January 1, 2025Journal of Materials Chemistry A

Physics-Guided Machine Learning of Excited-State Properties for the Design of High-Performance TADF Emitters

View Full Paper

Ask AI

Bookmark

Accurate predictions of singlet-triplet energy gaps enhance TADF emitter design, leading to improved efficiency.
Key metrics include oscillator strengths, showing potential for advancements in photoactive materials.
This study utilizes machine learning to inform the design of advanced photophysical properties in materials.
Significance lies in optimizing TADF emitters for better performance in light-emitting applications.

Implication

Machine learning predicts photophysical properties in TADF emitters, implying efficient design strategies.

Abstract

The rational design of thermally activated delayed fluorescence (TADF) and inverted singlet-triplet (INVEST) emitters demands accurate prediction of critical photophysical properties, particularly singlet-triplet energy gaps (∆E ST ) and oscillator strengths (f)....

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Sanyam et al. (2025) studied this question.

synapsesocial.com/papers/68af63e3ad7bf08b1eae42c9 https://doi.org/10.1039/d5ta03374h

View Full Paper

Ask AI

Bookmark

Implication

Machine learning predicts photophysical properties in TADF emitters, implying efficient design strategies.

Physics-Guided Machine Learning of Excited-State Properties for the Design of High-Performance TADF Emitters

Key Points

Implication

Abstract

Discussion

Cite This Study

Physics-Guided Machine Learning of Excited-State Properties for the Design of High-Performance TADF Emitters

Key Points

Implication

Abstract

Discussion

Cite This Study

Keep reading

Keep reading