The workflow presented here demonstrates how ML models can be used to identify new potential TTA candidates with triplet exciton energies in the IR, optical gaps in the visible range, and favorable TTA energetics. We note that the optical gap SISSO models presented above and the SF driving force SISSO models from Ref. could be used to perform further fast screening of materials that pass the triplet exciton energy screening. Thus, the number of candidates selected for computationally expensive excited-state calculations can be significantly reduced. Figure shows a Pareto plot of the accuracy vs the computational cost of the SISSO-generated models for the singlet-triplet gap. The equations of all the models, as well as tabulated values of their cost and accuracy are provided in the ESI. The singlettriplet gap models are clustered in four groups of very-low, low, intermediate, and higher computational cost. The very-low cost group contains models, whose evaluation only requires the calculation of single molecule primary features. The low-cost group contains models, whose evaluation only requires calculating the crystal band gap, Gap C , in addition to single molecule features. Models in the intermediate cost group require the calculation of the crystal triplet formation energy, E C T , and/or the estimated crystal singlet-triplet gap, ∆E C ST . The models in the high-cost group also contain H ab .