The first step in developing an updated model is to determine the input descriptors and their calculation method. Since the model will be used for inverse design approaches, such as genetic algorithms, the quantum chemical calculations should be quick, yet correlate well with the experimental data. Five semi-empirical methods were examined: sTD-DFT , sTDA , sTD-DFT-xTB , sTDA-xTB , and GFN2-xTB , and their performance was judged by its calculation of ionization energy, electron affinity, optical bandgap, and maximum absorption wavelength. It is important to note that the experimental data have much variation in the reported values. For example, there are 29 reports of ionization energy and electron affinity for the non-fullerene acceptor Y6. To get the most accurate reference value, a kernel density plot was made and the value at the maximum was used for reference (ESI Fig. ). The mean absolute percent error (MAPE) of each model is calculated (ESI Fig. ), with all models performing similarly for optical properties, and the non-xtb methods having much lower error for the ionization energy and electron affinity. Despite sTD-DFT having the lowest error, calculations on the relatively large non-fullerene acceptors can take minutes to hours. Instead, sTD-DFT-xTB requires only seconds and an accuracy similar to that of sTD-DFT, giving it the best balance of time and accuracy. Thus, it was selected as the main computational method for the present work.