Physical and chemical knowledge is leveraged in the choice of primary features and in the rules for combining them. SISSO has been demonstrated to work well with a relatively small amount of data for several different types of materials systems and properties. One application that requires predicting the excited-state properties of molecular crystals is singlet fission (SF), the conversion of one singlet exciton into two triplet excitons. The efficiency of solar cells can be boosted by augmenting traditional absorbers with SF materials. The SF material can convert photons with energies high above the traditional absorber's band gap into two charge carriers instead of losing their excess energy to heat. Currently, few classes of materials are known to undergo intermolecular SF in the solid state, and insufficient stability under operating conditions precludes their utilization in commercial modules. Therefore, there is a need for computational discovery of new SF materials. The primary criterion for a material to undergo SF is the thermodynamic driving force, i.e., the difference between the singlet exciton energy and twice the triplet exciton energy, E S -2E T , which can be calculated using GW+BSE. Recently, we have used SISSO to find models based on low-cost DFT properties that can reliably predict the GW+BSE SF driving force. SISSO generated several models that predicted the GW+BSE SF driving force with errors below 0.2 eV. Based on considerations of accuracy and computational cost, two SISSO models were selected to build a two-step hierarchical classifier for screening promising candidates for SF. To train SISSO, we generated a dataset of GW +BSE calculations of the SF driving force of 101 molecular crystals of polycyclic aromatic hydrocarbons (PAHs). PAHs are compounds comprising carbon and hydrogen atoms and containing multiple aromatic rings. Most SF materials are PAHs. In addition to SF, PAHs and their functionalized derivatives have versatile applications in organic electronic devices. To form the PAH101 set, crystal structures of unsubstituted PAHs (containing only C and H atoms) were extracted from the Cambridge Structural Database (CSD) . The PAH101 set contains several sub-classes including acenes, rylenes, zethrenes, as well as various compounds that do not belong to any particular family. As shown in Figure , the PAH101 set contains molecules ranging in size from 12 atoms in benzene (CSD Reference: BENZEN) to 136 atoms in two pyrene-stabilized acenes 9,11,13,22,24,26-Hexaphenyltetrabenzo[de, rs, wx, k 1 l 1 ]nonacene (CSD Reference: KECLAH), 9,11,13,14,15,16,18,20- The PAH101 dataset contains GW+BSE results for the electronic and optical properties of molecular crystals, as well as the DFT-level SISSO primary features used in Ref. . We envision this dataset being used for computational discovery of crystalline organic semiconductors and chromophores with desired properties for applications in various organic electronic devices. For example, the dataset contains information on optical gaps and absorption spectra, which could be used to search for chromophores that absorb light in a certain energy range. In addition, the dataset contains singlet and triplet excitation energies, which can be used to evaluate candidate chromophores for triplet-triplet annihilation (TTA) and thermally activated delayed fluorescence (TADF). TTA chromophores can be used for harvesting photons with energies below the absorption threshold of a solar cell by up-conversion of two low-energy triplet excitons into one singlet exciton that can be absorbed. TADF chromophores can be used to enhance the efficiency of OLEDs by converting electrically generated non-radiative triplet excitons into radiative singlet excitons. The dataset also contains GW band structures. The band dispersion is related to transport in crystalline organic semiconductors, which affects the performance of organic electronic devices such as field effect transistors (OFETs). Furthermore, this dataset can be used as a resource for comparing and benchmarking the performance of various electronic structure methods for calculating the electronic and optical properties of molecular crystals. Finally, this dataset can be used to augment other datasets, e.g., DFT datasets for molecular crystals or TDDFT datasets for isolated molecules to train multi-fidelity ML models for predicting various electronic and optical properties of molecular crystals. In summary, because the PAH101 is a unique set of GW+BSE data for molecular crystals, we expect it to be a resource of great usefulness to the computational community.