Low-cost machine learning prediction of excited state properties of iridium-centered phosphors
Name
d2sc06150c.pdf
Description
Published version
Size
1.02 MB
Format
Adobe PDF
Checksum (MD5)
f7aa52b5dc21a86891383945d0a53eb2
Author(s)
Terrones, Gianmarco G
Duan, Chenru
Nandy, Aditya
Kulik, Heather J
Date Issued
2023
Journal
Chemical Science
Publisher
Royal Society of Chemistry
Citation
Chem. Sci., 2023,14, 1419-1433
Version
Final published version
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 accuracy competitive with or superseding that of TDDFT. We conduct feature importance analysis to determine that high cyclometalating ligand ionization potential correlates to high mean emission energy, while high ancillary ligand ionization potential correlates to low lifetime and low spectral integral. As a demonstration of how our ML models can be used for HTVS and the acceleration of chemical discovery, we curate a set of novel hypothetical iridium complexes and use uncertainty-controlled predictions to identify promising ligands for the design of new phosphors while retaining confidence in the quality of the ANN predictions.
MIT Department
Massachusetts Institute of Technology. Department of Chemistry
Massachusetts Institute of Technology. Department of Chemical Engineering
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1039/d2sc06150c
