Organic optoelectronic materials are under widespread development to complement, or displace existing materials in applications including organic light-emitting diodes (OLED), and organic photovoltaics (OPV). These materials are selected or designed according to their internal optoelectronic and condensed-phase properties with concern for efficient charge injection and transport, and desired chemical and thermophysical stability. Atomic scale simulation using quantum chemistry, molecular dynamics, and QSPR modeling in the Schrödinger Materials Science Suite can provide critical insight and understanding to help identify the most promising solutions for experimental development, and to advise the selection of materials for use in optimized applications.
Organic electronic materials are categorized according to their molecular properties that include HOMO and LUMO energy levels, Marcus theory reorganization energies for holes and electrons (λh, λe), and triplet excited state energy (ET1). The Materials Science Suite provides users with a robust and automated simulation module (Optoelectronics Calculation & Analysis), applying validated models to efficiently and accurately calculate optoelectronic properties using the MS Jaguar quantum mechanics engine.1 Table 1 presents calculated results for NPB, mCP, Ir(ppy)3 and AlQ3 – well-known hole-transport, host, dopant and electron-transport materials. Where available, experimental values are included in parentheses for comparison, showing good agreement in each case.
Table 1. Selected organic optoelectronic properties for NPB, mCP, Ir(ppy)3 and AlQ3 calculated using DFT (along with experimental values for comparison).
|NPB||-5.40 (-5.4a, -5.19d)||-2.29 (-2.4a, -2.06d)||0.16||0.28||2.4 (2.3a, 2.5d)|
|mCP||-5.91 (-5.68d)||-1.98 (-1.75e)||0.15||0.07||3.1 (3.15d)|
|Ir(ppy)3||-5.49 (-5.2b, -5.6c, -5.27d)||-2.32 (-2.8b, -3.0c, -2.10d)||0.13||0.10||2.5 (2.4a, 2.54d)|
|AlQ3||-5.59 (-5.17d, -5.36e)||-2.74 (-2.85d, -2.51e)||0.22||0.20||2.1 (2.11e)|
a S.W. Liu, Y. Divayana, A.P. Abiyasa, S.T. Tan, H.V. Demir and X.W. Sun, App. Phys. Lett., 101, 093301, (2012);b. S.H. Kim, J. Jang and J.Y. Lee, App. Phys. Lett., 90, 223505, (2007); c http://www.sigmaaldrich.com; CAS # 94928-86-6 (accessed 10/19/15); d L.-S. Liao, X. Ren, C.A. Pellow and Y.-S. Tyan (Global OLED Tech), U.S. Patent US8034465 B2 (2007); e W.J. Begley, L.-S. Liao and C.A. Pellow (Eastman Kodak), U.S. Patent Application WO2009097108 A1 (2008).
In addition to suitable internal properties, organic optoelectronic materials must also possess favorable chemical and morphological stability to achieve required lifetimes. Chemical stability in the ground and excited states can be assessed through automated bond dissociation and reactivity modules (e.g. Bond and Ligand Dissociation, and Reaction Energetics Enumeration modules) driving MS Jaguar DFT calculations. Automated calculations of ET1 and ligand-dissociation energies were carried out for a set of 49 diverse Ir-metal complexes. Figure 1 illustrates one of the critical challenges faced in the development of phosphorescent light emitting dopants – with increasing ET1 (bluer emission energy) the metal-ligand bond dissociation energy decreases. In silico evaluation of chemical stability, in addition to internal properties can provide critical guidance in the development of high performance materials with extended lifetimes.
Figure 1. Scatter plot of DFT-calculated triplet excited state (ET1) and triplet state metal-ligand dissociation (T1 BDE) energies.
In optoelectronic devices, the active materials are often incorporated as solid amorphous thin films. In addition to favorable molecular optoelectronic properties and chemical stability, organic electronic materials also need to have desired morphological and thermophysical properties in the condensed phase. Classical molecular dynamics (MD) simulations of fully periodic disordered solids can be carried out using the highly efficient Desmond MD, running on CPU or GPU.2 The accuracy of the condensed phase simulations are determined by the simulation time and the highly refined modern force fields.
Critical thermophysical properties, such as the glass-transition temperature (Tg) and coefficient of thermal expansion (CTE), can be calculated by automatically running successive NPT simulations over a range of temperatures (Thermophysical Properties module). The specific volume (1/ρ) versus T data for the organic hole-transport material TPD is shown in Figure 2. The data clearly differentiates the low-temperature glassy regime (green) and high-temperature rubbery regime (blue). The linear best-fit equations for the two regions and their intersection determines the predicted CTE and Tg. Correcting for differences in cooling rate between experiment and simulation gives a predicted Tg of 331 K, comparable to the experimental value of 338 K.
Figure 2. Specific volume vs. temperature data for TPD (inset) calculated using NPT MD simulation using Desmond with the OPLS2005 force-field.
Multistep calculation workflows, combining DFT and MD techniques, allow the calculation of more advanced organic optoelectronic material properties such as carrier mobility. Using the atomic morphology computed from Desmond MD, the electronic coupling for molecular dimer pairs (HAB) composing the solid can be evaluated using MS Jaguar. This enables calculation of Marcus theory hopping rates for individual dimers, which can be used to rank organic optoelectronic materials based on estimated charge mobility.3 Figure 3 illustrates prediction of charge mobility using atomic scale simulation for the hole-transport materials NPB, CzC, 2TNATA, TCTA, TPD, spiro-TPD, o-BPD, m-BPD, and p-BPD.
Figure 3. Scatter plot showing comparison between percolation-corrected hole mobility predictions, µh,p vs. experimental hole mobility, µh,exp.
These and other capabilities (e.g. SO-TDDFT), make the Schrödinger Materials Science Suite extremely powerful for efficiently and accurately calculating the internal electronic properties and condensed-phase thermophysical properties for organic optoelectronic materials. The predictive reliability of DFT and MD simulations, provides understanding and rationale for observed material behavior, and enables informed selection of materials for use in optimized devices. Robust and efficient automation extends the role of simulation to exploration of chemical design space, informing synthetic development and driving innovation in the development of next-generation organic optoelectronic solutions.
- M.D. Halls, D. Yoshidome, T.J. Mustard, A. Goldberg, H.S. Kwak and J.L. Gavartin, “Atomic-scale Simulation for the Analysis, Optimization and Accelerated Development of Organic Optoelectronic Materials”, NIHON GAZO GAKKAISHI (J. Imaging Soc. Japan), 54, 561, 2015.
- D.R. Evans, H.S. Kwak, D.J. Giesen, A. Goldberg, M.D. Halls and M. Oh-e, “Estimation of Charge Carrier Mobility in Amorphous Organic Materials using Percolation Corrected Random-Walk Model”, Org. Electron., 29, 50, 2016.
- M.D. Halls, D.J. Giesen, T.F. Hughes, A. Goldberg, Y. Cao, H.S. Kwak and J. Gavartin, “Virtual Screening for OLED Materials”, Proc. SPIE 9183, Organic Light Emitting Materials and Devices XVIII, 91832G 2014.
- M.D. Halls, D.J. Giesen, T.F. Hughes, A. Goldberg and Y. Cao, “High-Throughput Quantum Chemistry and Virtual Screening for OLED Material Components”, Proc. SPIE 8829, Organic Light Emitting Materials and Devices XVII, 882926, 2013.
- M.D. Halls, P.J. Djurovich, D.J. Giesen, A. Goldberg, J. Sommer, E. McAnally and M.E. Thompson, “Virtual Screening of Electron Acceptor Materials for Organic Photovoltaic Applications”, New J. Physics, 15, 105029, 2013.
Collaborators and Advisors
- OPV Materials
Professor Mark Thompson, University of Southern California, USA
- OLED/TADF Materials
Professor Chihaya Adachi, OPERA and Kyushu University, Japan
- OLED Materials
Professor Jang-Joo Kim, Seoul National University, South Korea
- A.D. Bochevarov, E. Harder, T.F. Hughes, J.R. Greenwood, D. Braden, D. M. Philipp, D. Rinaldo, M.D. Halls, J. Zhang and R.A. Friesner, “Jaguar: A High-Performance Quantum Chemistry Software Program with Strengths in Life and Materials Sciences”, Int. J. Quantum Chem., 113, 2110, 2013.
- Salmon, Y. Shan and D.E. Shaw, "Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters” Proceedings of ACM/IEEE Conference on Supercomputing (SC06), Tampa, Florida November 11-17, 2006.