Figure shows the predictions of the SISSO models selected based on considerations of cost and accuracy as a function of the GW +BSE reference values of the singlet-triplet gap. Parity plots for all other SISSO models are provided in the ESI. We note that the energy range of GW +BSE reference data for the singlettriplet gap is significantly smaller than the energy range of the singlet and triplet exciton energies (see the distributions in Figure ). Therefore, the deviations of the SISSO model predictions appear larger in comparison to the reference data even though they are of comparable magnitude or even smaller than the deviations of the SISSO models for the singlet and triplet exciton energies. In the discussion of the outliers we focus on materials with relatively small singlet-triplet gaps because these would be of most interest for OLEDs. The very-low-cost model M 1,2 & M 2,1 has several significant outliers in the range of singlet-triplet gaps below 0.6 eV. These include materials that are also among the outliers for the singlet and triplet exciton energy models, PU-JQIV, FACPEE, and coronene. Hexaphenylbenzene (CSD refer-ence code HPHBNZ03) comprises phenyl rings connected by single bonds like some of the outliers for the singlet and triplet excitation energies. Trinaphtho[1,2,3,4-fgh:1',2',3',4'-pqr:1",2",3",4"-za_1_b_1_]trinaphthylene (CSD reference code GUQZUP) and ( )helicene (CSD reference code DBPHEN02) are not structurally or chemically distinct from most of the materials in the PAH101 set. In addition, they do not have any primary features with unusual values. It is possible that the relatively large number of outliers, especially in the low singlet-triplet gap range, is a reflection of the difficulty of training reliable models to predict very small target values, in particular considering that there are few materials with a singlet-triplet gap below 0.5 eV in the training data (See Figure ). Figure demonstrates a three-stage screening workflow constructed based on the SISSO models selected for the singlet-triplet gap. As shown in Figure , the PAH101 dataset does not contain any materials with singlet-triplet gaps that are small enough to be of interest for OLED applications. For demonstration purposes, we screen for materials with a singlet-triplet gap below 0.5 eV. There are 9 such materials in total in the PAH101 set. The screening thresholds for each stage of the workflow are set to 0.5 eV + n× RMSE, where n=1,2,3 and RMSE refers to the training set RMSE of each model. With n=1, the first-stage ensemble model of M 1,2 & M 2,1 screens out most of the materials, whose singlet-triplet gap is above 0.5 eV, leaving only 8 false positives. However, 4 of the 9 materials whose singlet-triplet gaps are below 0.5 eV are also screened out, leaving only 5 true positives. Further screening by the second-stage ensemble model of M 1,3 & M 3,1 and subsequently the third-stage model, M 3,3 , reduces the number of false positives to 5 and 3, respectively, without losing any additional true positives. Setting n=2 is not beneficial because it results in the same 5 true positives but a significantly higher number (15) of false positives passing the screening. With n=3, 8 true positives pass the screening along with 26 false positives. One may define the screening thresholds depending on their tolerance for false positives. With the thresholds set to one RMSE, the workflow presented here effectively eliminates most of the materials that are not of interest, which significantly reduces the number of candidates that need to be evaluated using more accurate and computationally expensive methods. Training better models for predicting the singlet-triplet gap would require acquiring more GW +BSE data for materials with small singlet-triplet gaps. To this end, the present models could be used to select materials for data acquisition.