Quantifying Design Principles for Light-Emitting Materials with Inverted Singlet-Triplet Energy Gaps.

Area of Science:

• Materials Science
• Organic Chemistry
• Computational Chemistry

Background:

• Inverted singlet-triplet energy gap (INVEST) materials enhance fluorescence efficiency via triplet harvesting.
• Current computational studies focus on qualitative structure-property relationships for INVEST molecules.
• Efficient reverse intersystem crossing is enabled by the S₁ state lying below the T₁ state in INVEST materials.

Purpose of the Study:

• To establish quantitative structure-property relationships for the S₁-T₁ energy gap (ΔE_ST) in heptazine-based INVEST molecules.
• To identify computable molecular descriptors that correlate with ΔE_ST.
• To provide a foundation for data-driven design and machine-learning approaches for INVEST emitters.

Main Methods:

• Development of a benchmark set of 15 heptazine-based INVEST molecules (HEPTA-INVEST15).
• Expansion to a set of 44 mono-, di-, and tri-substituted heptazines (HEPTA-INVEST44).
• Calculation of ΔE_ST and correlation with molecular descriptors like intramolecular charge transfer and %R₁ values.

Main Results:

• Strong linear correlations (R² > 0.94) were found between ΔE_ST and intramolecular charge transfer and single-excitation character (%R₁).
• These correlations were validated across the expanded HEPTA-INVEST44 set, demonstrating generality.
• Electron-donating groups showed complex effects on ΔE_ST, influenced by resonance and excited-state aromaticity, with no clear correlation to Hammett parameters.

Conclusions:

• Physically meaningful, computable descriptors provide a mechanistic basis for designing INVEST emitters.
• The findings support the development of machine-learning models to predict ΔE_ST from molecular structure, bypassing high-level calculations.
• This research facilitates the rational design of advanced organic fluorescent materials.