Predicting the excited-state properties of crystalline organic semiconductors using GW+BSE and machine learning

The excited-state properties of molecular crystals are important for applications in organic electronic devices. The GW approximation and Bethe-Salpeter equation (GW+BSE) is the state-of-the-art method for calculating the excited-state properties of crystalline solids with periodic boundary conditions. We present the PAH101 dataset of GW+BSE calculations for 101 molecular crystals of polycyclic aromatic hydrocarbons (PAHs) with up to ~500 atoms in the unit cell. The data records include the GW quasiparticle band structure, the fundamental band gap, the static dielectric constant, the first singlet exciton energy (optical gap), the first triplet exciton energy, the dielectric function, and optical absorption spectra for light polarized along the three lattice vectors. Based on this dataset, we use the sure-independence-screening-and-sparsifying-operator (SISSO) machine-learning algorithm to train predictive models for the fundamental gap, the optical gap, the first triplet excitaiton energy, the singlet-triplet gap, and the singlet exciton binding enrgy of organic semiconductor crystals.