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Machine-learning models are capable of capturing the structure-propertyrelationship from a dataset of computationally demanding ab initiocalculations. Over the past two years, the Organic Materials Database (OMDB)has hosted a growing number of calculated electronic properties of previouslysynthesized organic crystal structures. The complexity of the organic crystalscontained within the OMDB, which have on average 82 atoms per unit cell, makesthis database a challenging platform for machine learning applications. In thispaper, the focus is on predicting the band gap which represents one of thebasic properties of a crystalline materials. With this aim, a consistentdataset of 12 500 crystal structures and their corresponding DFT band gap arereleased, freely available for download at https://omdb.mathub.io/dataset. Anensemble of two state-of-the-art models reach a mean absolute error (MAE) of0.388 eV, which corresponds to a percentage error of 13% for an average bandgap of 3.05 eV. Finally, the trained models are employed to predict the bandgap for 260 092 materials contained within the Crystallography Open Database(COD) and made available online so that the predictions can be obtained for anyarbitrary crystal structure uploaded by a user.

Band Gap Prediction for Large Organic Crystal Structures with Machine Learning