Machine Learning-Based Prediction of Molecular Packing in Organic Semiconductors

Abstract We developed a machine learning model that predicts with 91.0% accuracy whether a π-conjugated organic semiconductor adopts a herringbone packing motif, a key motif associated with high carrier mobility. In addition, model interpretation identifies the molecular fragments that promote or suppress herringbone packing, providing practical design guidelines for developing high-performance organic semiconductors.