What question did this study set out to answer?

The study aims to develop a machine learning approach for predicting triplet excitation energies in organic chromophores.

April 23, 2026Physical Chemistry Chemical Physics

Δ-Machine Learning of Triplet Excitation Energies in Organic Chromophores

View Full Paper

Ask AI

Bookmark

The study aims to develop a machine learning approach for predicting triplet excitation energies in organic chromophores.
Utilized high-quality reference data combined with quantum chemical calculations.
Employed a directed message passing framework for the prediction model.
Achieved accurate predictions of triplet excitation energies (T1).
Demonstrated improved performance over traditional methods in diverse organic chromophores.

Implication

Research demonstrates a machine learning method to predict triplet excitation energies in organic chromophores, indicating improved accuracy in calculations.

Abstract

Here, we report Δ-machine learning approch for predicting triplet excitation energies (T1) of diverse organic chromohores by combining high quality reference data with quantum chemical calculations. A directed message passing...

Discussion

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

Cite This Study

GHOSH et al. (2026) studied this question.

synapsesocial.com/papers/69e9b8d485696592c86ebded https://doi.org/10.1039/d6cp01296e

View Full Paper

Ask AI

Bookmark

Implication

Research demonstrates a machine learning method to predict triplet excitation energies in organic chromophores, indicating improved accuracy in calculations.

Δ-Machine Learning of Triplet Excitation Energies in Organic Chromophores

Key Points

Implication

Abstract

Discussion

Cite This Study

Δ-Machine Learning of Triplet Excitation Energies in Organic Chromophores

Key Points

Implication

Abstract

Discussion

Cite This Study

Keep reading

Keep reading