include the effects of the energetically disordered 3D structure and the detailed inter- molecular hopping rate functions, if needed depending on the detailed distribution of relative orientations, distances and transfer integrals between all molecules and based on a “full-quantum” approach that includes the effect of molecular reorganization by taking contributions from all vibrational modes into account. On the other hand, the method is generally computationally less expensive than 3D-KMC simulations, in particular below ff . This is shown in Fig. 1A, which gives typical values for the simulation time required t o obtain for a 100 nm unipolar single-layer device the cur rent density at room temperature with a 1% accuracy, using 1D-DD, 3D-ME and 3D-KMC simulations. The lower and upper limits are obtained for materials with a very weak and strong Gaussian disorder (standard deviations = 0.05 and 0.12 eY), respectively. In addition to steady-state modeling, 3D-ME simulations can also be used t o predict transient properties of devices, such as obtained using dark injection and impedance spectroscopy measurements. In these cases, 3D-ME simulations were found t o provide a much better agreement with experimental results than 1D-DD simulations. Here the ID drift-diffusion (1D-DD) simulation employs the Bonham and Jarvis method, calculated with a Mathematica™code on a standard desktop computer. The 3D master-equation (3D-ME) and 3D kinetic Monte Carlo (3D-KMC) simulations are performed on a computer cluster. The 3D-KMC simulation software used is Bumble bee, provided by Simbeyond B.V. (simbeyond.com) The 3D-ME simulation method is presented in this work. 1D-DD simulation is computationally the most efficient, but it is not mechanistic. At voltages below , 3D-ME is the only feasible approach capable of 3D predictive modeling. We note that when carrying out the simulations for a device based on a semiconductor with a larger energy gap, but with identical injection barriers, essentially identical results will be obtained when expressed relative t o the (larger) value of 1-4;• We furthermore note that the simulation time shown here is obtained for normal numerical precision, and that for different hardware confi gura tions, coding techniques, and device parameters, the actual simulation time may vary strongly. The readers are suggested t o focus more on the general trends shown in this figure rather than the exact values of the simulation time. In addition t o steady- state modeling, 3D-ME simulations can also be used t o predict transient properties of devices, such as obtained using dark injection and impedance spectroscopy mea surements. I these cases, 3D-ME simulations were found t o provide a much better