Predicting phosphorescence energies and inferring wavefunction localization with machine learning.

ABSTRACT: Phosphorescence is commonly utilized for applications including light-emitting diodes and photovoltaics. Machine learning (ML) approaches trained on ab initio datasets of singlet-triplet energy gaps may expedite the discovery of phosphorescent compounds with the desired emission energies. However, we show that standard ML approaches for modeling potential energy surfaces inaccurately predict singlet-triplet energy gaps due to the failure to account for spatial localities of spin transitions. To solve this, we introduce localization layers in a neural network model that weight atomic contributions to the energy gap, thereby allowing the model to isolate the most determinative chemical environments. Trained on the singlet-triplet energy gaps of organic molecules, we apply our method t

SUBMITTER: Sifain AE 

PROVIDER: S-EPMC8336587 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature