposit. Surrounding molecules have to be included to account for polarization effects. A flexible shell setup as illustrated below, allows the application of different levels of computational accuracy (e.g. DFT or semi-empirical methods, such as DFTB or XTB), to optimize runtime without reducing the accuracy significantly.

Further details on the QP method are provided at our QP documentation page and in the following references:

  1. J. Chem. Theory Comput., 2014, 10 (9), Pages 3720-3725
  2. J. Chem. Theory Comput., 2015, 11 (2), Pages 560-567
  3. Procedia Comput. Sci., 2016, 80, Pages 1244-1254
  4. Adv. Functional Mater. 2016, 26 (31), Pages 5757-5763
  5. Phys. Rev. B 91, 2015, 155203 
  6. Adv. Mater. 2017, 29, 1703505.
  7. Adv. Mater. 2019, 31, 1808256.
  8. Phys. Rev. B 93, 2016, 195209
  9. ACS Nano, 2020, doi: 10.1021/acsnano.0c00384
  10. https://doi.org/10.26434/chemrxiv.11991441.v1
  11. arXiv:1908.11854 [cond-mat.soft]