Learning radical excited states from sparse data
Jing Shena, Lucy E Walkerb c, Kevin Maa, James D. Greena, Hugo Bronsteinb c, Keith Tobias Butlera, Timothy John Harvey Helea✉
- aDepartment of Chemistry, University College London, Christopher Ingold Building, WC1H 0AJ, UK
- bCavendish Laboratory, University of Cambridge, Cambridge, CB3 0HE, UK
- cYusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
- ✉Corresponding author
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Abstract
Emissive organic radicals are currently of great interest for their potential use in the next generation of highly efficient organic light emitting diode (OLED) devices and as molecular qubits. However, simulating their optoelectronic properties is challenging, largely due to spin-contamination and the multiconfigurational character of their excited states. Here we present a data-driven approach where, for the first time, the excited electronic states of organic radicals are learned directly from experimental excited state data, using a much smaller amount of data than typically required by Machine Learning. We adopt ExROPPP, a fast and spin-pure semiempirical method for the calculation of the excited states of radicals, as a surrogate physical model for which we learn the optimal set of parameters. To achieve this we compile the largest known database of organic radical geometries and their UV-vis data, which we use to train our model. Our trained model gives root mean square and mean absolute errors for excited state energies of 0.24 and 0.16 eV respectively, improving hugely over ExROPPP with literature parameters. Four new organic radicals are synthesised and we test the model on their spectra, finding even lower errors and similar correlation as for the training set. This paves the way for the high throughput discovery of next generation radical-based optoelectronics.
TL;DRThis work presents a data-driven approach where, for the first time, the excited electronic states of organic radicals are learned directly from experimental excited state data, using a much smaller amount of data than typically required by Machine Learning. (Semantic Scholar)