Physics-guided machine learning of excited-state properties for the design of high-performance TADF emitters
Physics-informed ML accurately predicts singlet–triplet gaps (Δ E ST ) and oscillator strengths ( f ), outperforming QM/ML baselines. Interpretable models reveal key factors and accelerate high-throughput discovery of TADF emitters for OLEDs.
Authors
- Bibhas Das (ORCID: https://orcid.org/0000-0002-9671-5275)
- Sanyam Sanyam
- Anirban Mondal (ORCID: https://orcid.org/0000-0003-3029-8840)
Institutions
- Indian Institute of Technology Gandhinagar (IN)
Publication Details
- Journal
- Journal of Materials Chemistry A
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5ta03374h
- Citations
- 2
- Primary Topic
- Advancements in Semiconductor Devices and Circuit Design
- Type
- article
- Field-Weighted Citation Impact
- 1.46