4.7 Article

Predictive Model of Charge Mobilities in Organic Semiconductor Small Molecules with Force-Matched Potentials

Journal

JOURNAL OF CHEMICAL THEORY AND COMPUTATION
Volume 16, Issue 6, Pages 3494-3503

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.jctc.0c00211

Keywords

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Funding

  1. Department of Energy, Basic Energy Sciences [DE-SC0010419]
  2. Office of Science of the U.S. Department of Energy [DE-AC02-05CH11231]
  3. LLNL [DE-AC52-07NA27344]
  4. ERC [862102]
  5. EPSRC [EP/N021754/2] Funding Source: UKRI
  6. U.S. Department of Energy (DOE) [DE-SC0010419] Funding Source: U.S. Department of Energy (DOE)
  7. European Research Council (ERC) [862102] Funding Source: European Research Council (ERC)

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Charge mobility of crystalline organic semiconductors (OSC) is limited by local dynamic disorder. Recently, the charge mobility for several high mobility OSCs, including TIPS-pentacene, were accurately predicted from a density functional theory (DFT) simulation constrained by the crystal structure and the inelastic neutron scattering spectrum, which provide direct measures of the structure and the dynamic disorder in the length scale and energy range of interest. However, the computational expense required for calculating all of the atomic and molecular forces is prohibitive. Here we demonstrate the use of density functional tight binding (DFTB), a semiempirical quantum mechanical method that is 2 to 3 orders of magnitude more efficient than DFT. We show that force matching a many-body interaction potential to DFT derived forces yields highly accurate DFTB models capable of reproducing the low-frequency intricacies of experimental inelastic neutron scattering (INS) spectra and accurately predicting charge mobility. We subsequently predicted charge mobilities from our DFTB model of a number of previously unstudied structural analogues to TIPS-pentacene using dynamic disorder from DFTB and transient localization theory. The approach we establish here could provide a truly rapid simulation pathway for accurate materials properties prediction, in our vision applied to new OSCs with tailored properties.

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