Shen Jingkun, Walker Lucy E, Ma Kevin, Green James D, Bronstein Hugo, Butler Keith T, Hele Timothy J H
Department of Chemistry, University College London Christopher Ingold Building WC1H 0AJ UK
Yusuf Hamied Department of Chemistry, University of Cambridge Cambridge CB2 1EW UK.
Chem Sci. 2025 Aug 12. doi: 10.1039/d5sc04276c.
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.
发光有机自由基因其在下一代高效有机发光二极管（OLED）器件中的潜在应用以及作为分子量子比特的用途，目前备受关注。然而，模拟它们的光电特性具有挑战性，这主要归因于自旋污染及其激发态的多组态特性。在此，我们提出一种数据驱动的方法，首次直接从实验激发态数据中学习有机自由基的激发电子态，所使用的数据量比机器学习通常所需的数据量少得多。我们采用ExROPPP，一种用于计算自由基激发态的快速且自旋纯净的半经验方法，作为我们学习最优参数集的替代物理模型。为实现这一点，我们编制了已知最大的有机自由基几何结构及其紫外 - 可见数据的数据库，用于训练我们的模型。我们训练的模型给出的激发态能量的均方根误差和平均绝对误差分别为0.24和0.16电子伏特，与具有文献参数的ExROPPP相比有了极大改进。合成了四个新的有机自由基，我们在它们的光谱上测试该模型，发现误差甚至更低，且与训练集具有相似的相关性。这为下一代基于自由基的光电子学的高通量发现铺平了道路。
