Low-cost machine learning prediction of excited state properties of iridium-centered phosphors.

ABSTRACT: Prediction of the excited state properties of photoactive iridium complexes challenges ab initio methods such as time-dependent density functional theory (TDDFT) both from the perspective of accuracy and of computational cost, complicating high-throughput virtual screening (HTVS). We instead leverage low-cost machine learning (ML) models and experimental data for 1380 iridium complexes to perform these prediction tasks. We find the best-performing and most transferable models to be those trained on electronic structure features from low-cost density functional tight binding calculations. Using artificial neural network (ANN) models, we predict the mean emission energy of phosphorescence, the excited state lifetime, and the emission spectral integral for iridium complexes with accura

SUBMITTER: Terrones GG 

PROVIDER: S-EPMC9906783 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature