2026-08-24T04:53:41Z
https://api.figshare.com/v2/oai
oai:figshare.com:article/29457698
2025-07-23T10:29:11Z
category_25258
category_25267
category_25240
category_29173
portal_549
item_type_3
month_year_07_2025
10.5522/04/29457698.v1
https://figshare.com/articles/dataset/Research_data_for_Learning_Radical_Excited_States_from_Sparse_Data_/29457698
https://ndownloader.figshare.com/files/56458529
arxiv:2412.10149v2
Shen, Jingkun
Jingkun
Shen
0009-0006-5932-0756
Walker, Lucy E.
Lucy E.
Walker
0009-0000-5473-4817
Ma, Kevin
Kevin
Ma
Green, James D.
James D.
Green
Bronstein, Hugo
Hugo
Bronstein
0000-0003-0293-8775
Butler, Keith T.
Keith T.
Butler
Hele, Timothy J. H.
Timothy J. H.
Hele
<![CDATA[Research data for "Learning Radical Excited States from Sparse Data"]]>
Computational chemistry
Theoretical quantum chemistry
Photochemistry
Machine learning not elsewhere classified
Organic Radicals
Excited States Simulation
UV-Visible Spectroscopy
OLEDs
Optimisation
Machine Learning
Computational Molecular Screening
2025-07-23
2025-08-27
Dataset
2025
University College London
In this study we have demonstrated a computational model for the simulation of excited electronic states of radicals whose parameters are learned from experimental excited-state data using derivative-free optimisation. This model allows for the rapid-screening of the UV-Visible absorption spectra of organic radicals, including those with potentially useful properties such as in OLEDs and radical qubits. We believe this to be the first rapid-screening method for the excited electronic states of radicals with zero spin-contamination.]]>