Deep transfer learning for predicting frontier orbital energies of organic materials using small data and its application to porphyrin photocatalysts
An Su, Xin Zhang, Chengwei Zhang, Debo Ding, Yun‐Fang Yang, Keke Wang, Yuanbin She
Abstract
A deep transfer learning approach is used to predict HOMO/LUMO energies of organic materials with a small amount of training data.
Topics & Concepts
HOMO/LUMOPorphyrinDeep learningTransfer of learningComputer scienceMaterials scienceEnergy transferDatabaseChemistryArtificial intelligenceChemical physicsPhotochemistryMoleculeOrganic chemistryMachine Learning in Materials ScienceAdvanced Photocatalysis TechniquesCO2 Reduction Techniques and Catalysts