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Updated: May 8, 2026

Time-resolved Photophysical Characterization of Triplet-harvesting Organic Compounds at an Oxygen-free Environment Using an iCCD Camera
Published on: December 27, 2018
Δ-machine learning of triplet excitation energies in organic chromophores
Arka Pratim Ghosh1, Koyal Roy1, Kalishankar Bhattacharyya1
1Department of Chemistry, Indian Institute of Technology Guwahati, Guwahati 781039, Assam, India. ksb@iitg.ac.in.
Abstract:
Here, we report a Δ-machine learning approach for predicting triplet excitation energies (T1) of diverse organic chromophores by combining high quality reference data with quantum chemical calculations. A directed message passing neural network corrects TDDFT, ΔSCF and xTB/sTDA prediction to achieve near-chemical accuracy while substantially reducing computational cost. Notably, Δ-ML corrected xTB/sTDA predictions approach TDDFT-level accuracy, enabling rapid high-throughput screening of T1 energies.
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